Data analysis - Knowledge

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problems can also be identified through a variety of analytical techniques. For example; with financial information, the totals for particular variables may be compared against separately published numbers that are believed to be reliable. Unusual amounts, above or below predetermined thresholds, may also be reviewed. There are several types of data cleaning, that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. Quantitative data methods for outlier detection, can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. Textual data spell checkers can be used to lessen the amount of mistyped words. However, it is harder to tell if the words themselves are correct.

1299:(NCA) may be used when the analyst is trying to determine the extent to which independent variable X allows variable Y (e.g., "To what extent is a certain unemployment rate (X) necessary for a certain inflation rate (Y)?"). Whereas (multiple) regression analysis uses additive logic where each X-variable can produce the outcome and the X's can compensate for each other (they are sufficient but not necessary), necessary condition analysis (NCA) uses necessity logic, where one or more X-variables allow the outcome to exist, but may not produce it (they are necessary but not sufficient). Each single necessary condition must be present and compensation is not possible. 868: 1096: 1242: 1051: 1104: 2332:

documentation. Often, the correct order of running scripts is only described informally or resides in the data scientist's memory. The potential for losing this information creates issues for reproducibility. To address these challenges, it is essential to have analysis scripts written for automated, reproducible workflows. Additionally, dynamic documentation is crucial, providing reports that are understandable by both machines and humans, ensuring accurate representation of the analysis workflow even as scripts evolve.

753: 399: 1765:, they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock. Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures. 1308: 2186:. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same 1758:

statements. This numerical technique is referred to as normalization or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs. nominal data) or considering population increases, demographics, etc. Analysts apply a variety of techniques to address the various quantitative messages described in the section above.

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The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:

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Wyckhuys, Kris A. G.; Wongtiem, Prapit; Rauf, Aunu; Thancharoen, Anchana; Heimpel, George E.; Le, Nhung T. T.; Fanani, Muhammad Zainal; Gurr, Geoff M.; Lundgren, Jonathan G.; Burra, Dharani D.; Palao, Leo K.; Hyman, Glenn; Graziosi, Ignazio; Le, Vi X.; Cock, Matthew J. W.; Tscharntke, Teja; Wratten,

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In the main analysis phase, either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In

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will arise from problems in the way that the datum are entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data

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may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)?"). This is an attempt to model or fit an equation line or curve to the data, such

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As another example, the auditor of a public company must arrive at a formal opinion on whether financial statements of publicly traded corporations are "fairly stated, in all material respects". This requires extensive analysis of factual data and evidence to support their opinion. When making the

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Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. Customers specifying requirements and analysts performing the data analysis may consider these messages

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For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy (GDP) or the amount of cost relative to revenue in corporate financial

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is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false. For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept

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A data analytics approach can be used in order to predict energy consumption in buildings. The different steps of the data analysis process are carried out in order to realise smart buildings, where the building management and control operations including heating, ventilation, air conditioning,

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Users may have particular data points of interest within a data set, as opposed to the general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data

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of 2001 and 2003 for the 2011–2020 time period would add approximately $ 3.3 trillion to the national debt. Everyone should be able to agree that indeed this is what CBO reported; they can all examine the report. This makes it a fact. Whether persons agree or disagree with the CBO is their own

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add up to the layer above them. The relationship is referred to as "Mutually Exclusive and Collectively Exhaustive" or MECE. For example, profit by definition can be broken down into total revenue and total cost. In turn, total revenue can be analyzed by its components, such as the revenue of

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with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's

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on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews,

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The typical data analysis workflow involves collecting data, running analyses through various scripts, creating visualizations, and writing reports. However, this workflow presents challenges, including a separation between analysis scripts and data, as well as a gap between analysis and

794:"Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." 5432:

Moreno Delgado, David; Møller, Thor C.; Ster, Jeanne; Giraldo, Jesús; Maurel, Damien; Rovira, Xavier; Scholler, Pauline; Zwier, Jurriaan M.; Perroy, Julie; Durroux, Thierry; Trinquet, Eric; Prezeau, Laurent; Rondard, Philippe; Pin, Jean-Philippe (29 June 2017). Chao, Moses V (ed.).

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The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), normal

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The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an

2208:. By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with 1977:

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis

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Tabachnick, B.G. & Fidell, L.S. (2007). Chapter 4: Cleaning up your act. Screening data prior to analysis. In B.G. Tabachnick & L.S. Fidell (Eds.), Using Multivariate Statistics, Fifth Edition (pp. 60–116). Boston: Pearson Education, Inc. / Allyn and

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Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show the comparison of the actual versus the reference

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It is important to obtain some indication about how generalizable the results are. While this is often difficult to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing that.

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Different companies or organizations hold data analysis contests to encourage researchers to utilize their data or to solve a particular question using data analysis. A few examples of well-known international data analysis contests are as follows:

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wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. He emphasized procedures to help surface and debate alternative points of view.

1042:, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy. 1881:

should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.

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Once data is analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.

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Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A

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When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience. Data visualization uses

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After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis

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Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.

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Frequency distribution: Shows the number of observations of a particular variable for a given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A

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If the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in

917:, to begin understanding the messages contained within the obtained data. The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the 823:(e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). 6601: 1080:

are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained in the data.

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Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.

3977:"Table 3: Best regression models between LIDAR data (independent variable) and field-based Forestereo data (dependent variable), used to map spatial distribution of the main forest structure variables" 2004:

Also, the original plan for the main data analyses can and should be specified in more detail or rewritten. In order to do this, several decisions about the main data analyses can and should be made:

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Analytics is the "extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions." It is a subset of

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In the main analysis phase, analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.

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is the tendency to search for or interpret information in a way that confirms one's preconceptions. In addition, individuals may discredit information that does not support their views.

6728:"Descriptive statistics indicating the mean, standard deviation and frequency of missing values for each condition (N = number of participants), and for the dependent variables (DV)" 2033:

In case items do not fit the scale: should one adapt the measurement instrument by omitting items, or rather ensure comparability with other (uses of the) measurement instrument(s)?

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Chao, Luke H.; Jang, Jaebong; Johnson, Adam; Nguyen, Anthony; Gray, Nathanael S.; Yang, Priscilla L.; Harrison, Stephen C. (12 July 2018). Jahn, Reinhard; Schekman, Randy (eds.).

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format (embedding labels, supplemental documentation, and a help system and making key package/display and content decisions) to improve the accuracy of educators’ data analyses.

8122: 5435:"Appendix 1—figure 5. Curve data included in Appendix 1—table 4 (solid points) and the theoretical curve by using the Hill equation parameters of Appendix 1—table 5 (curve line)" 1172:

Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A

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Hobold, Edilson; Pires-Lopes, Vitor; Gómez-Campos, Rossana; Arruda, Miguel de; Andruske, Cynthia Lee; Pacheco-Carrillo, Jaime; Cossio-Bolaños, Marco Antonio (30 November 2017).

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is also a technique used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights, regarding the messages within the data.

5183:"Simple Statistical Models for Discrete Panel Data Developed and Applied to Test the Hypothesis of True State Dependence against the Hypothesis of Spurious State Dependence" 4206: 2178:

should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a

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Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

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that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.

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The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.

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is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while

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Data, when initially obtained, must be processed or organized for analysis. For instance, these may involve placing data into rows and columns in a table format (

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covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into

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It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:

1754:; they are said to be innumerate. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques. 8175: 8145: 3944:"Table 3: Descriptive (mean ± SD), inferential (95% CI) and qualitative statistics (ES) of all variables between self-selected and predetermined conditions" 1697:. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. For example, in August 2010, the 2036:

In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use small sample techniques, like exact tests or

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During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.

6214:"The Effect of Regional Government Size, Legislative Size, Number of Population, and Intergovernmental Revenue on The Financial Statements Disclosure" 3975:

Cortés-Molino, Álvaro; Aulló-Maestro, Isabel; Fernandez-Luque, Ismael; Flores-Moya, Antonio; Carreira, José A.; Salvo, A. Enrique (22 October 2020).

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Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year;

4691:"Figure 4. Frequency of hemifusion (measured as DiD fluorescence dequenching) as a function of number of bound Alexa-fluor-555/3-110-22 molecules" 6543: 5728: 2414: 803: 664: 3481: 2881: 2554: 958:. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some 832: 3424:"How reliable are our published archaeometric analyses? Effects of analytical techniques through time on the elemental analysis of obsidians" 2988: 1750:

Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or

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This section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a Knowledge article.

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Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.

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lighting and security are realised automatically by miming the needs of the building users and optimising resources like energy and time.

3400: 1797:, which is a set of technologies and processes that uses data to understand and analyze business performance to drive decision-making . 1553:

Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.

8114: 7531:"Correction of the significance level when attempting multiple transformations of an explanatory variable in generalized linear models" 2224:. A procedure to study the behavior of a system or model when global parameters are (systematically) varied. One way to do that is via 7393:"Engaging in Exploratory Data Analysis, Visualization, and Hypothesis Testing – Exploratory Data Analysis, Geovisualization, and Data" 6103: 4483:

Steve D.; Nguyen, Liem V.; You, Minsheng; Lu, Yanhui; Ketelaar, Johannes W.; Goergen, Georg; Neuenschwander, Peter (19 October 2018).

1022:. Analysts may also attempt to build models that are descriptive of the data, in an aim to simplify analysis and communicate results. 5570: 3662: 7138:

Verkeer in een landelijk gebied: waarnemingen en analyse van het verkeer in zuidwest Friesland en ontwikkeling van een verkeersmodel

4900:"Table 1: Descriptive statistics (mean ± standard-deviation) for somatic variables and physical fitness ítems for males and females" 4298:

Bemowska-Kałabun, Olga; Wąsowicz, Paweł; Napora-Rutkowski, Łukasz; Nowak-Życzyńska, Zuzanna; Wierzbicka, Małgorzata (11 June 2019).

7837:"What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models" 5589: 5551: 3909:

Evans, Michelle V.; Dallas, Tad A.; Han, Barbara A.; Murdock, Courtney C.; Drake, John M. (28 February 2017). Brady, Oliver (ed.).

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procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.

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divisions A, B, and C (which are mutually exclusive of each other) and should add to the total revenue (collectively exhaustive).

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used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data analysis.

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applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of

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How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help.

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Billings S.A. "Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains". Wiley, 2013

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business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

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Characterization of epigenetic changes and their connection to gene expression abnormalities in clear cell renal cell carcinoma

3759:"Exploring your Data with Data Visualization & Descriptive Statistics: Common Descriptive Statistics for Quantitative Data" 8413: 8303: 8093: 8019: 7944: 7814: 7772: 7671: 7461: 7412: 7371: 7262: 7224: 6808: 6641: 6566: 6522: 6357: 6283: 6044: 6004: 5937: 5796: 5751: 5707: 5612:. Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS50 2017). University of Hawaiʻi at Mānoa. 5278: 5233: 5026: 4984: 4575: 4537: 4461: 4161: 4026: 3885: 3855: 3810: 3372: 3254: 3216: 3105: 3075: 3039: 2916: 2857: 2720: 2644: 2603: 2519: 1637: 1123:

Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the

2701:"Data Coding and Exploratory Analysis (EDA) Rules for Data Coding Exploratory Data Analysis (EDA) Statistical Assumptions" 8080:, Wiley Series in Probability and Statistics, Hoboken, NJ, USA: John Wiley & Sons, Inc., 2003-06-30, pp. 19–63, 7982: 6609:

Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.

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Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A

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A scatterplot illustrating the correlation between two variables (inflation and unemployment) measured at points in time.

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Davis, Steve; Pettengill, James B.; Luo, Yan; Payne, Justin; Shpuntoff, Al; Rand, Hugh; Strain, Errol (26 August 2015).

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Make categorical (ordinal / dichotomous) (if the distribution differs severely from normal, and no transformations help)

8331: 1090: 1062: 741: 41: 8399: 8382: 8359: 8345: 8317: 8259: 8221: 8049: 6897: 6462: 6079: 5865: 5633: 5343: 4362: 4049:"Performances of estimators of linear model with auto-correlated error terms when the independent variable is normal" 3574: 3138: 2951: 1605:

Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.

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Once the datasets are cleaned, they can then be analyzed. Analysts may apply a variety of techniques, referred to as

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Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A

718:(CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing 5610:

ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications

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or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.

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A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time.

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of a measurement instrument. During this analysis, one inspects the variances of the items and the scales, the

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Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for

2484: 2204: 515: 5372:"Exchange rate changes and inflation in India: What is the extent of exchange rate pass-through to imports?" 2182:. It is important to always adjust the significance level when testing multiple models with, for example, a 2509: 2319: 2287: 2213: 1762: 1761:

Analysts may also analyze data under different assumptions or scenario. For example, when analysts perform

1296: 478: 428: 17: 3689:"CFSAN SNP Pipeline: An automated method for constructing SNP matrices from next-generation sequence data" 1208:

Check relationships between numbers that should be related in a predictable way, such as ratios over time;

4818: 1698: 950:), may be applied to the data in order to identify relationships among the variables; for example, using 360: 5336:

Rejecting the second generation hypothesis : maintaining Estonian ethnicity in Lakewood, New Jersey

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includes utilizing techniques that measure the relationships between particular variables. For example,

8732: 6995:"2020/31 Comparing job descriptions is insufficient for checking whether work is equally valuable (BG)" 5499:"Necessary Condition Analysis (NCA): Logic and Methodology of 'Necessary But Not Sufficient' Causality" 2549: 2225: 2037: 1733:

Analysts may be trained specifically to be aware of these biases and how to overcome them. In his book

951: 450: 161: 4485:"Figure 2: Bi-monthly mealybug population fluctuations in southern Vietnam, over a 2-year time period" 3475:"Perceptual Edge-Jonathan Koomey-Best practices for understanding quantitative data-February 14, 2006" 3162:

Olusola, Johnson Adedeji; Shote, Adebola Adekunle; Ouigmane, Abdellah; Isaifan, Rima J. (7 May 2021).

8247: 8209: 6139:"Figure 6.7. Differences in literacy scores across OECD countries generally mirror those in numeracy" 3474: 2464: 2449: 2175: 2048: 1933: 1711: 1283: 1262: 914: 867: 726:

focuses on the application of statistical models for predictive forecasting or classification, while

711: 505: 55: 6843:"Computing the displacement of the initial contour of gears when they are checked by means of balls" 6700: 6455: 6334: 6173: 5301:"Alpha and Beta Tests for Type I and Type II Inferential Errors Determination in Hypothesis Testing" 5129: 4884: 4675: 4626: 4426: 4330: 3942:

Watson, Kevin; Halperin, Israel; Aguilera-Castells, Joan; Iacono, Antonio Dello (12 November 2020).

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that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to

8747: 8727: 8524: 8520: 5007:"H6 Antiphanes fr.172.1-4, from Women Who Looked Like Each Other or Men Who Looked Like Each Other" 4724:"Table 2: Graph comparison between Scatter plot, Violin + Scatter plot, Heatmap and ViSiElse graph" 2454: 2297: 1897: 1235: 485: 5929: 8449: 7975:

Beginning Data Science in R 4: Data Analysis, Visualization, and Modelling for the Data Scientist

2968: 2494: 1855:: outlying observations in the data are analyzed to see if they seem to disturb the distribution. 1844: 1678: 1377:

Given some concrete conditions on attribute values, find data cases satisfying those conditions.

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Skapandet av förtroende inom eWOM : En studie av profilbildens effekt ur ett könsperspektiv

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of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale

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Given a set of data cases and an attribute of interest, find the span of values within the set.

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Find data cases possessing an extreme value of an attribute over its range within the data set.

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has recommended a series of best practices for understanding quantitative data. These include:

8722: 8551: 5917: 4933:"Table 2: Cluster analysis presenting mean values of psychological variables per cluster group" 2489: 2399: 1958: 1862: 1257:

named a technique for breaking a quantitative problem down into its component parts called the

1223: 971: 922: 707: 70: 5818:"Congressional Budget Office-The Budget and Economic Outlook-August 2010-Table 1.7 on Page 24" 1813:

for the purpose of analyzing student data. These data systems present data to educators in an

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Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?

1076:(graphics such as, tables and charts) to help communicate key messages contained in the data. 8291: 8243: 8205: 6687: 6442: 6321: 6160: 5116: 4871: 4662: 4613: 4413: 4317: 4308: 4299: 3774: 3623: 3396: 3296: 2524: 2459: 2409: 2404: 2394: 2260:– The Konstanz Information Miner, a user friendly and comprehensive data analytics framework. 2183: 1951: 1814: 1794: 1202:

Re-perform important calculations, such as verifying columns of data that are formula driven;

703: 495: 390: 267: 156: 75: 4592:"Chart C5.3. Percentage of 15-19 year-olds not in education, by labour market status (2012)" 1408:

Given a set of data cases, compute an aggregate numeric representation of those data cases.

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are available for study & research. The requirements may be communicated by analysts to

8681: 7848: 3508:"Providing cell phone numbers and email addresses to Patients: the physician's perspective" 3435: 2514: 2504: 2220: 2212:. Hence other methods of validation sometimes need to be used. For more on this topic, see 2023:: should one neglect or impute the missing data; which imputation technique should be used? 1893: 1878: 1473:

What is the sorted order of a set S of data cases according to their value of attribute A?

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Xia, B. S., & Gong, P. (2015). Review of business intelligence through data analysis.

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Break problems into component parts by analyzing factors that led to the results, such as

802:, in that feedback from later phases may result in additional work in earlier phases. The 8: 8621: 8606: 8534: 7010: 5788: 5586: 4641:"Chart 7: Households: final consumption expenditure versus actual individual consumption" 2384: 2300:– A programming language and software environment for statistical computing and graphics. 2009: 1961:(whether this is random or not should be assessed during the initial data analysis phase) 1901: 1289: 1077: 975: 685: 636: 525: 433: 338: 287: 7999: 7852: 6994: 6922: 6890:

Qualitative-quantitative research methodology : exploring the interactive continuum

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Confirmation bias in witness interviewing: Can interviewers ignore their preconceptions?

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The process of analyzing data to discover useful information and support decision-making

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variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?

1965: 1274: 1273:

Analysts may use robust statistical measurements to solve certain analytical problems.

1231: 1054: 926: 872: 779:, and subsequently converting it into information useful for decision-making by users. 583: 538: 60: 8204:(2008a). "Chapter 14: Phases and initial steps in data analysis". In Adèr, Herman J.; 6471:"Towards energy efficiency smart buildings models based on intelligent data analytics" 5857: 5608:

Yavari, Ali; Jayaraman, Prem Prakash; Georgakopoulos, Dimitrios; Nepal, Surya (2017).

4087: 3736: 2322:– A programming language well-suited for numerical analysis and computational science. 2093:

loglinear analysis (to identify relevant/important variables and possible confounders)

1646:

Which data cases in a set S of data cases are relevant to the current users' context?

886:) for further analysis, often through the use of spreadsheet or statistical software. 783:

is collected and analyzed to answer questions, test hypotheses, or disprove theories.

8742: 8611: 8601: 8465: 8409: 8395: 8378: 8355: 8341: 8327: 8313: 8299: 8265: 8255: 8227: 8217: 8089: 8055: 8045: 8015: 7978: 7940: 7909: 7878: 7866: 7810: 7768: 7725: 7667: 7570: 7552: 7511: 7503: 7457: 7422: 7408: 7367: 7268: 7258: 7234: 7220: 7141: 7110: 7102: 7026: 7014: 6942: 6903: 6893: 6874: 6862: 6804: 6679: 6664:"Analysis of dimensional distortion data from initial 24 quality certification tubes" 6637: 6562: 6537: 6518: 6402: 6363: 6353: 6279: 6245: 6233: 6089: 6075: 6040: 6000: 5933: 5871: 5861: 5792: 5757: 5747: 5722: 5703: 5669: 5665: 5629: 5534: 5522: 5414: 5391: 5349: 5339: 5316: 5274: 5239: 5229: 5198: 5163: 5068: 5022: 5018: 4980: 4931:

Ablin, Jacob N.; Zohar, Ada H.; Zaraya-Blum, Reut; Buskila, Dan (13 September 2016).

4571: 4533: 4457: 4405: 4372: 4358: 4280: 4157: 4127: 4115: 4103: 4068: 4022: 3891: 3881: 3851: 3816: 3806: 3740: 3580: 3570: 3547: 3529: 3451: 3378: 3368: 3345: 3260: 3250: 3226: 3212: 3144: 3134: 3111: 3101: 3071: 3035: 3031: 2947: 2912: 2863: 2853: 2820: 2781: 2758: 2716: 2640: 2599: 2539: 2374: 2281: 2263: 1727: 1650:- Are there groups of restaurants that have foods based on my current caloric intake? 1608:

What is the correlation between attributes X and Y over a given set S of data cases?

925:, such as, the average or median, can be generated to aid in understanding the data. 731: 626: 601: 573: 343: 333: 8239: 8201: 7954: 7892:

United Nations Development Programme (2018). "Human development composite indices".

7122: 5006: 4148:, Indianapolis, Indiana: John Wiley & Sons, Inc., pp. 367–420, 2016-01-29, 3974: 2832: 798:

There are several phases that can be distinguished, described below. The phases are

767:

refers to dividing a whole into its separate components for individual examination.

8561: 8510: 8495: 8475: 8460: 8081: 8007: 7932: 7897: 7856: 7802: 7760: 7735: 7715: 7705: 7659: 7602: 7560: 7542: 7495: 7449: 7400: 7359: 7351: 7212: 7172: 7094: 7046: 7006: 6934: 6854: 6796: 6765: 6739: 6667: 6629: 6510: 6482: 6424: 6394: 6309: 6271: 6225: 6142: 6067: 6032: 5992: 5965: 5925: 5898: 5853: 5784: 5695: 5661: 5621: 5613: 5568:"A Preliminary Analysis of the Products of HCI Research, Using Pro Forma Abstracts" 5514: 5506: 5473: 5446: 5383: 5308: 5266: 5190: 5155: 5098: 5060: 5014: 4972: 4944: 4911: 4853: 4763: 4735: 4702: 4644: 4595: 4563: 4525: 4496: 4449: 4391: 4350: 4303: 4272: 4236: 4198: 4149: 4107: 4099: 4060: 4014: 3988: 3955: 3922: 3843: 3762: 3732: 3720: 3700: 3605: 3537: 3519: 3443: 3335: 3282: 3204: 3175: 3063: 2980: 2904: 2812: 2750: 2708: 2673: 2632: 2591: 2474: 2469: 2419: 2275: 2132: 1354:

What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?

1286:, which relate to whether the data supports accepting or rejecting the hypothesis. 1103: 820: 737: 611: 578: 460: 348: 171: 146: 8042:

The consumer in Austrian economics and the Austrian perspective on consumer policy

7752: 7483: 6350:

The History of the Church Missionary Society Its Environment, its Men and its Work

6264:"Effectiveness of Brainwriting Techniques: Comparing Nominal Groups to Real Teams" 6263: 5649: 5258: 5049:"On Mutually Exclusive and Collectively Exhaustive Properties of Demand Functions" 4517: 4441: 4232:

Data requirements for semiconductor die. Exchange data formats and data dictionary

4008: 3993: 3976: 3960: 3943: 3180: 3163: 2896: 2254:– Data mining framework in Java with data mining oriented visualization functions. 1934:

Did the implementation of the study fulfill the intentions of the research design?

1673:

You are entitled to your own opinion, but you are not entitled to your own facts.

8691: 8626: 8616: 8586: 8529: 8500: 8490: 8387: 7924: 7764: 7499: 6923:"Some Relationships Among Internal Consistency, Reproducibility, and Homogeneity" 6605: 6275: 6213: 5984: 5593: 5574: 5555: 5270: 4949: 4932: 4916: 4899: 4849: 4740: 4723: 4529: 4453: 4018: 3835: 3705: 3688: 3023: 2908: 2429: 1710:

leap from facts to opinions, there is always the possibility that the opinion is

1215: 1192: 1186: 465: 252: 121: 85: 8308:

Chambers, John M.; Cleveland, William S.; Kleiner, Beat; Tukey, Paul A. (1983).

7936: 7484:"MIB-1 Cell Membrane Reactivity: A Finding That Should be Interpreted Carefully" 6744: 6727: 6582: 6487: 6470: 6229: 5996: 4857: 4088:"A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis" 3847: 3055: 752: 8701: 8696: 8661: 8641: 8636: 8591: 8566: 8485: 7441: 7350:, Hoboken, NJ, USA: John Wiley & Sons, Inc., pp. 119–138, 2017-10-13, 7343: 7177: 7160: 6621: 5371: 5143: 4567: 4501: 4484: 4141: 4048: 3423: 3067: 2624: 1920:

Square root transformation (if the distribution differs moderately from normal)

1738: 1723: 1643:

Given a set of data cases, find contextual relevancy of the data to the users.

1560:- Are there exceptions to the relationship between horsepower and acceleration? 1279: 1258: 1246: 1238:

of the key variables to see how the individual values cluster around the mean.

895: 836: 772: 727: 681: 616: 606: 550: 510: 277: 166: 116: 8059: 7453: 7355: 7145: 6788: 6633: 6502: 6367: 5687: 5418: 5387: 5159: 4964: 4396: 4387: 4276: 4153: 4064: 3447: 3196: 3115: 2984: 2785: 2636: 1556:

Which data cases in a set S of data cases have unexpected/exceptional values?

1411:

What is the value of aggregation function F over a given set S of data cases?

398: 8716: 8666: 8656: 8631: 8505: 8426: 8269: 8231: 7870: 7861: 7836: 7794: 7556: 7507: 7272: 7106: 7018: 6946: 6866: 6800: 6514: 6406: 6237: 5959: 5875: 5761: 5699: 5673: 5526: 5395: 5353: 5320: 5243: 5202: 5167: 5072: 4976: 4788:"Stephen Few-Perceptual Edge-Selecting the Right Graph for Your Message-2004" 4284: 4260: 4119: 4072: 3895: 3820: 3803:

Tableau your data! : fast and easy visual analysis with Tableau Software

3744: 3533: 3455: 3382: 3349: 3264: 3208: 2867: 2824: 2762: 2700: 2677: 2424: 2044: 1939: 1702: 689: 257: 136: 8287: 8073: 7806: 7739: 7594: 7547: 7204: 7159:

Foth, Christian; Hedrick, Brandon P.; Ezcurra, Martin D. (18 January 2016).

6907: 5969: 5617: 5451: 5434: 4755: 4707: 4690: 3927: 3910: 3584: 3340: 3327: 3287: 3278: 3148: 2712: 1530:

What is the distribution of values of attribute A in a set S of data cases?

8676: 8671: 8651: 8646: 8576: 8571: 8546: 8539: 8515: 8365: 8085: 7729: 7606: 7574: 7515: 7216: 7114: 6382: 6259: 6188:"Bloomberg-Barry Ritholz-Bad Math that Passes for Insight-October 28, 2014" 6061: 4767: 4722:

Garnier, Elodie M.; Fouret, Nastasia; Descoins, Médéric (3 February 2020).

4344: 4202: 3758: 3600: 3551: 3524: 2444: 2187: 2179: 2148: 2140: 2020: 1163: 247: 227: 217: 181: 176: 151: 141: 131: 100: 7651: 6420: 6398: 6138: 6071: 5650:"Connectivity tool transfers data among database and statistical products" 5498: 5477: 5468: 5300: 5094: 4640: 4591: 4354: 3766: 3609: 2847: 2742: 1923:

Log-transformation (if the distribution differs substantially from normal)

8596: 8556: 7901: 7663: 7082: 6428: 6146: 5510: 5312: 5102: 4648: 4599: 4442:"Performing Requirements Analysis Project Courses for External Customers" 3062:, Hoboken, NJ: John Wiley & Sons, Inc, pp. 296–303, 2015-02-06, 2754: 2534: 2529: 2434: 2271: 1926:

Inverse transformation (if the distribution differs severely from normal)

1810: 855: 807: 699: 631: 313: 292: 242: 95: 7710: 7363: 7098: 6842: 5549:"Low-Level Components of Analytic Activity in Information Visualization" 5210: 4346:

Visualizing Data About UK Museums: Bar Charts, Line Charts and Heat Maps

3279:"Figure 3—source data 1. Raw and processed values obtained through qPCR" 2090:

hierarchical loglinear analysis (restricted to a maximum of 8 variables)

740:

is a precursor to data analysis, and data analysis is closely linked to

6858: 6759: 5817: 5625: 5080: 3911:"Figure 2. Variable importance by permutation, averaged over 25 models" 2800: 2583: 2209: 2131:

Nonlinear analysis is often necessary when the data is recorded from a

1694: 1470:

Given a set of data cases, rank them according to some ordinal metric.

1117: 946: 883: 787: 719: 237: 202: 186: 126: 34: 7835:

Sheikholeslami, Razi; Razavi, Saman; Haghnegahdar, Amin (2019-10-10).

7652:"3 The Facelift: A Guide for Safe, Reliable, and Reproducible Results" 7392: 7040: 6769: 6663: 6305: 5518: 5182: 4230: 4111: 2816: 2595: 1980:

The characteristics of the data sample can be assessed by looking at:

1579:

Given a set of data cases, find clusters of similar attribute values.

8686: 8581: 8004:

Cybermetric Techniques to Evaluate Organizations Using Web-Based Data

7404: 7050: 6671: 6313: 6026: 5194: 5048: 4240: 3941: 2379: 2144: 2060:

Several analyses can be used during the initial data analysis phase:

1806: 1788: 1499:

What is the range of values of attribute A in a set S of data cases?

1173: 1155: 1143: 1136: 955: 852: 799: 328: 282: 272: 262: 232: 212: 207: 8242:(2008b). "Chapter 15: The main analysis phase". In Adèr, Herman J.; 7891: 6036: 5064: 3164:"Table 1: Data type and sources of data collected for this research" 2172:

a confirmatory analysis clear hypotheses about the data are tested.

8480: 6795:, Newbury Park, CA: SAGE Publications, Inc., pp. 64–75, 1993, 5890: 4897: 2849:

Business intelligence guidebook: from data integration to analytics

2389: 1751: 776: 323: 318: 222: 8254:. Huizen, Netherlands: Johannes van Kessel Pub. pp. 357–386. 8216:. Huizen, Netherlands: Johannes van Kessel Pub. pp. 333–356. 5902: 5777:"Consisting partly of facts, and partly of observations upon them" 5607: 3363:

Jeannie Scruggs, Garber; Gross, Monty; Slonim, Anthony D. (2010).

2805:

Statistical Analysis and Data Mining: The ASA Data Science Journal

2135:. Nonlinear systems can exhibit complex dynamic effects including 1954:(this should be identified during the initial data analysis phase) 1442:

What are the top/bottom N data cases with respect to attribute A?

1307: 7595:"Some ethical issues in confirmatory versus exploratory analysis" 7161:"Figure 4: Centroid size regression analyses for the main sample" 2544: 2241: 2027: 1852: 1690: 7834: 6028:

Barriers and Biases in Computer-Mediated Knowledge Communication

6025:

Bromme, Rainer; Hesse, Friedrich W.; Spada, Hans, eds. (2005).

5431: 4481: 2801:"The forecast for predictive analytics: hot and getting hotter" 2346: 1351:

Given a set of specific cases, find attributes of those cases.

1227: 1222:

For the variables under examination, analysts typically obtain

4518:"A sampling of 2-categorical aspects of quasi-category theory" 3328:"Many surveys, about one in five, may contain fraudulent data" 2743:"New European ICT call focuses on PICs, lasers, data transfer" 2047:

procedure seems to be defective: can and should one calculate

1057:

is used to help understand the results after data is analyzed.

5891:"Do Publicly Traded Corporations Act in the Public Interest?" 3506:

Peleg, Roni; Avdalimov, Angelika; Freud, Tamar (2011-03-23).

2313: 2257: 1199:

Check raw data for anomalies prior to performing an analysis;

8298:. Huizen, the Netherlands: Johannes van Kessel Publishing. 8252:

Advising on research methods : a consultant's companion

8214:

Advising on research methods : a consultant's companion

6550: 4930: 4300:"Supplemental Information 1: Raw data for charts and tables" 4261:"How to Communicate Your Message to an Audience Effectively" 1947:

Other possible data distortions that should be checked are:

1626:- Is there a trend of increasing film length over the years? 1586:- Are there groups of cereals w/ similar fat/calories/sugar? 1455:- What Marvel Studios film has the most recent release date? 1282:. Hypothesis testing involves considering the likelihood of 8000:"A cybermetric analysis model to measure private companies" 7759:, Cambridge: Cambridge University Press, pp. 327–368, 6628:, Nomos Verlagsgesellschaft mbH & Co. KG, p. 525, 4524:, Cambridge: Cambridge University Press, pp. 318–336, 4053:

Journal of the Nigerian Association of Mathematical Physics

3367:. Wolters Kluwer Health/Lippincott Williams & Wilkins. 3161: 2937: 2935: 2933: 2360: 2356: 2307: 2303: 2291: 2251: 1997: 1686: 1616:- Is there a correlation between country of origin and MPG? 1139:

may be used to show the comparison across the salespersons.

692: 308: 6583:

Report finds states on course to build pupil-data systems.

3362: 3017: 3015: 3013: 3011: 3009: 3007: 3005: 2903:, Cambridge: Cambridge University Press, pp. 96–109, 2166: 2098:

Exact tests or bootstrapping (in case subgroups are small)

7997: 7631: 7619: 7448:, London: SAGE Publications Ltd, pp. 138–151, 2010, 7303: 7291: 7279: 7062: 6953: 6822: 6468: 6212:

Gusnaini, Nuriska; Andesto, Rony; Ermawati (2020-12-15).

859:

downloads from online sources, or reading documentation.

5472:(Video). The Institute of Engineering & Technology. 3098:

Introduction to accounting : an integrated approach

2930: 2584:"Transforming Unstructured Data into Useful Information" 8418: 7488:

Applied Immunohistochemistry & Molecular Morphology

6211: 6060:

Heuer, Richards (2019-06-10). Heuer, Richards J (ed.).

5846:"Students' sense of belonging, by immigrant background" 4560:

Encyclopedia of Production and Manufacturing Management

3686: 3131:

Wages and labor markets in the United States, 1820-1860

3002: 2590:, Auerbach Publications, pp. 227–246, 2014-03-12, 2578: 2576: 2335: 1621:- Do different genders have a preferred payment method? 1612:- Is there a correlation between carbohydrates and fat? 1534:- What is the distribution of carbohydrates in cereals? 982:), provides an explanation for the variation in sales ( 8296:

Advising on Research Methods: A Consultant's Companion

7211:, Boydell & Brewer, pp. 853–922, 2018-06-18, 6726:

Kjell, Oscar N. E.; Thompson, Sam (19 December 2013).

6469:

González-Vidal, Aurora; Moreno-Cano, Victoria (2016).

2631:, Cambridge University Press, pp. 526–576, 2017, 1782: 1415:- What is the average calorie content of Post cereals? 1302: 978:

may be used to model whether a change in advertising (

8408:. Boston: Pearson Education, Inc. / Allyn and Bacon, 7998:

Orduna-Malea, Enrique; Alonso-Arroyo, Adolfo (2018),

7324: 7185: 7083:"Acupuncture Procedures Must be Accurately Described" 6965: 6707: 5409:

Mudiyanselage, Nawarathna; Nawarathna, Pubudu Manoj.

4721: 4688: 4448:, New York, NY: Springer New York, pp. 276–285, 3908: 1158:, a type of bar chart, may be used for this analysis. 7482:

Sordo, Rachele Del; Sidoni, Angelo (December 2008).

6218:

European Journal of Business and Management Research

5469:"How Can Engineers and Journalists Help Each Other?" 5265:, London: Macmillan Education UK, pp. 186–218, 4819:"Stephen Few-Perceptual Edge-Graph Selection Matrix" 2573: 2051:

and include them as covariates in the main analyses?

1689:

to answer questions, support a conclusion or formal

1358:- What is the mileage per gallon of the Ford Mondeo? 8394:. Oxford : Blackwell Scientific Publications. 8115:"The machine learning community takes on the Higgs" 7692:Benson, Noah C; Winawer, Jonathan (December 2018). 6921:Terwilliger, James S.; Lele, Kaustubh (June 1979). 5991:, Dordrecht: Springer Netherlands, pp. 37–57, 5895:

National Bureau of Economic Research Working Papers

5587:"What Makes Good Research in Software Engineering?" 5408: 5365: 5363: 3505: 734:. All of the above are varieties of data analysis. 7929:Advanced R Statistical Programming and Data Models 7158: 6503:"Low-Energy Air Conditioning and Lighting Control" 6306:"Purported Responsible Address in E-Mail Messages" 5985:"Does the Sociology of Science Discredit Science?" 5467: 3599: 3422:Hancock, R.G.V.; Carter, Tristan (February 2010). 2969:"John Tukey-The Future of Data Analysis-July 1961" 2775: 2232: 1885:There are two ways to assess measurement quality: 1419:- What is the gross income of all stores combined? 1380:Which data cases satisfy conditions {A, B, C...}? 8137: 6063:Quantitative Approaches to Political Intelligence 5547:Robert Amar, James Eagan, and John Stasko (2005) 5011:Broken Laughter: Select Fragments of Greek Comedy 2290:– FORTRAN/C data analysis framework developed at 2237:Notable free software for data analysis include: 1972: 1726:that can adversely affect analysis. For example, 1657: 1006:+ error), where the model is designed such that ( 921:mentioned in the lead paragraph of this section. 8714: 8172:"Data.Gov:Long-Term Pavement Performance (LTPP)" 7588: 7586: 7584: 6789:"Alternative Coding Schemes for Dummy Variables" 6024: 5360: 5259:"Unemployment, Inflation and the Phillips Curve" 5144:"Dual-use car may solve transportation problems" 4965:"Consultants Employed by McKinsey & Company" 3652:"Quantitative Data Cleaning for Large Databases" 3021: 2629:Data Analysis Techniques for Physical Scientists 2112:Statistics (M, SD, variance, skewness, kurtosis) 7734:Supplementary file 1. Cross-validation schema. 6920: 6761:Practice for Dealing With Outlying Observations 6556: 3836:"First-Order Logic: Formulas, Models, Tableaux" 3197:"Information Technology Analysts' Perspectives" 2776:Samandar, Petersson; Svantesson, Sofia (2017). 2415:Cross-industry standard process for data mining 2248:for monitoring and analyzing human development. 1685:Effective analysis requires obtaining relevant 1311:Analytic activities of data visualization users 964:depending on the implemented model's accuracy ( 831:Data is collected from a variety of sources. A 8336:Juran, Joseph M.; Godfrey, A. Blanton (1999). 8078:Statistical Methods for Survival Data Analysis 5692:Obtaining Information for Effective Management 4142:"Customer Purchases and Other Repeated Events" 2625:"The Multiple Facets of Correlation Functions" 2555:List of datasets for machine-learning research 1180: 8434: 7894:Human Development Indices and Indicators 2018 7691: 7581: 6725: 6270:, London: Springer London, pp. 165–171, 3842:, London: Springer London, pp. 131–154, 3421: 2941: 1666: 1590:- Is there a cluster of typical film lengths? 1450:- What director/film has won the most awards? 1205:Confirm main totals are the sum of subtotals; 1014:) minimize the error when the model predicts 658: 368: 7529:Liquet, Benoit; Riou, Jérémie (2013-06-08). 7042:Random sampling and randomization procedures 6258: 5964:(Thesis). Florida International University. 5654:Computational Statistics & Data Analysis 5370:Yanamandra, Venkataramana (September 2015). 5005:Antiphanes (2007), Olson, S. Douglas (ed.), 4385: 2306:– C++ data analysis framework developed at 2030:: should one use robust analysis techniques? 908: 7649: 7481: 7399:, CRC Press, pp. 106–139, 2015-07-28, 7252: 6542:: CS1 maint: DOI inactive as of May 2024 ( 6509:, Routledge, pp. 406–439, 2013-07-04, 6383:"Coupon Valuation and Interest Rate Cycles" 5924:, Oxford University Press, pp. 13–42, 5727:: CS1 maint: DOI inactive as of May 2024 ( 3649: 3321: 3319: 2270:and methods for statistical data analysis, 1916:Possible transformations of variables are: 1538:- What is the age distribution of shoppers? 1424:- How many manufacturers of cars are there? 1362:- How long is the movie Gone with the Wind? 8441: 8427: 8406:Using Multivariate Statistics, 5th Edition 8294:(with contributions by D.J. Hand) (2008). 8146:"LTPP International Data Analysis Contest" 7922: 7528: 6992: 6619: 6557:Davenport, Thomas; Harris, Jeanne (2007). 5603: 5601: 5369: 5298: 5004: 3469: 3467: 3465: 3201:Data Strategy in Colleges and Universities 3194: 2880:: CS1 maint: location missing publisher ( 2316:– Python library for scientific computing. 2081:Frequency counts (numbers and percentages) 1908: 1872: 932: 665: 651: 397: 375: 361: 7860: 7719: 7709: 7564: 7546: 7446:Starting Statistics: A Short, Clear Guide 7176: 6743: 6486: 5930:10.1093/acprof:oso/9780195379372.003.0003 5694:, Routledge, pp. 48–54, 2007-07-11, 5450: 4971:, Routledge, pp. 77–82, 2008-07-30, 4948: 4915: 4739: 4706: 4500: 4395: 4307: 4181: 3992: 3959: 3926: 3704: 3541: 3523: 3339: 3286: 3179: 3095: 2798: 2707:, Routledge, pp. 42–67, 2004-08-16, 1384:- What Kellogg's cereals have high fiber? 7931:, Berkeley, CA: Apress, pp. 33–59, 7801:, Chapman and Hall/CRC, pp. 24–56, 7753:"Cross-Sectionally Dependent Panel Data" 7080: 6983:Tabachnick & Fidell, 2007, p. 87-88. 6840: 5982: 5774: 4446:Issues in Software Engineering Education 4439: 4386:Tunqui Neira, José Manuel (2019-09-19). 4046: 3650:Hellerstein, Joseph (27 February 2008). 3325: 3316: 2897:"Dividing listening into its components" 2326: 1998:Final stage of the initial data analysis 1828: 1393:- Which funds underperformed the SP-500? 1306: 1240: 1102: 1094: 1084: 1049: 866: 751: 747: 8404:Tabachnick, B.G.; Fidell, L.S. (2007). 7972: 7694:"Bayesian analysis of retinotopic maps" 7592: 5915: 5741: 5598: 5180: 5148:Chemical & Engineering News Archive 3840:Mathematical Logic for Computer Science 3833: 3567:Judaism, human rights, and human values 3564: 3462: 2942:Judd, Charles; McCleland, Gary (1989). 2845: 2193: 2167:Exploratory and confirmatory approaches 2064:Univariate statistics (single variable) 1984:Basic statistics of important variables 1507:- What is the range of car horsepowers? 1446:- What is the car with the highest MPG? 994:(advertising). It may be described as ( 14: 8715: 8338:Juran's Quality Handbook, 5th Edition. 7923:Wiley, Matt; Wiley, Joshua F. (2019), 7799:Computer Intensive Statistical Methods 7792: 7253:Fitzmaurice, Kathryn (17 March 2015). 6892:. Southern Illinois University Press. 6887: 6622:"Article 2.2.1 (Scope of the Section)" 6421:"25. General government total outlays" 6096: 5957: 5888: 5256: 5223: 4309:10.7287/peerj.preprints.27793v1/supp-1 4085: 3800: 3244: 3060:Handbook of Petroleum Product Analysis 2780:. Högskolan i Gävle, Företagsekonomi. 2266:– A visual programming tool featuring 1226:for them, such as the mean (average), 8422: 8238: 8200: 8178:from the original on November 1, 2017 8152:from the original on October 21, 2017 8143: 7750: 7650:Truswell IV, William H., ed. (2009), 7637: 7625: 7330: 7315: 7309: 7297: 7285: 7191: 7068: 6971: 6959: 6828: 6713: 6661: 6380: 6347: 6059: 5333: 5046: 4756:"Product comparison chart: Wearables" 4515: 3128: 2973:The Annals of Mathematical Statistics 2966: 2894: 2520:Structured data analysis (statistics) 2345:Kaggle competition, which is held by 2158: 2126: 2067:Bivariate associations (correlations) 1820: 1512:- What actresses are in the data set? 1120:may be used to demonstrate the trend. 1034:is a computer application that takes 8074:"Examples of Survival Data Analysis" 6303: 5465: 3875: 3668:from the original on 13 October 2013 3487:from the original on October 5, 2014 3403:from the original on 29 October 2013 2740: 2336:International data analysis contests 2244:– A database system endorsed by the 2087:circumambulations (crosstabulations) 2070:Graphical techniques (scatter plots) 1938:One should check the success of the 1896:), which gives an indication of the 1564:- Are there any outliers in protein? 1503:- What is the range of film lengths? 813: 790:, defined data analysis in 1961, as: 760:, by Schutt & O'Neil (2013) 756:Data science process flowchart from 8310:Graphical Methods for Data Analysis 7135: 6993:Tchakarova, Kalina (October 2020). 5496: 4258: 2901:Listening in the Language Classroom 2284:– Python library for data analysis. 1783:Analytics and business intelligence 1735:Psychology of Intelligence Analysis 1717: 1701:(CBO) estimated that extending the 1316:points, and arranging data points. 1303:Analytical activities of data users 1166:is typically used for this message. 24: 8281: 8148:. Federal Highway Administration. 8144:Nehme, Jean (September 29, 2016). 8125:from the original on 16 April 2021 8012:10.1016/b978-0-08-101877-4.00003-x 7896:. United Nations. pp. 21–41. 7658:, Stuttgart: Georg Thieme Verlag, 7011:10.5553/eelc/187791072020005003006 6939:10.1111/j.1745-3984.1979.tb00091.x 6927:Journal of Educational Measurement 6507:Building Energy Management Systems 5789:10.1093/owc/9780199536993.003.0193 5688:"Information relevant to your job" 4146:Data Analysis Using SQL and Excel® 1837: 1809:, most educators have access to a 1773: 1112:during the course of the process. 1091:Data and information visualization 1063:Data and information visualization 862: 826: 42:Data and information visualization 25: 8759: 8324:Python Data Analysis, 2nd Edition 7925:"Multivariate Data Visualization" 7793:Hjorth, J.S. 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Cambridge, MA. 5798:978-0-19-953699-3 5753:978-1-4419-3178-8 5709:978-0-08-054430-4 5585:Mary Shaw (2002) 5497:Dul, Jan (2015). 5280:978-0-333-64444-7 5235:978-1-4722-4830-5 5028:978-0-19-928785-7 4986:978-1-315-70197-4 4760:PsycEXTRA Dataset 4577:978-0-7923-8630-8 4539:978-1-107-26145-7 4463:978-1-4613-9616-1 4265:The Gerontologist 4163:978-1-119-18341-9 4028:978-1-4725-6167-1 3887:978-0-19-875094-9 3857:978-1-4471-4128-0 3812:978-1-118-61204-0 3374:978-1-60547-087-0 3256:978-1-118-89810-9 3218:978-0-429-43756-4 3107:978-1-119-60014-5 3077:978-1-118-98637-0 3056:"USE OF THE DATA" 3041:978-1-449-35865-5 2918:978-0-511-57594-5 2859:978-0-12-411528-6 2817:10.1002/sam.10106 2747:SPIE Professional 2722:978-1-4106-1142-0 2646:978-1-108-41678-8 2605:978-0-429-09529-0 2596:10.1201/b16666-14 2540:Unstructured data 2375:Actuarial science 2049:propensity scores 1993:Cross-tabulations 1728:confirmation bias 1655: 1654: 1638:Contextualization 821:experimental unit 814:Data requirements 732:unstructured data 675: 674: 526:Turbulence models 506:Lattice Boltzmann 486:Finite difference 385: 384: 344:Statistical model 334:Visual perception 109:Important figures 16:(Redirected from 8755: 8443: 8436: 8429: 8420: 8419: 8273: 8235: 8188: 8187: 8185: 8183: 8174:. May 26, 2016. 8168: 8162: 8161: 8159: 8157: 8141: 8135: 8134: 8132: 8130: 8111: 8105: 8104: 8103: 8102: 8070: 8064: 8063: 8037: 8031: 8030: 8029: 8028: 7995: 7989: 7988: 7977:(2nd ed.). 7970: 7964: 7963: 7962: 7961: 7920: 7914: 7913: 7889: 7883: 7882: 7864: 7832: 7826: 7825: 7824: 7823: 7790: 7784: 7783: 7782: 7781: 7748: 7742: 7733: 7723: 7713: 7689: 7683: 7682: 7681: 7680: 7647: 7641: 7635: 7629: 7623: 7617: 7616: 7614: 7613: 7590: 7579: 7578: 7568: 7550: 7526: 7520: 7519: 7479: 7473: 7472: 7471: 7470: 7438: 7432: 7431: 7430: 7429: 7405:10.1201/b18808-8 7397:Spatial Analysis 7389: 7383: 7382: 7381: 7380: 7340: 7334: 7328: 7322: 7319: 7313: 7307: 7301: 7295: 7289: 7283: 7277: 7276: 7250: 7244: 7243: 7242: 7241: 7201: 7195: 7189: 7183: 7182: 7180: 7156: 7150: 7149: 7133: 7127: 7126: 7078: 7072: 7066: 7060: 7059: 7058: 7057: 7051:10.3403/30137438 7037: 7031: 7030: 6990: 6984: 6981: 6975: 6969: 6963: 6957: 6951: 6950: 6918: 6912: 6911: 6885: 6879: 6878: 6838: 6832: 6826: 6820: 6819: 6818: 6817: 6785: 6779: 6778: 6777: 6776: 6756: 6750: 6749: 6747: 6723: 6717: 6711: 6705: 6704: 6698: 6693: 6691: 6683: 6672:10.2172/10170345 6659: 6653: 6652: 6651: 6650: 6617: 6611: 6595: 6589: 6579: 6573: 6572: 6554: 6548: 6547: 6541: 6533: 6532: 6531: 6499: 6493: 6492: 6490: 6466: 6460: 6459: 6453: 6448: 6446: 6438: 6436: 6435: 6417: 6411: 6410: 6378: 6372: 6371: 6345: 6339: 6338: 6332: 6327: 6325: 6317: 6314:10.17487/rfc4407 6301: 6295: 6294: 6293: 6292: 6260:Linsey, Julie S. 6256: 6250: 6249: 6209: 6203: 6202: 6200: 6199: 6190:. Archived from 6184: 6178: 6177: 6171: 6166: 6164: 6156: 6154: 6153: 6135: 6129: 6128: 6126: 6125: 6119: 6108: 6100: 6094: 6093: 6057: 6051: 6050: 6022: 6016: 6015: 6014: 6013: 5980: 5974: 5973: 5955: 5949: 5948: 5947: 5946: 5913: 5907: 5906: 5886: 5880: 5879: 5842: 5836: 5835: 5833: 5832: 5822: 5814: 5808: 5807: 5806: 5805: 5772: 5766: 5765: 5739: 5733: 5732: 5726: 5718: 5717: 5716: 5684: 5678: 5677: 5646: 5640: 5639: 5605: 5596: 5583: 5577: 5564: 5558: 5545: 5539: 5538: 5494: 5488: 5487: 5485: 5484: 5471: 5466:Feinmann, Jane. 5463: 5457: 5456: 5454: 5429: 5423: 5422: 5406: 5400: 5399: 5367: 5358: 5357: 5331: 5325: 5324: 5296: 5290: 5289: 5288: 5287: 5254: 5248: 5247: 5221: 5215: 5214: 5195:10.2307/20075292 5181:Heckman (1978). 5178: 5172: 5171: 5140: 5134: 5133: 5127: 5122: 5120: 5112: 5110: 5109: 5091: 5085: 5084: 5059:(192): 407–415. 5044: 5038: 5037: 5036: 5035: 5002: 4996: 4995: 4994: 4993: 4961: 4955: 4954: 4952: 4928: 4922: 4921: 4919: 4895: 4889: 4888: 4882: 4877: 4875: 4867: 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3627: 3619: 3617: 3616: 3603: 3595: 3589: 3588: 3562: 3556: 3555: 3545: 3527: 3503: 3497: 3496: 3494: 3492: 3486: 3479: 3471: 3460: 3459: 3419: 3413: 3412: 3410: 3408: 3393: 3387: 3386: 3360: 3354: 3353: 3343: 3323: 3314: 3313: 3307: 3302: 3300: 3292: 3290: 3275: 3269: 3268: 3242: 3236: 3235: 3234: 3233: 3192: 3186: 3185: 3183: 3159: 3153: 3152: 3126: 3120: 3119: 3093: 3087: 3086: 3085: 3084: 3052: 3046: 3045: 3022:Schutt, Rachel; 3019: 3000: 2999: 2997: 2996: 2964: 2958: 2957: 2939: 2928: 2927: 2926: 2925: 2892: 2886: 2885: 2879: 2871: 2843: 2837: 2836: 2796: 2790: 2789: 2773: 2767: 2766: 2738: 2732: 2731: 2730: 2729: 2697: 2691: 2686: 2680: 2662: 2656: 2655: 2654: 2653: 2621: 2615: 2614: 2613: 2612: 2580: 2475:Machine learning 2470:Fourier analysis 2420:Data acquisition 2276:machine learning 2205:Cross-validation 2133:nonlinear system 1724:cognitive biases 1718:Cognitive biases 1681: 1319: 1318: 919:iterative phases 738:Data integration 667: 660: 653: 579:Particle-in-cell 501:Boundary element 461:Yukawa potential 424:Particle physics 414:Electromagnetics 401: 387: 386: 377: 370: 363: 349:Misleading graph 172:Leland Wilkinson 147:David McCandless 48:Major dimensions 30: 29: 21: 8763: 8762: 8758: 8757: 8756: 8754: 8753: 8752: 8748:Data management 8728:Data processing 8713: 8712: 8711: 8706: 8682:Synchronization 8452: 8447: 8388:Richard Veryard 8284: 8282:Further reading 8262: 8240:Adèr, Herman J. 8224: 8202:Adèr, Herman J. 8197: 8192: 8191: 8181: 8179: 8170: 8169: 8165: 8155: 8153: 8142: 8138: 8128: 8126: 8113: 8112: 8108: 8100: 8098: 8096: 8072: 8071: 8067: 8052: 8038: 8034: 8026: 8024: 8022: 7996: 7992: 7985: 7984:978-148428155-0 7971: 7967: 7959: 7957: 7947: 7921: 7917: 7890: 7886: 7833: 7829: 7821: 7819: 7817: 7791: 7787: 7779: 7777: 7775: 7749: 7745: 7690: 7686: 7678: 7676: 7674: 7648: 7644: 7636: 7632: 7624: 7620: 7611: 7609: 7591: 7582: 7527: 7523: 7480: 7476: 7468: 7466: 7464: 7440: 7439: 7435: 7427: 7425: 7415: 7391: 7390: 7386: 7378: 7376: 7374: 7342: 7341: 7337: 7329: 7325: 7320: 7316: 7308: 7304: 7296: 7292: 7284: 7280: 7265: 7251: 7247: 7239: 7237: 7227: 7203: 7202: 7198: 7190: 7186: 7157: 7153: 7134: 7130: 7079: 7075: 7067: 7063: 7055: 7053: 7039: 7038: 7034: 6991: 6987: 6982: 6978: 6970: 6966: 6958: 6954: 6919: 6915: 6900: 6886: 6882: 6839: 6835: 6827: 6823: 6815: 6813: 6811: 6787: 6786: 6782: 6774: 6772: 6758: 6757: 6753: 6724: 6720: 6712: 6708: 6696: 6694: 6685: 6684: 6660: 6656: 6648: 6646: 6644: 6618: 6614: 6606:Wayback Machine 6596: 6592: 6580: 6576: 6569: 6555: 6551: 6535: 6534: 6529: 6527: 6525: 6501: 6500: 6496: 6467: 6463: 6451: 6449: 6440: 6439: 6433: 6431: 6419: 6418: 6414: 6379: 6375: 6360: 6346: 6342: 6330: 6328: 6319: 6318: 6302: 6298: 6290: 6288: 6286: 6257: 6253: 6210: 6206: 6197: 6195: 6186: 6185: 6181: 6169: 6167: 6158: 6157: 6151: 6149: 6137: 6136: 6132: 6123: 6121: 6117: 6106: 6102: 6101: 6097: 6082: 6058: 6054: 6047: 6037:10.1007/b105100 6023: 6019: 6011: 6009: 6007: 5981: 5977: 5956: 5952: 5944: 5942: 5940: 5914: 5910: 5887: 5883: 5868: 5844: 5843: 5839: 5830: 5828: 5820: 5816: 5815: 5811: 5803: 5801: 5799: 5773: 5769: 5754: 5740: 5736: 5720: 5719: 5714: 5712: 5710: 5686: 5685: 5681: 5648: 5647: 5643: 5636: 5606: 5599: 5594:Wayback Machine 5584: 5580: 5575:Wayback Machine 5565: 5561: 5556:Wayback Machine 5546: 5542: 5495: 5491: 5482: 5480: 5464: 5460: 5430: 5426: 5407: 5403: 5368: 5361: 5346: 5332: 5328: 5297: 5293: 5285: 5283: 5281: 5255: 5251: 5236: 5222: 5218: 5179: 5175: 5142: 5141: 5137: 5125: 5123: 5114: 5113: 5107: 5105: 5093: 5092: 5088: 5065:10.2307/2553697 5045: 5041: 5033: 5031: 5029: 5003: 4999: 4991: 4989: 4987: 4963: 4962: 4958: 4929: 4925: 4896: 4892: 4880: 4878: 4869: 4868: 4862: 4860: 4848: 4847: 4843: 4834: 4832: 4828: 4821: 4817: 4816: 4812: 4803: 4801: 4797: 4790: 4786: 4785: 4781: 4772: 4770: 4754: 4753: 4749: 4720: 4716: 4687: 4683: 4671: 4669: 4660: 4659: 4653: 4651: 4639: 4638: 4634: 4622: 4620: 4611: 4610: 4604: 4602: 4590: 4589: 4585: 4578: 4558:"X-Bar Chart". 4557: 4556: 4552: 4544: 4542: 4540: 4514: 4510: 4480: 4476: 4468: 4466: 4464: 4438: 4434: 4422: 4420: 4411: 4410: 4384: 4380: 4365: 4343: 4342: 4338: 4326: 4324: 4315: 4314: 4296: 4292: 4257: 4253: 4245: 4243: 4229: 4228: 4224: 4215: 4213: 4209: 4186: 4180: 4176: 4168: 4166: 4164: 4140: 4139: 4135: 4084: 4080: 4045: 4041: 4033: 4031: 4029: 4007: 4006: 4002: 3973: 3969: 3940: 3936: 3907: 3903: 3888: 3874: 3870: 3862: 3860: 3858: 3832: 3828: 3813: 3799: 3795: 3783: 3781: 3772: 3771: 3757: 3756: 3752: 3719: 3718: 3714: 3685: 3681: 3671: 3669: 3665: 3654: 3648: 3644: 3632: 3630: 3621: 3620: 3614: 3612: 3596: 3592: 3577: 3563: 3559: 3504: 3500: 3490: 3488: 3484: 3477: 3473: 3472: 3463: 3420: 3416: 3406: 3404: 3397:"Data Cleaning" 3395: 3394: 3390: 3375: 3361: 3357: 3324: 3317: 3305: 3303: 3294: 3293: 3277: 3276: 3272: 3257: 3243: 3239: 3231: 3229: 3219: 3193: 3189: 3160: 3156: 3141: 3127: 3123: 3108: 3094: 3090: 3082: 3080: 3078: 3054: 3053: 3049: 3042: 3020: 3003: 2994: 2992: 2965: 2961: 2954: 2940: 2931: 2923: 2921: 2919: 2893: 2889: 2873: 2872: 2860: 2844: 2840: 2797: 2793: 2774: 2770: 2739: 2735: 2727: 2725: 2723: 2699: 2698: 2694: 2687: 2683: 2663: 2659: 2651: 2649: 2647: 2623: 2622: 2618: 2610: 2608: 2606: 2582: 2581: 2574: 2569: 2564: 2559: 2480:Multilinear PCA 2430:Data governance 2370: 2338: 2329: 2235: 2196: 2169: 2161: 2129: 2058: 2026:In the case of 2019:In the case of 2003: 2000: 1979: 1975: 1946: 1943: 1936: 1915: 1911: 1875: 1840: 1838:Quality of data 1831: 1823: 1803: 1791: 1785: 1776: 1774:Smart buildings 1771: 1748: 1720: 1683: 1677: 1669: 1660: 1492:Determine Range 1334: 1329: 1305: 1216:DuPont analysis 1193:Jonathan Koomey 1189: 1187:Problem solving 1183: 1093: 1087: 1065: 1048: 1028: 944:(also known as 935: 911: 898: 892: 884:structured data 865: 863:Data processing 845:Data collection 829: 827:Data collection 816: 804:CRISP framework 750: 671: 642: 641: 597: 589: 588: 569: 561: 560: 541: 531: 530: 481: 471: 470: 466:Morse potential 446: 436: 381: 293:Marimekko chart 122:Ben Shneiderman 28: 23: 22: 15: 12: 11: 5: 8761: 8751: 8750: 8745: 8740: 8735: 8730: 8725: 8708: 8707: 8705: 8704: 8699: 8694: 8689: 8684: 8679: 8674: 8669: 8664: 8659: 8654: 8649: 8644: 8639: 8634: 8629: 8624: 8619: 8614: 8609: 8607:Pre-processing 8604: 8599: 8594: 8589: 8584: 8579: 8574: 8569: 8564: 8559: 8554: 8549: 8544: 8543: 8542: 8537: 8532: 8518: 8513: 8508: 8503: 8498: 8493: 8488: 8483: 8478: 8473: 8468: 8463: 8457: 8454: 8453: 8446: 8445: 8438: 8431: 8423: 8417: 8416: 8402: 8385: 8371: 8362: 8348: 8334: 8332:978-1787127487 8320: 8306: 8283: 8280: 8279: 8278: 8274: 8260: 8236: 8222: 8196: 8193: 8190: 8189: 8163: 8136: 8106: 8094: 8065: 8050: 8032: 8020: 7990: 7983: 7965: 7945: 7915: 7884: 7827: 7815: 7785: 7773: 7743: 7684: 7672: 7642: 7630: 7618: 7580: 7521: 7474: 7462: 7433: 7413: 7384: 7372: 7335: 7333:, p. 363. 7323: 7314: 7302: 7290: 7278: 7263: 7245: 7225: 7196: 7194:, p. 345. 7184: 7151: 7136:Jaarsma, C.F. 7128: 7073: 7061: 7032: 7005:(3): 168–170. 6985: 6976: 6974:, p. 344. 6964: 6952: 6933:(2): 101–108. 6913: 6898: 6880: 6853:(7): 585–587. 6833: 6821: 6809: 6780: 6751: 6718: 6716:, p. 337. 6706: 6697:|journal= 6654: 6642: 6626:Commercial Law 6612: 6590: 6574: 6567: 6549: 6523: 6494: 6461: 6452:|journal= 6412: 6373: 6358: 6340: 6331:|journal= 6296: 6284: 6251: 6204: 6179: 6170:|journal= 6130: 6104:"Introduction" 6095: 6080: 6052: 6045: 6017: 6005: 5975: 5950: 5938: 5908: 5881: 5866: 5837: 5809: 5797: 5767: 5752: 5734: 5708: 5679: 5641: 5634: 5597: 5578: 5559: 5540: 5489: 5458: 5424: 5401: 5359: 5344: 5326: 5291: 5279: 5249: 5234: 5216: 5173: 5135: 5126:|journal= 5086: 5039: 5027: 4997: 4985: 4956: 4923: 4890: 4881:|journal= 4852:. 2008-10-01. 4841: 4810: 4779: 4747: 4714: 4681: 4672:|journal= 4632: 4623:|journal= 4583: 4576: 4550: 4538: 4508: 4474: 4462: 4432: 4423:|journal= 4378: 4363: 4336: 4327:|journal= 4290: 4251: 4222: 4174: 4162: 4133: 4098:(1): 102–117. 4078: 4039: 4027: 4000: 3967: 3934: 3901: 3886: 3868: 3856: 3826: 3811: 3793: 3784:|journal= 3750: 3712: 3679: 3642: 3633:|journal= 3598:Hanzo, Lajos. 3590: 3575: 3557: 3498: 3461: 3434:(2): 243–250. 3414: 3388: 3373: 3355: 3315: 3306:|journal= 3270: 3255: 3237: 3217: 3187: 3154: 3139: 3121: 3106: 3088: 3076: 3047: 3040: 3032:O'Reilly Media 3001: 2959: 2952: 2929: 2917: 2887: 2858: 2838: 2791: 2768: 2733: 2721: 2692: 2681: 2672:(2), 300-311. 2657: 2645: 2616: 2604: 2571: 2570: 2568: 2565: 2563: 2560: 2558: 2557: 2552: 2547: 2542: 2537: 2532: 2527: 2522: 2517: 2512: 2507: 2502: 2497: 2492: 2487: 2482: 2477: 2472: 2467: 2462: 2457: 2452: 2447: 2442: 2437: 2432: 2427: 2422: 2417: 2412: 2407: 2402: 2397: 2392: 2387: 2382: 2377: 2371: 2369: 2366: 2365: 2364: 2350: 2337: 2334: 2328: 2325: 2324: 2323: 2317: 2311: 2301: 2295: 2285: 2279: 2261: 2255: 2249: 2234: 2231: 2230: 2229: 2217: 2195: 2192: 2168: 2165: 2160: 2157: 2128: 2125: 2124: 2123: 2122: 2121: 2120: 2119: 2116: 2113: 2104: 2103: 2102: 2099: 2096: 2095: 2094: 2091: 2088: 2082: 2072: 2071: 2068: 2065: 2057: 2054: 2053: 2052: 2041: 2034: 2031: 2024: 2017: 1999: 1996: 1995: 1994: 1991: 1988: 1985: 1974: 1971: 1970: 1969: 1962: 1955: 1935: 1932: 1931: 1930: 1927: 1924: 1921: 1910: 1907: 1906: 1905: 1890: 1874: 1871: 1867: 1866: 1859: 1856: 1839: 1836: 1830: 1827: 1822: 1819: 1802: 1799: 1787:Main article: 1784: 1781: 1775: 1772: 1770: 1767: 1747: 1744: 1739:Richards Heuer 1719: 1716: 1670: 1668: 1665: 1659: 1656: 1653: 1652: 1647: 1644: 1641: 1634: 1630: 1629: 1609: 1606: 1603: 1598: 1594: 1593: 1583: 1580: 1577: 1572: 1568: 1567: 1557: 1554: 1551: 1549:Find Anomalies 1546: 1542: 1541: 1531: 1528: 1525: 1520: 1516: 1515: 1500: 1497: 1494: 1489: 1485: 1484: 1474: 1471: 1468: 1463: 1459: 1458: 1443: 1440: 1437: 1432: 1428: 1427: 1412: 1409: 1406: 1401: 1397: 1396: 1381: 1378: 1375: 1370: 1366: 1365: 1355: 1352: 1349: 1347:Retrieve Value 1344: 1340: 1339: 1336: 1331: 1326: 1323: 1304: 1301: 1280:Phillips Curve 1259:MECE principle 1247:MECE principle 1220: 1219: 1212: 1209: 1206: 1203: 1200: 1182: 1179: 1178: 1177: 1170: 1167: 1159: 1151: 1147: 1140: 1121: 1089:Main article: 1086: 1083: 1061:Main article: 1047: 1044: 1038:and generates 1027: 1024: 961:residual error 934: 931: 910: 907: 896:Data cleansing 894:Main article: 891: 888: 864: 861: 849:data gathering 828: 825: 815: 812: 775:for obtaining 749: 746: 728:text analytics 673: 672: 670: 669: 662: 655: 647: 644: 643: 640: 639: 634: 629: 624: 619: 614: 609: 604: 598: 595: 594: 591: 590: 587: 586: 581: 576: 570: 567: 566: 563: 562: 559: 558: 553: 551:Gibbs sampling 548: 542: 537: 536: 533: 532: 529: 528: 523: 518: 513: 511:Riemann solver 508: 503: 498: 496:Finite element 493: 488: 482: 479:Fluid dynamics 477: 476: 473: 472: 469: 468: 463: 458: 453: 447: 444: 443: 440: 439: 438: 437: 431: 429:Thermodynamics 426: 421: 416: 411: 403: 402: 394: 393: 383: 382: 380: 379: 372: 365: 357: 354: 353: 352: 351: 346: 341: 336: 331: 326: 321: 316: 311: 303: 302: 301:Related topics 298: 297: 296: 295: 290: 285: 280: 278:Small multiple 275: 270: 265: 260: 255: 253:Stripe graphic 250: 245: 240: 235: 230: 225: 220: 215: 210: 205: 197: 196: 192: 191: 190: 189: 184: 179: 174: 169: 167:Hadley Wickham 164: 159: 154: 149: 144: 139: 134: 129: 124: 119: 117:Tamara Munzner 111: 110: 106: 105: 104: 103: 98: 93: 88: 83: 78: 73: 68: 63: 58: 50: 49: 45: 44: 38: 37: 26: 9: 6: 4: 3: 2: 8760: 8749: 8746: 8744: 8741: 8739: 8736: 8734: 8731: 8729: 8726: 8724: 8723:Data analysis 8721: 8720: 8718: 8703: 8700: 8698: 8695: 8693: 8690: 8688: 8685: 8683: 8680: 8678: 8675: 8673: 8670: 8668: 8665: 8663: 8660: 8658: 8655: 8653: 8650: 8648: 8645: 8643: 8640: 8638: 8635: 8633: 8630: 8628: 8625: 8623: 8620: 8618: 8615: 8613: 8610: 8608: 8605: 8603: 8600: 8598: 8595: 8593: 8590: 8588: 8585: 8583: 8580: 8578: 8575: 8573: 8570: 8568: 8565: 8563: 8560: 8558: 8555: 8553: 8550: 8548: 8545: 8541: 8538: 8536: 8533: 8531: 8528: 8527: 8526: 8522: 8519: 8517: 8514: 8512: 8509: 8507: 8504: 8502: 8499: 8497: 8494: 8492: 8489: 8487: 8484: 8482: 8479: 8477: 8474: 8472: 8469: 8467: 8464: 8462: 8459: 8458: 8455: 8451: 8444: 8439: 8437: 8432: 8430: 8425: 8424: 8421: 8415: 8411: 8407: 8403: 8401: 8400:0-632-01311-7 8397: 8393: 8389: 8386: 8384: 8383:0-8247-4614-7 8380: 8376: 8372: 8369: 8368: 8363: 8361: 8360:0-8039-5772-6 8357: 8353: 8349: 8347: 8346:0-07-034003-X 8343: 8339: 8335: 8333: 8329: 8325: 8321: 8319: 8318:0-534-98052-X 8315: 8311: 8307: 8305: 8301: 8297: 8293: 8289: 8286: 8285: 8275: 8271: 8267: 8263: 8261:9789079418015 8257: 8253: 8249: 8248:Hand, David J 8245: 8241: 8237: 8233: 8229: 8225: 8223:9789079418015 8219: 8215: 8211: 8210:Hand, David J 8207: 8203: 8199: 8198: 8177: 8173: 8167: 8151: 8147: 8140: 8124: 8120: 8116: 8110: 8097: 8091: 8087: 8083: 8079: 8075: 8069: 8061: 8057: 8053: 8051:90-5808-102-8 8047: 8043: 8036: 8023: 8017: 8013: 8009: 8005: 8001: 7994: 7986: 7980: 7976: 7969: 7956: 7952: 7948: 7942: 7938: 7934: 7930: 7926: 7919: 7911: 7907: 7903: 7899: 7895: 7888: 7880: 7876: 7872: 7868: 7863: 7858: 7854: 7850: 7846: 7842: 7838: 7831: 7818: 7812: 7808: 7804: 7800: 7796: 7789: 7776: 7770: 7766: 7762: 7758: 7754: 7747: 7741: 7737: 7731: 7727: 7722: 7717: 7712: 7707: 7703: 7699: 7695: 7688: 7675: 7669: 7665: 7661: 7657: 7653: 7646: 7639: 7634: 7627: 7622: 7608: 7604: 7600: 7596: 7589: 7587: 7585: 7576: 7572: 7567: 7562: 7558: 7554: 7549: 7544: 7540: 7536: 7532: 7525: 7517: 7513: 7509: 7505: 7501: 7497: 7493: 7489: 7485: 7478: 7465: 7459: 7455: 7451: 7447: 7443: 7437: 7424: 7420: 7416: 7410: 7406: 7402: 7398: 7394: 7388: 7375: 7369: 7365: 7361: 7357: 7353: 7349: 7345: 7339: 7332: 7327: 7318: 7311: 7306: 7299: 7294: 7287: 7282: 7274: 7270: 7266: 7260: 7256: 7249: 7236: 7232: 7228: 7222: 7218: 7214: 7210: 7206: 7200: 7193: 7188: 7179: 7174: 7170: 7166: 7162: 7155: 7147: 7143: 7139: 7132: 7124: 7120: 7116: 7112: 7108: 7104: 7100: 7096: 7092: 7088: 7084: 7077: 7070: 7065: 7052: 7048: 7044: 7043: 7036: 7028: 7024: 7020: 7016: 7012: 7008: 7004: 7000: 6996: 6989: 6980: 6973: 6968: 6961: 6956: 6948: 6944: 6940: 6936: 6932: 6928: 6924: 6917: 6909: 6905: 6901: 6899:0-585-17889-5 6895: 6891: 6884: 6876: 6872: 6868: 6864: 6860: 6856: 6852: 6848: 6844: 6837: 6830: 6825: 6812: 6806: 6802: 6798: 6794: 6790: 6784: 6771: 6767: 6763: 6762: 6755: 6746: 6741: 6737: 6733: 6729: 6722: 6715: 6710: 6702: 6689: 6681: 6677: 6673: 6669: 6665: 6658: 6645: 6639: 6635: 6631: 6627: 6623: 6616: 6610: 6607: 6603: 6600: 6594: 6587: 6584: 6578: 6570: 6564: 6560: 6553: 6545: 6539: 6526: 6520: 6516: 6512: 6508: 6504: 6498: 6489: 6484: 6480: 6476: 6472: 6465: 6457: 6444: 6430: 6426: 6422: 6416: 6408: 6404: 6400: 6396: 6392: 6388: 6384: 6377: 6369: 6365: 6361: 6355: 6351: 6344: 6336: 6323: 6315: 6311: 6307: 6300: 6287: 6281: 6277: 6273: 6269: 6265: 6261: 6255: 6247: 6243: 6239: 6235: 6231: 6227: 6223: 6219: 6215: 6208: 6194:on 2014-10-29 6193: 6189: 6183: 6175: 6162: 6148: 6144: 6140: 6134: 6116: 6112: 6105: 6099: 6091: 6087: 6083: 6081:9780429303647 6077: 6073: 6069: 6065: 6064: 6056: 6048: 6042: 6038: 6034: 6030: 6029: 6021: 6008: 6002: 5998: 5994: 5990: 5986: 5979: 5971: 5967: 5963: 5962: 5954: 5941: 5935: 5931: 5927: 5923: 5919: 5912: 5904: 5903:10.3386/w3303 5900: 5896: 5892: 5885: 5877: 5873: 5869: 5867:9789264273818 5863: 5859: 5855: 5851: 5847: 5841: 5826: 5819: 5813: 5800: 5794: 5790: 5786: 5782: 5778: 5771: 5763: 5759: 5755: 5749: 5745: 5738: 5730: 5724: 5711: 5705: 5701: 5697: 5693: 5689: 5683: 5675: 5671: 5667: 5663: 5659: 5655: 5651: 5645: 5637: 5635:9780998133102 5631: 5627: 5623: 5619: 5615: 5611: 5604: 5602: 5595: 5591: 5588: 5582: 5576: 5572: 5569: 5563: 5557: 5553: 5550: 5544: 5536: 5532: 5528: 5524: 5520: 5516: 5512: 5508: 5504: 5500: 5493: 5479: 5475: 5470: 5462: 5453: 5448: 5444: 5440: 5436: 5428: 5420: 5416: 5412: 5405: 5397: 5393: 5389: 5385: 5381: 5377: 5373: 5366: 5364: 5355: 5351: 5347: 5345:0-404-19454-0 5341: 5338:. AMS Press. 5337: 5330: 5322: 5318: 5314: 5310: 5306: 5302: 5295: 5282: 5276: 5272: 5268: 5264: 5260: 5253: 5245: 5241: 5237: 5231: 5227: 5220: 5212: 5208: 5204: 5200: 5196: 5192: 5188: 5184: 5177: 5169: 5165: 5161: 5157: 5153: 5149: 5145: 5139: 5131: 5118: 5104: 5100: 5096: 5090: 5082: 5078: 5074: 5070: 5066: 5062: 5058: 5054: 5050: 5043: 5030: 5024: 5020: 5016: 5012: 5008: 5001: 4988: 4982: 4978: 4974: 4970: 4966: 4960: 4951: 4946: 4942: 4938: 4934: 4927: 4918: 4913: 4909: 4905: 4901: 4894: 4886: 4873: 4859: 4855: 4851: 4845: 4827: 4820: 4814: 4796: 4789: 4783: 4769: 4765: 4761: 4757: 4751: 4742: 4737: 4733: 4729: 4725: 4718: 4709: 4704: 4700: 4696: 4692: 4685: 4677: 4664: 4650: 4646: 4642: 4636: 4628: 4615: 4601: 4597: 4593: 4587: 4579: 4573: 4569: 4565: 4561: 4554: 4541: 4535: 4531: 4527: 4523: 4519: 4512: 4503: 4498: 4494: 4490: 4486: 4478: 4465: 4459: 4455: 4451: 4447: 4443: 4436: 4428: 4415: 4407: 4403: 4398: 4393: 4389: 4382: 4374: 4370: 4366: 4364:9781529768749 4360: 4356: 4352: 4348: 4347: 4340: 4332: 4319: 4310: 4305: 4301: 4294: 4286: 4282: 4278: 4274: 4270: 4266: 4262: 4255: 4242: 4238: 4234: 4233: 4226: 4208: 4204: 4200: 4196: 4192: 4185: 4178: 4165: 4159: 4155: 4151: 4147: 4143: 4137: 4129: 4125: 4121: 4117: 4113: 4109: 4105: 4101: 4097: 4093: 4089: 4082: 4074: 4070: 4066: 4062: 4058: 4054: 4050: 4043: 4030: 4024: 4020: 4016: 4012: 4011: 4004: 3995: 3990: 3986: 3982: 3978: 3971: 3962: 3957: 3953: 3949: 3945: 3938: 3929: 3924: 3920: 3916: 3912: 3905: 3897: 3893: 3889: 3883: 3879: 3872: 3859: 3853: 3849: 3845: 3841: 3837: 3830: 3822: 3818: 3814: 3808: 3804: 3797: 3789: 3776: 3768: 3764: 3760: 3754: 3746: 3742: 3738: 3734: 3730: 3726: 3722: 3716: 3707: 3702: 3698: 3694: 3690: 3683: 3664: 3660: 3653: 3646: 3638: 3625: 3611: 3607: 3602: 3594: 3586: 3582: 3578: 3576:0-585-24568-1 3572: 3568: 3561: 3553: 3549: 3544: 3539: 3535: 3531: 3526: 3521: 3517: 3513: 3509: 3502: 3483: 3476: 3470: 3468: 3466: 3457: 3453: 3449: 3445: 3441: 3437: 3433: 3429: 3425: 3418: 3402: 3398: 3392: 3384: 3380: 3376: 3370: 3366: 3359: 3351: 3347: 3342: 3337: 3333: 3329: 3322: 3320: 3311: 3298: 3289: 3284: 3280: 3274: 3266: 3262: 3258: 3252: 3248: 3241: 3228: 3224: 3220: 3214: 3210: 3206: 3202: 3198: 3191: 3182: 3177: 3173: 3169: 3165: 3158: 3150: 3146: 3142: 3140:0-226-50507-3 3136: 3132: 3125: 3117: 3113: 3109: 3103: 3099: 3092: 3079: 3073: 3069: 3065: 3061: 3057: 3051: 3043: 3037: 3033: 3029: 3025: 3024:O'Neil, Cathy 3018: 3016: 3014: 3012: 3010: 3008: 3006: 2990: 2986: 2982: 2978: 2974: 2970: 2963: 2955: 2953:0-15-516765-0 2949: 2945: 2944:Data Analysis 2938: 2936: 2934: 2920: 2914: 2910: 2906: 2902: 2898: 2891: 2883: 2877: 2869: 2865: 2861: 2855: 2852:. Amsterdam. 2851: 2850: 2842: 2834: 2830: 2826: 2822: 2818: 2814: 2810: 2806: 2802: 2795: 2787: 2783: 2779: 2772: 2764: 2760: 2756: 2752: 2748: 2744: 2737: 2724: 2718: 2714: 2710: 2706: 2702: 2696: 2690: 2685: 2679: 2675: 2671: 2667: 2661: 2648: 2642: 2638: 2634: 2630: 2626: 2620: 2607: 2601: 2597: 2593: 2589: 2585: 2579: 2577: 2572: 2556: 2553: 2551: 2548: 2546: 2543: 2541: 2538: 2536: 2533: 2531: 2528: 2526: 2523: 2521: 2518: 2516: 2513: 2511: 2508: 2506: 2503: 2501: 2498: 2496: 2493: 2491: 2488: 2486: 2483: 2481: 2478: 2476: 2473: 2471: 2468: 2466: 2463: 2461: 2458: 2456: 2453: 2451: 2448: 2446: 2443: 2441: 2438: 2436: 2433: 2431: 2428: 2426: 2425:Data blending 2423: 2421: 2418: 2416: 2413: 2411: 2408: 2406: 2403: 2401: 2398: 2396: 2393: 2391: 2388: 2386: 2383: 2381: 2378: 2376: 2373: 2372: 2362: 2358: 2354: 2351: 2348: 2344: 2343: 2342: 2333: 2321: 2318: 2315: 2312: 2309: 2305: 2302: 2299: 2296: 2293: 2289: 2286: 2283: 2280: 2277: 2273: 2269: 2265: 2262: 2259: 2256: 2253: 2250: 2247: 2243: 2240: 2239: 2238: 2227: 2226:bootstrapping 2223: 2222: 2218: 2215: 2211: 2207: 2206: 2202: 2201: 2200: 2191: 2189: 2185: 2181: 2177: 2173: 2164: 2156: 2154: 2150: 2146: 2142: 2138: 2134: 2117: 2114: 2111: 2110: 2109:Distribution 2108: 2107: 2105: 2100: 2097: 2092: 2089: 2086: 2085: 2084:Associations 2083: 2080: 2079: 2077: 2076: 2075: 2069: 2066: 2063: 2062: 2061: 2050: 2046: 2045:randomization 2042: 2039: 2038:bootstrapping 2035: 2032: 2029: 2025: 2022: 2018: 2015: 2012:: should one 2011: 2007: 2006: 2005: 1992: 1989: 1987:Scatter plots 1986: 1983: 1982: 1981: 1967: 1963: 1960: 1956: 1953: 1950: 1949: 1948: 1941: 1940:randomization 1928: 1925: 1922: 1919: 1918: 1917: 1903: 1899: 1895: 1891: 1888: 1887: 1886: 1883: 1880: 1870: 1864: 1860: 1857: 1854: 1850: 1849: 1848: 1846: 1835: 1826: 1818: 1816: 1812: 1808: 1798: 1796: 1790: 1780: 1766: 1764: 1759: 1755: 1753: 1743: 1740: 1736: 1731: 1729: 1725: 1715: 1713: 1707: 1704: 1703:Bush tax cuts 1700: 1696: 1692: 1688: 1682: 1680: 1674: 1664: 1651: 1648: 1645: 1642: 1640: 1639: 1635: 1631: 1628: 1627: 1623: 1622: 1618: 1617: 1613: 1610: 1607: 1604: 1602: 1599: 1595: 1592: 1591: 1587: 1584: 1581: 1578: 1576: 1573: 1569: 1566: 1565: 1561: 1558: 1555: 1552: 1550: 1547: 1543: 1540: 1539: 1535: 1532: 1529: 1526: 1524: 1521: 1517: 1514: 1513: 1509: 1508: 1504: 1501: 1498: 1495: 1493: 1490: 1486: 1483: 1482: 1478: 1475: 1472: 1469: 1467: 1464: 1460: 1457: 1456: 1452: 1451: 1447: 1444: 1441: 1438: 1436: 1435:Find Extremum 1433: 1429: 1426: 1425: 1421: 1420: 1416: 1413: 1410: 1407: 1405: 1402: 1398: 1395: 1394: 1390: 1389: 1385: 1382: 1379: 1376: 1374: 1371: 1367: 1364: 1363: 1359: 1356: 1353: 1350: 1348: 1345: 1341: 1332: 1327: 1320: 1317: 1309: 1300: 1298: 1294: 1291: 1287: 1285: 1281: 1276: 1271: 1268: 1264: 1260: 1256: 1248: 1243: 1239: 1237: 1233: 1229: 1225: 1217: 1213: 1210: 1207: 1204: 1201: 1198: 1197: 1196: 1194: 1188: 1175: 1171: 1168: 1165: 1160: 1157: 1152: 1148: 1145: 1141: 1138: 1134: 1130: 1126: 1122: 1119: 1115: 1114: 1113: 1105: 1097: 1092: 1082: 1079: 1075: 1069: 1064: 1056: 1052: 1046:Communication 1043: 1041: 1037: 1033: 1023: 1021: 1017: 1013: 1009: 1005: 1001: 997: 993: 989: 985: 981: 977: 973: 969: 967: 963: 962: 957: 953: 949: 948: 943: 939: 930: 928: 924: 920: 916: 906: 903: 902:data cleaning 897: 890:Data cleaning 887: 885: 882: 874: 869: 860: 857: 854: 850: 846: 842: 838: 834: 824: 822: 811: 809: 805: 801: 795: 791: 789: 786:Statistician 784: 782: 778: 774: 770: 769:Data analysis 766: 759: 754: 745: 743: 739: 735: 733: 729: 725: 721: 717: 713: 709: 705: 701: 697: 694: 691: 687: 683: 679: 678:Data analysis 668: 663: 661: 656: 654: 649: 648: 646: 645: 638: 635: 633: 630: 628: 625: 623: 620: 618: 615: 613: 610: 608: 605: 603: 600: 599: 593: 592: 585: 582: 580: 577: 575: 572: 571: 565: 564: 557: 554: 552: 549: 547: 544: 543: 540: 535: 534: 527: 524: 522: 519: 517: 514: 512: 509: 507: 504: 502: 499: 497: 494: 492: 491:Finite volume 489: 487: 484: 483: 480: 475: 474: 467: 464: 462: 459: 457: 454: 452: 449: 448: 442: 441: 435: 432: 430: 427: 425: 422: 420: 417: 415: 412: 410: 407: 406: 405: 404: 400: 396: 395: 392: 389: 388: 378: 373: 371: 366: 364: 359: 358: 356: 355: 350: 347: 345: 342: 340: 337: 335: 332: 330: 327: 325: 322: 320: 317: 315: 312: 310: 307: 306: 305: 304: 300: 299: 294: 291: 289: 286: 284: 281: 279: 276: 274: 271: 269: 266: 264: 261: 259: 258:Control chart 256: 254: 251: 249: 246: 244: 241: 239: 236: 234: 231: 229: 226: 224: 221: 219: 216: 214: 211: 209: 206: 204: 201: 200: 199: 198: 194: 193: 188: 185: 183: 180: 178: 175: 173: 170: 168: 165: 163: 160: 158: 155: 153: 150: 148: 145: 143: 140: 138: 137:Simon Wardley 135: 133: 130: 128: 125: 123: 120: 118: 115: 114: 113: 112: 108: 107: 102: 99: 97: 94: 92: 91:Data analysis 89: 87: 84: 82: 79: 77: 74: 72: 69: 67: 64: 62: 59: 57: 54: 53: 52: 51: 47: 46: 43: 40: 39: 36: 32: 31: 19: 8612:Preservation 8602:Philanthropy 8470: 8466:Augmentation 8405: 8391: 8374: 8366: 8351: 8337: 8323: 8309: 8295: 8251: 8213: 8195:Bibliography 8182:November 10, 8180:. Retrieved 8166: 8154:. Retrieved 8139: 8127:. Retrieved 8118: 8109: 8099:, retrieved 8077: 8068: 8041: 8035: 8025:, retrieved 8003: 7993: 7974: 7968: 7958:, retrieved 7928: 7918: 7893: 7887: 7844: 7840: 7830: 7820:, retrieved 7798: 7788: 7778:, retrieved 7756: 7746: 7701: 7697: 7687: 7677:, retrieved 7655: 7645: 7633: 7621: 7610:. Retrieved 7598: 7538: 7534: 7524: 7491: 7487: 7477: 7467:, retrieved 7445: 7436: 7426:, retrieved 7396: 7387: 7377:, retrieved 7364:11380/971504 7347: 7338: 7326: 7317: 7305: 7293: 7281: 7254: 7248: 7238:, retrieved 7208: 7199: 7187: 7168: 7164: 7154: 7137: 7131: 7093:(2): 92–94. 7090: 7086: 7076: 7064: 7054:, retrieved 7041: 7035: 7002: 6998: 6988: 6979: 6967: 6955: 6930: 6926: 6916: 6889: 6883: 6850: 6846: 6836: 6824: 6814:, retrieved 6792: 6783: 6773:, retrieved 6760: 6754: 6735: 6731: 6721: 6709: 6688:cite journal 6657: 6647:, retrieved 6625: 6615: 6608: 6593: 6585: 6577: 6561:. O'Reilly. 6558: 6552: 6528:, retrieved 6506: 6497: 6478: 6474: 6464: 6443:cite journal 6432:. Retrieved 6415: 6393:(4): 68–71. 6390: 6386: 6376: 6349: 6343: 6322:cite journal 6299: 6289:, retrieved 6267: 6254: 6221: 6217: 6207: 6196:. Retrieved 6192:the original 6182: 6161:cite journal 6150:. Retrieved 6133: 6122:. Retrieved 6110: 6098: 6062: 6055: 6027: 6020: 6010:, retrieved 5988: 5978: 5960: 5953: 5943:, retrieved 5921: 5911: 5894: 5884: 5849: 5840: 5829:. Retrieved 5812: 5802:, retrieved 5780: 5770: 5746:. Springer. 5743: 5737: 5713:, retrieved 5691: 5682: 5657: 5653: 5644: 5609: 5581: 5562: 5543: 5502: 5492: 5481:. Retrieved 5461: 5442: 5438: 5427: 5410: 5404: 5379: 5375: 5335: 5329: 5304: 5294: 5284:, retrieved 5262: 5252: 5226:False Memory 5225: 5219: 5186: 5176: 5151: 5147: 5138: 5117:cite journal 5106:. Retrieved 5089: 5056: 5052: 5042: 5032:, retrieved 5010: 5000: 4990:, retrieved 4968: 4959: 4940: 4936: 4926: 4907: 4903: 4893: 4872:cite journal 4861:. Retrieved 4844: 4833:. Retrieved 4813: 4802:. Retrieved 4782: 4771:. Retrieved 4759: 4750: 4731: 4727: 4717: 4698: 4694: 4684: 4663:cite journal 4652:. Retrieved 4635: 4614:cite journal 4603:. Retrieved 4586: 4559: 4553: 4543:, retrieved 4521: 4511: 4492: 4488: 4477: 4467:, retrieved 4445: 4435: 4414:cite journal 4381: 4345: 4339: 4318:cite journal 4293: 4268: 4264: 4254: 4244:, retrieved 4231: 4225: 4214:. Retrieved 4197:(3): 37–54. 4194: 4190: 4177: 4167:, retrieved 4145: 4136: 4095: 4091: 4081: 4056: 4052: 4042: 4032:, retrieved 4009: 4003: 3984: 3980: 3970: 3951: 3947: 3937: 3918: 3914: 3904: 3877: 3871: 3861:, retrieved 3839: 3829: 3802: 3796: 3775:cite journal 3753: 3728: 3724: 3715: 3696: 3692: 3682: 3670:. Retrieved 3658: 3645: 3624:cite journal 3613:. Retrieved 3593: 3566: 3560: 3515: 3511: 3501: 3491:November 12, 3489:. Retrieved 3431: 3427: 3417: 3405:. Retrieved 3391: 3364: 3358: 3331: 3297:cite journal 3273: 3246: 3240: 3230:, retrieved 3200: 3190: 3171: 3167: 3157: 3130: 3124: 3097: 3091: 3081:, retrieved 3059: 3050: 3027: 2993:. 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