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Dot plot (statistics)

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chart consisting of data points plotted on a fairly simple scale, typically using filled in circles. There are two common, yet very different, versions of the dot chart. The first has been used in hand-drawn (pre-computer era) graphs to depict distributions going back to 1884. The other version is
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is also used in the area of process mapping. This is a simplified flowchart process flow chart in which columns are tasks, rows are roles, and dots that are inserted at the intersection of tasks and roles represent a sequence of steps. In other words, it is an extensive RACI table with additional
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Dot plot may also refer to plots of points that each belong to one of several categories. They are an alternative to bar charts or pie charts, and look somewhat like a horizontal bar chart where the bars are replaced by dots at the values associated with each category. Compared to (vertical) bar
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charts and pie charts, Cleveland argues that dot plots allow more accurate interpretation of the graph by readers by making the labels easier to read, reducing non-data ink (or graph clutter) and supporting table look-up.
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may be more efficient, as dot plots may become too cluttered after this point. Dot plots may be distinguished from histograms in that dots are not spaced uniformly along the horizontal axis.
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Although the plot appears to be simple, its computation and the statistical theory underlying it are not simple. The algorithm for computing a dot plot is closely related to
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Dot plots are one of the simplest statistical plots, and are suitable for small to moderate sized data sets. They are useful for highlighting clusters and gaps, as well as
90:. The size chosen for the dots affects the appearance of the plot. Choice of dot size is equivalent to choosing the bandwidth for a kernel density estimate. 71:. Their other advantage is the conservation of numerical information. When dealing with larger data sets (around 20–30 or more data points) the related 36:
as an alternative to the bar chart, in which dots are used to depict the quantitative values (e.g. counts) associated with categorical variables.
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The dot plot as a representation of a distribution consists of group of data points plotted on a simple scale. Dot plots are used for
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Example of a Cleveland dot plot, showing the death rates per 1000 in Virginia in 1940
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Chance Encounters: A First Course in Data Analysis and Inference
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programming language this type of plot is also referred to as a
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data. Data points may be labelled if there are few of them.
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information about the sequence of steps in the process.
210: 335: 179:(3). American Statistical Association: 276–281. 127: 329:Dot Plots: A Useful Alternative to Bar Charts 273: 48:A dot plot of 50 random values from 0 to 9. 245: 205: 170: 114: 43: 171:Wilkinson, Leland (1999). "Dot plots". 336: 110: 39: 301: 13: 147:Data and information visualization 14: 355: 322: 344:Statistical charts and diagrams 307:Wild, C. and Seber, G. (2000) 267: 251:Introductory Statistics with R 239: 199: 164: 1: 157: 128:Dot chart in process mapping 16:Type of bar chart using dots 7: 140: 10: 362: 283:. Chapman & Hall/CRC. 173:The American Statistician 88:kernel density estimation 223:2027/mdp.39015026891187 120: 49: 311:John Wiley and Sons. 207:Cleveland, William S. 118: 47: 34:William S. Cleveland 111:Cleveland dot plots 121: 50: 40:Of a distribution 351: 302:Other references 295: 294: 271: 265: 264: 243: 237: 236: 217:. Hobart Press. 216: 213:Visualizing Data 203: 197: 196: 168: 361: 360: 354: 353: 352: 350: 349: 348: 334: 333: 325: 304: 299: 298: 291: 272: 268: 261: 244: 240: 233: 204: 200: 185:10.2307/2686111 169: 165: 160: 143: 130: 113: 42: 17: 12: 11: 5: 359: 358: 347: 346: 332: 331: 324: 323:External links 321: 320: 319: 303: 300: 297: 296: 289: 266: 259: 247:Peter Dalgaard 238: 231: 198: 162: 161: 159: 156: 155: 154: 149: 142: 139: 129: 126: 112: 109: 41: 38: 15: 9: 6: 4: 3: 2: 357: 356: 345: 342: 341: 339: 330: 327: 326: 318: 317:0-471-32936-3 314: 310: 306: 305: 292: 290:1-58488-486-X 286: 282: 281: 276: 270: 262: 260:0-387-95475-9 256: 252: 248: 242: 234: 232:0-9634884-0-6 228: 224: 220: 215: 214: 208: 202: 194: 190: 186: 182: 178: 174: 167: 163: 153: 150: 148: 145: 144: 138: 135: 125: 117: 108: 106: 102: 98: 97: 91: 89: 84: 82: 78: 74: 70: 65: 63: 59: 55: 46: 37: 35: 32:described by 30: 26: 22: 308: 279: 275:Paul Murrell 269: 253:. Springer. 250: 241: 212: 201: 176: 172: 166: 152:Scatter plot 133: 131: 122: 104: 100: 94: 92: 85: 66: 58:quantitative 51: 24: 20: 18: 29:statistical 280:R Graphics 158:References 101:stripchart 62:univariate 54:continuous 134:dot chart 132:The term 105:stripplot 81:histogram 21:dot chart 338:Category 277:(2005). 209:(1993). 141:See also 77:box plot 73:stemplot 69:outliers 25:dot plot 193:2686111 93:In the 315:  287:  257:  229:  191:  189:JSTOR 27:is a 313:ISBN 285:ISBN 255:ISBN 227:ISBN 219:hdl 181:doi 103:or 79:or 23:or 340:: 249:. 225:. 187:. 177:53 175:. 107:. 75:, 60:, 56:, 19:A 293:. 263:. 235:. 221:: 195:. 183:: 96:R

Index

statistical
William S. Cleveland

continuous
quantitative
univariate
outliers
stemplot
box plot
histogram
kernel density estimation
R

Data and information visualization
Scatter plot
doi
10.2307/2686111
JSTOR
2686111
Cleveland, William S.
Visualizing Data
hdl
2027/mdp.39015026891187
ISBN
0-9634884-0-6
Peter Dalgaard
ISBN
0-387-95475-9
Paul Murrell
R Graphics

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