Introduction to Data Analysis with R for Forensic Scientists (International Forensic Science and Investigation)

Contents......Page 12
1.2 What this book is not about......Page 24
1.3 How to read this book......Page 25
1.3.1 Examples and tutorials......Page 26
1.5.1 R is free......Page 27
1.5.4 R has a high-quality graphics system......Page 28
1.5.5 R allows you to share your analyses with others......Page 29
2.2 Introduction......Page 30
2.3.2 Types of variables......Page 31
2.4 Simple descriptive statistics......Page 32
2.4.2 The sample mean, standard deviation, and variance......Page 33
2.4.3 Order statistics, medians, quartiles, and quantiles......Page 35
2.5.1 An important question......Page 36
2.5.4 Two categorical variables......Page 37
2.5.5 Comparing groups......Page 40
2.5.6 Two quantitative variables......Page 46
2.6 Installing R on your computer......Page 50
2.7 Reading data into R......Page 51
2.7.1 read.csv......Page 52
2.7.2 scan and others......Page 54
2.8 The dafs package......Page 55
2.9.1 Three simple things......Page 56
2.9.2 R data types and manipulating R objects......Page 61
3.2.1 A little bit of language......Page 68
3.5 Drawing simple graphs......Page 69
3.5.3 Kernel density estimates......Page 70
3.5.4 Box plots......Page 73
3.5.6 Plotting categorical data......Page 74
3.5.7 One categorical and one continuous variable......Page 79
3.5.8 Two quantitative variables......Page 82
3.6.1 Legends......Page 83
3.6.2 Lines and smoothers......Page 84
3.6.3 Text and point highlighting......Page 85
3.6.5 Arrows, circles, and everything else......Page 86
3.7.1 Drawing bar plots......Page 87
3.7.2 Drawing histograms and kernel density estimates......Page 91
3.7.3 Drawing box plots......Page 93
3.7.4 Drawing scatter plots......Page 94
3.7.5 Getting your graph out of R and into another program......Page 96
3.8 Further reading......Page 99
4.2 Topics covered in this chapter......Page 102
4.4.1 Some concepts and notation......Page 103
4.4.2 The normal distribution......Page 105
4.4.3 Student's t-distribution......Page 108
4.4.4 The binomial distribution......Page 109
4.4.8 Distribution terminology......Page 110
4.5.1 Statistical inference......Page 111
4.5.2 A general framework for hypothesis tests......Page 112
4.5.3 Confidence intervals......Page 114
4.5.4 Statistically significant, significance level, significantly different, confidence, and other confusing phrases......Page 116
4.5.5 The two sample t-test......Page 117
4.5.6 The sampling distribution of the sample mean and other statistics......Page 121
4.5.7 The X2-test of independence......Page 125
4.6 Tutorial......Page 134
5.2 How to read this chapter......Page 140
5.3 Simple linear regression......Page 141
5.3.1 Example 5.1—Manganese and barium......Page 142
5.3.2 Example 5.2—DPD and age estimation......Page 144
5.3.3 Zero intercept models or regression through the origin......Page 151
5.3.4 Tutorial......Page 152
5.4.1 Example 5.3—Range of fire estimation......Page 156
5.4.2 Example 5.4—Elemental concentration in beer bottles......Page 163
5.4.3 Example 5.5—Age estimation from teeth......Page 165
5.4.5 Tutorial......Page 169
5.5 Calibration in the simple linear regression case......Page 174
5.5.1 Example 5.7—Calibration of RI measurements......Page 176
5.5.2 Example 5.8—Calibration in range of fire experiments......Page 178
5.5.3 Tutorial......Page 179
5.6 Regression with factors......Page 183
5.6.1 Example 5.9—Dummy variables in regression......Page 186
5.6.2 Example 5.10—Dummy variables in regression II......Page 187
5.6.3 A pitfall for the unwary......Page 189
5.6.4 Tutorial......Page 190
5.7 Linear models for grouped data—One-way ANOVA......Page 191
5.7.1 Example 5.11—RI differences......Page 193
5.7.2 Three procedures for multiple comparisons......Page 197
5.7.3 Dropping the assumption of equal variances......Page 203
5.7.4 Tutorial......Page 206
5.8 Two-way ANOVA......Page 216
5.8.1 The hypotheses for two-way ANOVA models......Page 218
5.8.2 Example 5.14—DNA left on drinking containers......Page 219
5.8.3 Tutorial......Page 224
5.9.1 The ANOVA identity......Page 231
6.2 How to read this chapter......Page 234
6.3 Introduction to GLMs......Page 235
6.4.1 Example 6.1—Glass fragments on the ground......Page 236
6.5 The negative binomial GLM......Page 242
6.5.1 Example 6.2—Over–dispersed data......Page 243
6.5.3 Example 6.4—Over-dispersion in car crash data......Page 246
6.5.4 Tutorial......Page 248
6.6 Logistic regression or the binomial GLM......Page 257
6.6.1 Example 6.5—Logistic regression for SIDS risks......Page 259
6.6.3 Example 6.6—Carbohydrate deficient transferrin as a predictor of alcohol abuse......Page 260
6.6.4 Example 6.7—Morphine concentration ratios as a predictor of acute morphine deaths......Page 263
6.6.5 Example 6.8—Risk factors for thoracic injuries......Page 265
6.6.6 Pitfalls for the unwary......Page 266
6.6.8 Tutorial......Page 268
6.7 Deviance......Page 276
7.1 Introduction......Page 280
7.3 What is an experiment?......Page 281
7.4.2 Response variables......Page 282
7.4.4 Experimental units......Page 283
7.5 The principles of experimental design......Page 284
7.5.2 Blocking......Page 285
7.7 Fixed and random effects......Page 286
7.8.1 Examples......Page 287
7.9.2 Data model for RCBDs......Page 295
7.9.3 Randomized block designs and repeated measures experiments......Page 304
7.10.2 2[sup(p)] factorial experiments......Page 314
7.11 Further reading......Page 315
Bibliography......Page 318