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xi | |
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xv | |
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xvii | |
Acknowledgments |
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xix | |
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1 | (8) |
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1.1 Organization of the Book |
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6 | (3) |
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PART I R AND BASIC STATISTICS |
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9 | (208) |
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11 | (25) |
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11 | (11) |
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22 | (4) |
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2.3 Getting Your Data Into R |
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26 | (2) |
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2.4 Starting and Stopping R |
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28 | (1) |
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28 | (2) |
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30 | (1) |
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31 | (2) |
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2.8 Archaeological Data for Learning R |
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33 | (3) |
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3 Looking at Data - Numerical Summaries |
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36 | (29) |
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38 | (4) |
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3.2 Four Common Distributions |
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42 | (7) |
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3.3 Descriptive Statistics - Numeric |
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49 | (2) |
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3.4 Descriptive Statistics Using R |
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51 | (14) |
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4 Looking at Data - Tables |
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65 | (20) |
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65 | (3) |
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4.2 Producing Simple Tables in R |
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68 | (4) |
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4.3 More Than Two Variables |
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72 | (5) |
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4.4 Binning Numeric Variables |
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77 | (1) |
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4.5 Saving and Exporting Tables |
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78 | (7) |
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5 Looking at Data - Graphs |
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85 | (41) |
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86 | (2) |
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5.2 Plotting One or Two Categorical Variables |
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88 | (7) |
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5.3 One Numerical Variable |
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95 | (4) |
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5.4 One Numerical Variable and One Categorical Variable |
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99 | (4) |
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5.5 Two Numerical Variables |
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103 | (6) |
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5.6 More Than Two Numerical Variables |
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109 | (7) |
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116 | (10) |
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126 | (18) |
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6.1 The Apply Family of Functions in R |
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127 | (2) |
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6.2 Transforming Variables (Columns) |
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129 | (7) |
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6.3 Transforming Observations (Rows) |
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136 | (8) |
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144 | (15) |
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7.1 Missing Values and Other Special Values in R |
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145 | (2) |
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7.2 Eliminating Cases or Variables with Missing Values |
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147 | (3) |
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7.3 Imputing Missing Values |
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150 | (9) |
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8 Confidence Intervals and Hypothesis Testing |
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159 | (31) |
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8.1 Programming R - Writing Functions |
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160 | (2) |
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162 | (7) |
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169 | (2) |
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8.4 Comparing Two Samples |
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171 | (7) |
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8.5 Comparing More Than Two Samples |
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178 | (12) |
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190 | (27) |
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190 | (8) |
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9.2 Numeric Data - Association |
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198 | (6) |
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9.3 Numeric Data - Regression |
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204 | (13) |
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PART II MULTIVARIATE METHODS |
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217 | (130) |
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10 Multiple Regression and Generalized Linear Models |
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219 | (25) |
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219 | (13) |
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10.2 Regression with Dummy Variables |
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232 | (3) |
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10.3 Generalized Linear Models - Logistic Regression |
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235 | (9) |
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11 MANOVA and Discriminant Analysis |
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244 | (21) |
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11.1 Hotelling's T and MANOVA |
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245 | (4) |
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11.2 Descriptive (Canonical) Discriminant Analysis |
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249 | (6) |
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11.3 Predictive Discriminant Analysis |
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255 | (10) |
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12 Principal Components Analysis |
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265 | (14) |
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13 Correspondence Analysis |
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279 | (17) |
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296 | (22) |
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14.1 Distance, Dissimilarity, and Similarity |
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297 | (6) |
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14.2 Multidimensional Scaling |
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303 | (8) |
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14.3 Comparing Distance Matrices - Mantel Tests |
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311 | (7) |
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318 | (29) |
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15.1 K-Means Partitioning |
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321 | (13) |
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15.2 Hierarchical Clustering |
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334 | (8) |
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342 | (5) |
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PART III ARCHAEOLOGICAL APPROACHES TO DATA |
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347 | (68) |
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349 | (30) |
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16.1 Circular or Directional Statistics |
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349 | (9) |
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16.2 Mapping Quadrat-Based Data |
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358 | (9) |
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16.3 Mapping Piece Plot Data |
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367 | (5) |
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16.4 Simple Spatial Statistics |
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372 | (7) |
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379 | (18) |
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17.1 Distance Matrix Ordering |
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381 | (3) |
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17.2 Ordering the Data Matrix Directly |
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384 | (4) |
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17.3 Detrended Correspondence Analysis |
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388 | (2) |
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390 | (7) |
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397 | (15) |
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18.1 Diversity, Ubiquity, and Evenness |
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399 | (4) |
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18.2 Sample Size and Richness |
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403 | (5) |
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408 | (4) |
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412 | (3) |
References |
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415 | (8) |
Index |
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423 | |