Practical statistics for data scientists: 50+ essential concepts using R and Python
Gespeichert in:
| Hauptverfasser: | , , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
Mai 2020
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvi, 342 Seiten Illustrationen, Diagramme |
| ISBN: | 9781492072942 149207294X |
Internformat
MARC
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| adam_text | Table of Contents Preface..................................................................................................... xiii 1. Exploratory Data Analysis.............................................................................. 1 Elements of Structured Data Further Reading Rectangular Data Data Frames and Indexes Nonrectangular Data Structures Further Reading Estimates of Location Mean Median and Robust Estimates Example: Location Estimates of Population and Murder Rates Further Reading Estimates of Variability Standard Deviation and Related Estimates Estimates Based on Percentiles Example: Variability Estimates of State Population Further Reading Exploring the Data Distribution Percentiles and Boxplots Frequency Tables and Histograms Density Plots and Estimates Further Reading Exploring Binary and Categorical Data Mode Expected Value Probability 2 4 4 6 6 7 7 9 10 12 13 13 14 16 18 19 19 20 22 24 26 27 29 29 30 v
Further Reading Correlation Scatterplots Further Reading Exploring Two or More Variables Hexagonal Binning and Contours (Plotting Numeric Versus Numeric Data) Two Categorical Variables Categorical and Numeric Data Visualizing Multiple Variables Further Reading Summary 2. Data and Sampling Distributions.. 00000000000000 Random Sampling and Sample Bias Bias Random Selection Size Versus Quality: When Does Size Matter? Sample Mean Versus Population Mean Further Reading Selection Bias Regression to the Mean Further Reading Sampling Distribution of a Statistic Central Limit Theorem Standard Error Further Reading The Bootstrap Resampling Versus Bootstrapping Further Reading Confidence Intervals Further Reading Normal Distribution Standard Normal and QQ-Plots Long-Tailed Distributions Further Reading Students t-Distribution Further Reading Binomial Distribution Further Reading Chi-Square Distribution Further Reading F-Distribution vl I Table of Contents 30 30 34 36 36 36 39 41 43 46 46 47 48 50 51 52 53 53 54 55 57 57 60 60 61 61 65 65 65 68 69 71 73 75 75 78 78 80 80 81 82
82 82 83 84 84 85 86 86 Further Reading Poisson and Related Distributions Poisson Distributions Exponential Distribution Estimating the Failure Rate Weibull Distribution Further Reading Summary 3. Statistical Experiments and Significance Testing............................. ............... 87 88 90 91 92 93 94 95 95 96 96 97 98 102 102 103 103 106 107 109 109 110 110 112 112 116 116 118 118 121 123 124 124 A/В Testing Why Have a Control Group? Why Just A/В? Why Not C, D„..? Further Reading Hypothesis Tests The Null Hypothesis Alternative Hypothesis One-Way Versus Two-Way Hypothesis Tests Further Reading Resampling Permutation Test Example: Web Stickiness Exhaustive and Bootstrap Permutation Tests Permutation Tests: The Bottom Line for Data Science Further Reading Statistical Significance and p-Values p-Value Alpha Type 1 and Type 2 Errors Data Science and p-Values Further Reading t-Tests Further Reading Multiple Testing Further Reading Degrees of Freedom Further Reading ANOVA F-Statistic Two-Way ANOVA Further Reading Chi-Square Test Table of Contents | vii
Chi-Square Test: A Resampling Approach Chi-Square Test: Statistical Theory Fisher’s Exact Test Relevance for Data Science Further Reading Multi-Arm Bandit Algorithm Further Reading Power and Sample Size Sample Size Further Reading Summary 124 127 128 130 131 131 134 135 136 138 139 4. Regression and Prediction........................................................................ 141 Simple Linear Regression The Regression Equation Fitted Values and Residuals Least Squares Prediction Versus Explanation (Profiling) Further Reading Multiple Linear Regression Example: King County Housing Data Assessing the Model Cross-Validation Model Selection and Stepwise Regression Weighted Regression Further Reading Prediction Using Regression The Dangers of Extrapolation Confidence and Prediction Intervals Factor Variables in Regression Dummy Variables Representation Factor Variables with Many Levels Ordered Factor Variables Interpreting the Regression Equation Correlated Predictors Multicollinearity Confounding Variables Interactions and Main Effects Regression Diagnostics Outliers Influential Values Heteroskedasticity, Non-Normality, and Correlated Errors viii I Table of Contents 141 143 146 148 149 150 150 151 153 155 156 159 161 161 161 161 163 164 167 169 169 170 172 172 174 176 177 179 182
Partial Residual Plots and Nonlinearity Polynomial and Spline Regression Polynomial Splines Generalized Additive Models Further Reading Summary 185 187 188 189 192 193 194 5. Classification........................................................................................ 195 Naive Bayes Why Exact Bayesian Classification Is Impractical The Naive Solution Numeric Predictor Variables Further Reading Discriminant Analysis Covariance Matrix Fishers Linear Discriminant A Simple Example Further Reading Logistic Regression Logistic Response Function and Logit Logistic Regression and the GLM Generalized Linear Models Predicted Values from Logistic Regression Interpreting the Coefficients and Odds Ratios Linear and Logistic Regression: Similarities and Differences Assessing the Model Further Reading Evaluating Classification Models Confusion Matrix The Rare Class Problem Precision, Recall, and Specificity ROC Curve AUC Lift Further Reading Strategies for Imbalanced Data Undersampling Oversampling and Up/Down Weighting Data Generation Cost-Based Classification Exploring the Predictions 196 197 198 200 201 201 202 203 204 207 208 208 210 212 212 213 214 216 219 219 221 223 223 224 226 228 229 230 231 232 233 234 234 Table of Contents | ix
Further Reading Summary 236 236 6. Statistical Machine Learning. 000000000000000000 К-Nearest Neighbors A Small Example: Predicting Loan Default Distance Metrics One Hot Encoder Standardization (Normalization, z-Scores) Choosing К KNN as a Feature Engine Tree Models A Simple Example The Recursive Partitioning Algorithm Measuring Homogeneity or Impurity Stopping the Tree from Growing Predicting a Continuous Value How Trees Are Used Further Reading Bagging and the Random Forest Bagging Random Forest Variable Importance Hyperparameters Boosting The Boosting Algorithm XGBoost Regularization: Avoiding Overfitting Hyperparameters and Cross-Validation Summary 7. Unsupervised Learning............. 0000000000000000000000 Principal Components Analysis A Simple Example Computing the Principal Components Interpreting Principal Components Correspondence Analysis Further Reading К-Means Clustering A Simple Example К-Means Algorithm Interpreting the Clusters x I Table of Contents 237 238 239 241 242 243 246 247 249 250 252 254 256 257 258 259 259 260 261 265 269 270 271 272 274 279 282 283 284 285 288 289 292 294 294 295 298 299
Selecting the Number of Clusters Hierarchical Clustering A Simple Example The Dendrogram The Agglomerative Algorithm Measures of Dissimilarity Model-Based Clustering Multivariate Normal Distribution Mixtures of Normals Selecting the Number of Clusters Further Reading Scaling and Categorical Variables Scaling the Variables Dominant Variables Categorical Data and Gower’s Distance Problems with Clustering Mixed Data Summary 302 304 305 306 308 309 311 311 312 315 318 318 319 321 322 325 326 Bibliography........................................................................................ 327 Index.. 0000000000000000000000000000 329 Table of Contents | xi
|
| adam_txt |
Table of Contents Preface. xiii 1. Exploratory Data Analysis. 1 Elements of Structured Data Further Reading Rectangular Data Data Frames and Indexes Nonrectangular Data Structures Further Reading Estimates of Location Mean Median and Robust Estimates Example: Location Estimates of Population and Murder Rates Further Reading Estimates of Variability Standard Deviation and Related Estimates Estimates Based on Percentiles Example: Variability Estimates of State Population Further Reading Exploring the Data Distribution Percentiles and Boxplots Frequency Tables and Histograms Density Plots and Estimates Further Reading Exploring Binary and Categorical Data Mode Expected Value Probability 2 4 4 6 6 7 7 9 10 12 13 13 14 16 18 19 19 20 22 24 26 27 29 29 30 v
Further Reading Correlation Scatterplots Further Reading Exploring Two or More Variables Hexagonal Binning and Contours (Plotting Numeric Versus Numeric Data) Two Categorical Variables Categorical and Numeric Data Visualizing Multiple Variables Further Reading Summary 2. Data and Sampling Distributions. 00000000000000 Random Sampling and Sample Bias Bias Random Selection Size Versus Quality: When Does Size Matter? Sample Mean Versus Population Mean Further Reading Selection Bias Regression to the Mean Further Reading Sampling Distribution of a Statistic Central Limit Theorem Standard Error Further Reading The Bootstrap Resampling Versus Bootstrapping Further Reading Confidence Intervals Further Reading Normal Distribution Standard Normal and QQ-Plots Long-Tailed Distributions Further Reading Students t-Distribution Further Reading Binomial Distribution Further Reading Chi-Square Distribution Further Reading F-Distribution vl I Table of Contents 30 30 34 36 36 36 39 41 43 46 46 47 48 50 51 52 53 53 54 55 57 57 60 60 61 61 65 65 65 68 69 71 73 75 75 78 78 80 80 81 82
82 82 83 84 84 85 86 86 Further Reading Poisson and Related Distributions Poisson Distributions Exponential Distribution Estimating the Failure Rate Weibull Distribution Further Reading Summary 3. Statistical Experiments and Significance Testing. . 87 88 90 91 92 93 94 95 95 96 96 97 98 102 102 103 103 106 107 109 109 110 110 112 112 116 116 118 118 121 123 124 124 A/В Testing Why Have a Control Group? Why Just A/В? Why Not C, D„.? Further Reading Hypothesis Tests The Null Hypothesis Alternative Hypothesis One-Way Versus Two-Way Hypothesis Tests Further Reading Resampling Permutation Test Example: Web Stickiness Exhaustive and Bootstrap Permutation Tests Permutation Tests: The Bottom Line for Data Science Further Reading Statistical Significance and p-Values p-Value Alpha Type 1 and Type 2 Errors Data Science and p-Values Further Reading t-Tests Further Reading Multiple Testing Further Reading Degrees of Freedom Further Reading ANOVA F-Statistic Two-Way ANOVA Further Reading Chi-Square Test Table of Contents | vii
Chi-Square Test: A Resampling Approach Chi-Square Test: Statistical Theory Fisher’s Exact Test Relevance for Data Science Further Reading Multi-Arm Bandit Algorithm Further Reading Power and Sample Size Sample Size Further Reading Summary 124 127 128 130 131 131 134 135 136 138 139 4. Regression and Prediction. 141 Simple Linear Regression The Regression Equation Fitted Values and Residuals Least Squares Prediction Versus Explanation (Profiling) Further Reading Multiple Linear Regression Example: King County Housing Data Assessing the Model Cross-Validation Model Selection and Stepwise Regression Weighted Regression Further Reading Prediction Using Regression The Dangers of Extrapolation Confidence and Prediction Intervals Factor Variables in Regression Dummy Variables Representation Factor Variables with Many Levels Ordered Factor Variables Interpreting the Regression Equation Correlated Predictors Multicollinearity Confounding Variables Interactions and Main Effects Regression Diagnostics Outliers Influential Values Heteroskedasticity, Non-Normality, and Correlated Errors viii I Table of Contents 141 143 146 148 149 150 150 151 153 155 156 159 161 161 161 161 163 164 167 169 169 170 172 172 174 176 177 179 182
Partial Residual Plots and Nonlinearity Polynomial and Spline Regression Polynomial Splines Generalized Additive Models Further Reading Summary 185 187 188 189 192 193 194 5. Classification. 195 Naive Bayes Why Exact Bayesian Classification Is Impractical The Naive Solution Numeric Predictor Variables Further Reading Discriminant Analysis Covariance Matrix Fishers Linear Discriminant A Simple Example Further Reading Logistic Regression Logistic Response Function and Logit Logistic Regression and the GLM Generalized Linear Models Predicted Values from Logistic Regression Interpreting the Coefficients and Odds Ratios Linear and Logistic Regression: Similarities and Differences Assessing the Model Further Reading Evaluating Classification Models Confusion Matrix The Rare Class Problem Precision, Recall, and Specificity ROC Curve AUC Lift Further Reading Strategies for Imbalanced Data Undersampling Oversampling and Up/Down Weighting Data Generation Cost-Based Classification Exploring the Predictions 196 197 198 200 201 201 202 203 204 207 208 208 210 212 212 213 214 216 219 219 221 223 223 224 226 228 229 230 231 232 233 234 234 Table of Contents | ix
Further Reading Summary 236 236 6. Statistical Machine Learning. 000000000000000000 К-Nearest Neighbors A Small Example: Predicting Loan Default Distance Metrics One Hot Encoder Standardization (Normalization, z-Scores) Choosing К KNN as a Feature Engine Tree Models A Simple Example The Recursive Partitioning Algorithm Measuring Homogeneity or Impurity Stopping the Tree from Growing Predicting a Continuous Value How Trees Are Used Further Reading Bagging and the Random Forest Bagging Random Forest Variable Importance Hyperparameters Boosting The Boosting Algorithm XGBoost Regularization: Avoiding Overfitting Hyperparameters and Cross-Validation Summary 7. Unsupervised Learning. 0000000000000000000000 Principal Components Analysis A Simple Example Computing the Principal Components Interpreting Principal Components Correspondence Analysis Further Reading К-Means Clustering A Simple Example К-Means Algorithm Interpreting the Clusters x I Table of Contents 237 238 239 241 242 243 246 247 249 250 252 254 256 257 258 259 259 260 261 265 269 270 271 272 274 279 282 283 284 285 288 289 292 294 294 295 298 299
Selecting the Number of Clusters Hierarchical Clustering A Simple Example The Dendrogram The Agglomerative Algorithm Measures of Dissimilarity Model-Based Clustering Multivariate Normal Distribution Mixtures of Normals Selecting the Number of Clusters Further Reading Scaling and Categorical Variables Scaling the Variables Dominant Variables Categorical Data and Gower’s Distance Problems with Clustering Mixed Data Summary 302 304 305 306 308 309 311 311 312 315 318 318 319 321 322 325 326 Bibliography. 327 Index. 0000000000000000000000000000 329 Table of Contents | xi |
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| id | DE-604.BV046884098 |
| illustrated | Illustrated |
| index_date | 2024-07-03T15:19:02Z |
| indexdate | 2024-08-01T11:32:10Z |
| institution | BVB |
| isbn | 9781492072942 149207294X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032294042 |
| oclc_num | 1197703544 |
| open_access_boolean | |
| owner | DE-83 DE-N2 DE-384 DE-898 DE-BY-UBR DE-11 DE-706 DE-N32 DE-91G DE-BY-TUM DE-862 DE-BY-FWS DE-20 DE-860 DE-739 DE-473 DE-BY-UBG |
| owner_facet | DE-83 DE-N2 DE-384 DE-898 DE-BY-UBR DE-11 DE-706 DE-N32 DE-91G DE-BY-TUM DE-862 DE-BY-FWS DE-20 DE-860 DE-739 DE-473 DE-BY-UBG |
| physical | xvi, 342 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | O'Reilly |
| record_format | marc |
| spellingShingle | Bruce, Peter C. 1953- Bruce, Andrew Gedeck, Peter Practical statistics for data scientists 50+ essential concepts using R and Python Mathematical analysis / Statistical methods Quantitative research / Statistical methods Big data / Mathematics Data Science (DE-588)1140936166 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd R Programm (DE-588)4705956-4 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)1140936166 (DE-588)4802620-7 (DE-588)4123037-1 (DE-588)4056995-0 (DE-588)4428654-5 (DE-588)4705956-4 (DE-588)4434275-5 |
| title | Practical statistics for data scientists 50+ essential concepts using R and Python |
| title_auth | Practical statistics for data scientists 50+ essential concepts using R and Python |
| title_exact_search | Practical statistics for data scientists 50+ essential concepts using R and Python |
| title_exact_search_txtP | Practical statistics for data scientists 50+ essential concepts using R and Python |
| title_full | Practical statistics for data scientists 50+ essential concepts using R and Python Peter Bruce, Andrew Bruce, and Peter Gedeck |
| title_fullStr | Practical statistics for data scientists 50+ essential concepts using R and Python Peter Bruce, Andrew Bruce, and Peter Gedeck |
| title_full_unstemmed | Practical statistics for data scientists 50+ essential concepts using R and Python Peter Bruce, Andrew Bruce, and Peter Gedeck |
| title_short | Practical statistics for data scientists |
| title_sort | practical statistics for data scientists 50 essential concepts using r and python |
| title_sub | 50+ essential concepts using R and Python |
| topic | Mathematical analysis / Statistical methods Quantitative research / Statistical methods Big data / Mathematics Data Science (DE-588)1140936166 gnd Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd R Programm (DE-588)4705956-4 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Mathematical analysis / Statistical methods Quantitative research / Statistical methods Big data / Mathematics Data Science Big Data Datenanalyse Statistik Data Mining R Programm Python Programmiersprache |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032294042&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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Inhaltsverzeichnis
THWS Schweinfurt Zentralbibliothek Lesesaal
| Signatur: |
2000 ST 250 R01 B886(2) |
|---|---|
| Exemplar 1 | ausleihbar Verfügbar Bestellen |