Statistics for data scientists: an introduction to probability, statistics, and data analysis
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2022]
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| Ausgabe: | Corrected publication |
| Schriftenreihe: | Undergraduate topics in computer science
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxiv, 321 Seiten Illustrationen, Diagramme |
| ISBN: | 9783030105303 |
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| 245 | 1 | 0 | |a Statistics for data scientists |b an introduction to probability, statistics, and data analysis |c Maurits Kaptein, Edwin van den Heuvel |
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| 264 | 1 | |a Cham, Switzerland |b Springer |c [2022] | |
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Datensatz im Suchindex
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Contents A First Look at Data. 1.1 Overview and Learning Goals. 1.2 Getting Started with R . . 1.2.1 Opening a Dataset: f ace-data. csv. 1.2.2 Some Useful Commands for Exploring a Dataset . 1.2.3 Scalars, Vectors, Matrices, Data.frames, Objects. 1.3 Measurement Levels . 1.3.1 Outliers and Unrealistic Values . 1.4 Describing Data. 1.4.1 Frequency . 1.4.2 Central Tendency . 1.4.3 Dispersion, Skewness, and Kurtosis . 1.4.4 A Note on Aggregated Data . 1.5 Visualizing Data . 1.5.1 Describing Nominal/ordinal Variables . 1.5.2 Describing Interval/ratio Variables . 1.5.3 Relations Between Variables . 1.5.4 Multi-panel Plots . 1.5.5 Plotting Mathematical Functions
. 1.5.6 Frequently Used Arguments . 1.6 Other R Plotting Systems (And Installing Packages) . 1.6.1 Lattice . 1.6.2 GGplot2. Problems. References . 1 1 2 2 6 8 10 11 13 13 14 17 19 20 21 23 25 26 27 30 31 31 32 33 37 2 Sampling Plans and Estimates. 2.1 Introduction . 2.2 Definitions and Standard Terminology. 2.3 Non-representative Sampling. 2.3.1 Convenience Sampling . 39 39 41 44 44 1 xiii
xiv Contents 2.3,2 Haphazard Sampling . 2,3.3 Purposive Sampling . 2.4 Representative Sampling . 2.4.1 Simple Random Sampling. 2.4.2 Systematic Sampling . 2.4.3 Stratified Sampling. 2.4.4 Cluster Sampling . 2.5 Evaluating Estimators Given Different Sampling Plans. 2.5.1 Generic Formulation of Sampling Plans . 2.5.2 Bias, Standard Error, and Mean Squared Error . 2.5.3 Illustration of a Comparison of Sampling Plans . 2.5.4 Comparing Sampling Plans Using R. 2.6 Estimation of the Population Mean. 2.6.1 'Simple Random Sampling. 2.6.2 Systematic Sampling . 2.6.3 Stratified Sampling. 2.6.4 Cluster Sampling . 2.7 Estimation of the Population Proportion . 2.8 Estimation of the Population Variance . 2.8.1 Estimation
of the MSE . 2.9 Conclusions . Problems. References . 3 Probability Theory . 3.1 Introduction . 3.2 Definitions of Probability. 3.3 Probability Axioms . 3.3.1 Example: Using the Probability Axioms . 3.4 Conditional Probability . 3.4.1 Example: Using Conditional Probabilities . .*. 3.4.2 Computing Probabilities Using R . 3.5 Measures of Risk. 3.5.1 Risk Difference. 3.5.2 Relative Risk. 3.5.3 Odds Ratio. 3.5.4 Example: Using Risk Measures . 3.6 Sampling from Populations: Different Study Designs . 3.6.1 Cross-Sectional Study .
3.6.2 Cohort Study. 3.6.3 Case-Control Study . 3.7 Simpson’s Paradox . 3.8 Conclusion . Problems. References . 44 45 45 46 49 50 51 53 53 54 57 59 64 64 67 68 69 72 73 74 75 75 79 81 81 82 84 85 86 87 89 89 90 91 91 92 93 93 94 95 96 98 98 102
Contents 4 XV Random Variables and Distributions . Introduction . . Probability Density Functions . 4.2.1 Normal Density Function . 4.2.2 Lognormal Density Function . . 4.2.3 Uniform Density Function . 4.2.4 Exponential Density Function . 4.3 Distribution Functions and Continuous Random Variables. 4.4 Expected Values of Continuous Random Variables . 4.5 Distributions of Discrete Random Variables . 4.6 Expected Values of Discrete Random Variables . 4.7 Well-Known Discrete Distributions . 4.7.1 Bernoulli Probability Mass Function. . . 4.7.2 Binomial Probability Mass Function. 4.7.3 Poisson Probability Mass Function . 4.7.4 Negative Binomial Probability Mass Function. 4.7.5 Overview of Moments for Well-Known Discrete Distributions . 4.8 Working with Distributions in R . 4.8.1 R Built-In Functions . 4.8.2 Using Monte-Carlo Methods . 4.8.3 Obtaining Draws from Distributions: Inverse Transform
Sampling . 131 4.9 Relationships Between Distributions . 4.9.1 Binomial—Poisson . 4.9.2 Binomial—Normal. 4.10 Calculation Rules for Random Variables . 4.10.1 Rules for Single Random Variables. 4.10.2 Rules for Two Random Variables. 4.11 Conclusion . Problems. References . 4.1 4.2 5 Estimation. 5.1 5.2 5.3 5.4 Introduction . From Population Characteristics to Sample Statistics . 5.2.1 Population Characteristics. 5.2.2 Sample Statistics Under Simple Random Sampling . Distributions of Sample Statistic Tn . 5.3.1 Distribution of the Sample Maximum or Minimum . 5.3.2 Distribution of the Sample Average X . 5.3.3 Distribution of the Sample Variance S2. 5.3.4 The
Central Limit Theorem . . 5.3.5 Asymptotic Confidence Intervals. . . Normally Distributed Populations. 103 103 104 105 108 109 110 112 116 119 121 122 122 122 124 125 126 127 127 128 132 133 133 134 134 135 136 136 140 141 141 142 143 144 145 146 147 149 149 152 154
xvi 6 Contents 5.4.1 Confidence Intervals for Normal Populations . 5.4.2 Lognormally Distributed Populations . 5.5 Methods of Estimation . 5.5.1 Method of Moments. 5.5.2 Maximum Likelihood Estimation . Problems. Reference . 156 159 159 160 162 167 169 Multiple RandomVariables . . 6.1 Introduction . ;. . 6.2 Multivariate Distributions . 6.2.1 Definition of Independence. 6.2.2 Discrete Random Variables. 6.2.3 - ,Continuous Random Variables . 6.3 Constructing Bivariate Probability Distributions . 6.3.1 Using Sums of Random Variables . 6.3.2 Using the Farlie-Gumbel-Morgenstern Family of Distributions. 180 6.3.3 Using Mixtures of Probability Distributions. 6.3.4 Using the Fréchet Family of Distributions . 6.4 Properties of MultivariateDistributions .
6.4.1 Expectations . 6.4.2 Covariances. 6.5 Measures of Association . 6.5.1 Pearson’s Correlation Coefficient. 6.5.2 Kendall’s Tau Correlation . 6.5.3 Spearman’s Rho Correlation. 6.5.4 Cohen’s Kappa Statistic. 6.6 Estimators of Measures of Association . . 6.6.1 Pearson’s Correlation Coefficient. 6.6.2 Kendall’s Tau Correlation Coefficient . . *. 6.6.3 Spearman’s Rho Correlation Coefficient. 6.6.4 Should We Use Pearson’s Rho, Spearman’s Rho or Kendall’s Tau Correlation?. 6.6.5 Cohen’s Kappa Statistic. . . 6.6.6 Risk Difference, Relative Risk, and Odds Ratio . 6.7 Other Sample Statistics for Association. 6.7.1 Nominal Association Statistics. 6.7.2 Ordinal Association Statistics. 6.7.3 Binary Association Statistics . 6.8 Exploring Multiple Variables Using R . 6.8.1
Associations Between Continuous Variables . 6.8.2 Association Between Binary Variables . 6.8.3 Association Between Categorical Variables . 6.9 Conclusions . 171 171 172 173 174 177 179 179 181 183 183 184 186 191 191 195 196 197 199 199 202 204 207 209 211 213 213 217 219 223 223 226 232 235
xvii Contents 7 Problems. References . . 235 238 Making Decisions in Uncertainty . 241 241 242 243 7.1 7.2 Introduction . Bootstrapping. 7.2.1 The Basic Idea Behind the Bootstrap . 7:2.2 Applying the Bootstrap: The Non-parametric Bootstrap . 245 7.2.3 Applying the Bootstrap: The Parametric Bootstrap. 7.2.4 Applying the Bootstrap: Bootstrapping Massive Datasets . 248 7.2.5 A Critical Discussion of the Bootstrap . 7.3 Hypothesis Testing . 7.3.1 The One-Sided z-Test for a Single Mean . 7.3.2 The Two-Sided z-Test for a Single Mean . 7.3.3 Confidence Intervals and Hypothesis Testing. 7.3.4 The t-Tests for Means . 7.3.5 Non-parametric Tests for Medians. 7.3.6 Tests for Equality of Variation from Two Independent Samples. 269 7.3.7 Tests for Independence Between Two Variables . 7.3.8 Tests for Normality . 7.3.9 Tests for Outliers . 7.3.10 Equivalence Testing
. 7.4 Conclusions . Problems. References . 8 Bayesian Statistics. 8.1 8.2 8.3 8.4 Introduction . Bayes’ Theorem for Population Parameters . 8.2.1 Bayes’Law for Multiple Events. 8.2.2 Bayes’ Law for Competing Hypotheses . 8.2.3 Bayes’Law for Statistical Models. 8.2.4 The Fundamentals of Bayesian Data Analysis. Bayesian Data Analysis by Example . 8.3.1 Estimating the Parameter of a Bernoulli Population . 8.3.2 Estimating the Parameters of a Normal Population. 8.3.3 Bayesian Analysis for Normal Populations Based on Single Observation . 8.3.4 Bayesian Analysis for Normal Populations Based on Multiple Observations . 8.3.5 Bayesian Analysis for Normal Populations with Unknown Mean and Variance . Bayesian Decision-Making in Uncertainty . 247 251 251 253 256 258
259 263 271 274 276 280 282 283 285 287 287 288 290 290 291 292 293 293 295 296 298 299 301
xviii Contents 8.4.1 Providing Point Estimates of Parameters . 8.4.2 Providing Interval Estimates of the Parameters _ 8.4.3 Testing Hypotheses . 8.5 Challenges Involved in the Bayesian Approach . 8.5.1 Choosing a Prior. 8.5.2 Bayesian Computation. 8.6 Software for Bayesian Analysis . 8.6.1 A Simple Bernoulli Model Using Stan . 8.7 Bayesian and Frequentisi Thinking Compared . 8.8 Conclusion . Problems. References . Correction to: Statistics for Data Scientists. ЗОЇ 303 305 307 308 311 313 314 317 318 319 320 Cl |
| adam_txt |
Contents A First Look at Data. 1.1 Overview and Learning Goals. 1.2 Getting Started with R . . 1.2.1 Opening a Dataset: f ace-data. csv. 1.2.2 Some Useful Commands for Exploring a Dataset . 1.2.3 Scalars, Vectors, Matrices, Data.frames, Objects. 1.3 Measurement Levels . 1.3.1 Outliers and Unrealistic Values . 1.4 Describing Data. 1.4.1 Frequency . 1.4.2 Central Tendency . 1.4.3 Dispersion, Skewness, and Kurtosis . 1.4.4 A Note on Aggregated Data . 1.5 Visualizing Data . 1.5.1 Describing Nominal/ordinal Variables . 1.5.2 Describing Interval/ratio Variables . 1.5.3 Relations Between Variables . 1.5.4 Multi-panel Plots . 1.5.5 Plotting Mathematical Functions
. 1.5.6 Frequently Used Arguments . 1.6 Other R Plotting Systems (And Installing Packages) . 1.6.1 Lattice . 1.6.2 GGplot2. Problems. References . 1 1 2 2 6 8 10 11 13 13 14 17 19 20 21 23 25 26 27 30 31 31 32 33 37 2 Sampling Plans and Estimates. 2.1 Introduction . 2.2 Definitions and Standard Terminology. 2.3 Non-representative Sampling. 2.3.1 Convenience Sampling . 39 39 41 44 44 1 xiii
xiv Contents 2.3,2 Haphazard Sampling . 2,3.3 Purposive Sampling . 2.4 Representative Sampling . 2.4.1 Simple Random Sampling. 2.4.2 Systematic Sampling . 2.4.3 Stratified Sampling. 2.4.4 Cluster Sampling . 2.5 Evaluating Estimators Given Different Sampling Plans. 2.5.1 Generic Formulation of Sampling Plans . 2.5.2 Bias, Standard Error, and Mean Squared Error . 2.5.3 Illustration of a Comparison of Sampling Plans . 2.5.4 Comparing Sampling Plans Using R. 2.6 Estimation of the Population Mean. 2.6.1 'Simple Random Sampling. 2.6.2 Systematic Sampling . 2.6.3 Stratified Sampling. 2.6.4 Cluster Sampling . 2.7 Estimation of the Population Proportion . 2.8 Estimation of the Population Variance . 2.8.1 Estimation
of the MSE . 2.9 Conclusions . Problems. References . 3 Probability Theory . 3.1 Introduction . 3.2 Definitions of Probability. 3.3 Probability Axioms . 3.3.1 Example: Using the Probability Axioms . 3.4 Conditional Probability . 3.4.1 Example: Using Conditional Probabilities . .*. 3.4.2 Computing Probabilities Using R . 3.5 Measures of Risk. 3.5.1 Risk Difference. 3.5.2 Relative Risk. 3.5.3 Odds Ratio. 3.5.4 Example: Using Risk Measures . 3.6 Sampling from Populations: Different Study Designs . 3.6.1 Cross-Sectional Study .
3.6.2 Cohort Study. 3.6.3 Case-Control Study . 3.7 Simpson’s Paradox . 3.8 Conclusion . Problems. References . 44 45 45 46 49 50 51 53 53 54 57 59 64 64 67 68 69 72 73 74 75 75 79 81 81 82 84 85 86 87 89 89 90 91 91 92 93 93 94 95 96 98 98 102
Contents 4 XV Random Variables and Distributions . Introduction . . Probability Density Functions . 4.2.1 Normal Density Function . 4.2.2 Lognormal Density Function . . 4.2.3 Uniform Density Function . 4.2.4 Exponential Density Function . 4.3 Distribution Functions and Continuous Random Variables. 4.4 Expected Values of Continuous Random Variables . 4.5 Distributions of Discrete Random Variables . 4.6 Expected Values of Discrete Random Variables . 4.7 Well-Known Discrete Distributions . 4.7.1 Bernoulli Probability Mass Function. . . 4.7.2 Binomial Probability Mass Function. 4.7.3 Poisson Probability Mass Function . 4.7.4 Negative Binomial Probability Mass Function. 4.7.5 Overview of Moments for Well-Known Discrete Distributions . 4.8 Working with Distributions in R . 4.8.1 R Built-In Functions . 4.8.2 Using Monte-Carlo Methods . 4.8.3 Obtaining Draws from Distributions: Inverse Transform
Sampling . 131 4.9 Relationships Between Distributions . 4.9.1 Binomial—Poisson . 4.9.2 Binomial—Normal. 4.10 Calculation Rules for Random Variables . 4.10.1 Rules for Single Random Variables. 4.10.2 Rules for Two Random Variables. 4.11 Conclusion . Problems. References . 4.1 4.2 5 Estimation. 5.1 5.2 5.3 5.4 Introduction . From Population Characteristics to Sample Statistics . 5.2.1 Population Characteristics. 5.2.2 Sample Statistics Under Simple Random Sampling . Distributions of Sample Statistic Tn . 5.3.1 Distribution of the Sample Maximum or Minimum . 5.3.2 Distribution of the Sample Average X . 5.3.3 Distribution of the Sample Variance S2. 5.3.4 The
Central Limit Theorem . . 5.3.5 Asymptotic Confidence Intervals. . . Normally Distributed Populations. 103 103 104 105 108 109 110 112 116 119 121 122 122 122 124 125 126 127 127 128 132 133 133 134 134 135 136 136 140 141 141 142 143 144 145 146 147 149 149 152 154
xvi 6 Contents 5.4.1 Confidence Intervals for Normal Populations . 5.4.2 Lognormally Distributed Populations . 5.5 Methods of Estimation . 5.5.1 Method of Moments. 5.5.2 Maximum Likelihood Estimation . Problems. Reference . 156 159 159 160 162 167 169 Multiple RandomVariables . . 6.1 Introduction . ;. . 6.2 Multivariate Distributions . 6.2.1 Definition of Independence. 6.2.2 Discrete Random Variables. 6.2.3 - ,Continuous Random Variables . 6.3 Constructing Bivariate Probability Distributions . 6.3.1 Using Sums of Random Variables . 6.3.2 Using the Farlie-Gumbel-Morgenstern Family of Distributions. 180 6.3.3 Using Mixtures of Probability Distributions. 6.3.4 Using the Fréchet Family of Distributions . 6.4 Properties of MultivariateDistributions .
6.4.1 Expectations . 6.4.2 Covariances. 6.5 Measures of Association . 6.5.1 Pearson’s Correlation Coefficient. 6.5.2 Kendall’s Tau Correlation . 6.5.3 Spearman’s Rho Correlation. 6.5.4 Cohen’s Kappa Statistic. 6.6 Estimators of Measures of Association . . 6.6.1 Pearson’s Correlation Coefficient. 6.6.2 Kendall’s Tau Correlation Coefficient . . *. 6.6.3 Spearman’s Rho Correlation Coefficient. 6.6.4 Should We Use Pearson’s Rho, Spearman’s Rho or Kendall’s Tau Correlation?. 6.6.5 Cohen’s Kappa Statistic. . . 6.6.6 Risk Difference, Relative Risk, and Odds Ratio . 6.7 Other Sample Statistics for Association. 6.7.1 Nominal Association Statistics. 6.7.2 Ordinal Association Statistics. 6.7.3 Binary Association Statistics . 6.8 Exploring Multiple Variables Using R . 6.8.1
Associations Between Continuous Variables . 6.8.2 Association Between Binary Variables . 6.8.3 Association Between Categorical Variables . 6.9 Conclusions . 171 171 172 173 174 177 179 179 181 183 183 184 186 191 191 195 196 197 199 199 202 204 207 209 211 213 213 217 219 223 223 226 232 235
xvii Contents 7 Problems. References . . 235 238 Making Decisions in Uncertainty . 241 241 242 243 7.1 7.2 Introduction . Bootstrapping. 7.2.1 The Basic Idea Behind the Bootstrap . 7:2.2 Applying the Bootstrap: The Non-parametric Bootstrap . 245 7.2.3 Applying the Bootstrap: The Parametric Bootstrap. 7.2.4 Applying the Bootstrap: Bootstrapping Massive Datasets . 248 7.2.5 A Critical Discussion of the Bootstrap . 7.3 Hypothesis Testing . 7.3.1 The One-Sided z-Test for a Single Mean . 7.3.2 The Two-Sided z-Test for a Single Mean . 7.3.3 Confidence Intervals and Hypothesis Testing. 7.3.4 The t-Tests for Means . 7.3.5 Non-parametric Tests for Medians. 7.3.6 Tests for Equality of Variation from Two Independent Samples. 269 7.3.7 Tests for Independence Between Two Variables . 7.3.8 Tests for Normality . 7.3.9 Tests for Outliers . 7.3.10 Equivalence Testing
. 7.4 Conclusions . Problems. References . 8 Bayesian Statistics. 8.1 8.2 8.3 8.4 Introduction . Bayes’ Theorem for Population Parameters . 8.2.1 Bayes’Law for Multiple Events. 8.2.2 Bayes’ Law for Competing Hypotheses . 8.2.3 Bayes’Law for Statistical Models. 8.2.4 The Fundamentals of Bayesian Data Analysis. Bayesian Data Analysis by Example . 8.3.1 Estimating the Parameter of a Bernoulli Population . 8.3.2 Estimating the Parameters of a Normal Population. 8.3.3 Bayesian Analysis for Normal Populations Based on Single Observation . 8.3.4 Bayesian Analysis for Normal Populations Based on Multiple Observations . 8.3.5 Bayesian Analysis for Normal Populations with Unknown Mean and Variance . Bayesian Decision-Making in Uncertainty . 247 251 251 253 256 258
259 263 271 274 276 280 282 283 285 287 287 288 290 290 291 292 293 293 295 296 298 299 301
xviii Contents 8.4.1 Providing Point Estimates of Parameters . 8.4.2 Providing Interval Estimates of the Parameters _ 8.4.3 Testing Hypotheses . 8.5 Challenges Involved in the Bayesian Approach . 8.5.1 Choosing a Prior. 8.5.2 Bayesian Computation. 8.6 Software for Bayesian Analysis . 8.6.1 A Simple Bernoulli Model Using Stan . 8.7 Bayesian and Frequentisi Thinking Compared . 8.8 Conclusion . Problems. References . Correction to: Statistics for Data Scientists. ЗОЇ 303 305 307 308 311 313 314 317 318 319 320 Cl |
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| author | Kaptein, Maurits 1983- |
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| discipline | Informatik |
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| id | DE-604.BV048321696 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:12:15Z |
| indexdate | 2025-01-10T17:14:03Z |
| institution | BVB |
| isbn | 9783030105303 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033701022 |
| oclc_num | 1335404287 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-355 DE-BY-UBR |
| owner_facet | DE-91 DE-BY-TUM DE-355 DE-BY-UBR |
| physical | xxiv, 321 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| record_format | marc |
| series2 | Undergraduate topics in computer science |
| spelling | Kaptein, Maurits 1983- Verfasser (DE-588)1100728430 aut Statistics for data scientists an introduction to probability, statistics, and data analysis Maurits Kaptein, Edwin van den Heuvel Corrected publication Cham, Switzerland Springer [2022] © 2022 xxiv, 321 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Undergraduate topics in computer science Probability and Statistics in Computer Science Statistical Theory and Methods Probability Theory Computer science—Mathematics Mathematical statistics Statistics Probabilities R Programm (DE-588)4705956-4 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s Statistische Analyse (DE-588)4116599-8 s DE-604 Heuvel, Edwin van den 1967- Sonstige (DE-588)1219155187 oth Erscheint auch als Online-Ausgabe 978-3-030-10531-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033701022&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kaptein, Maurits 1983- Statistics for data scientists an introduction to probability, statistics, and data analysis Probability and Statistics in Computer Science Statistical Theory and Methods Probability Theory Computer science—Mathematics Mathematical statistics Statistics Probabilities R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4123037-1 (DE-588)4116599-8 |
| title | Statistics for data scientists an introduction to probability, statistics, and data analysis |
| title_auth | Statistics for data scientists an introduction to probability, statistics, and data analysis |
| title_exact_search | Statistics for data scientists an introduction to probability, statistics, and data analysis |
| title_exact_search_txtP | Statistics for data scientists an introduction to probability, statistics, and data analysis |
| title_full | Statistics for data scientists an introduction to probability, statistics, and data analysis Maurits Kaptein, Edwin van den Heuvel |
| title_fullStr | Statistics for data scientists an introduction to probability, statistics, and data analysis Maurits Kaptein, Edwin van den Heuvel |
| title_full_unstemmed | Statistics for data scientists an introduction to probability, statistics, and data analysis Maurits Kaptein, Edwin van den Heuvel |
| title_short | Statistics for data scientists |
| title_sort | statistics for data scientists an introduction to probability statistics and data analysis |
| title_sub | an introduction to probability, statistics, and data analysis |
| topic | Probability and Statistics in Computer Science Statistical Theory and Methods Probability Theory Computer science—Mathematics Mathematical statistics Statistics Probabilities R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd Statistische Analyse (DE-588)4116599-8 gnd |
| topic_facet | Probability and Statistics in Computer Science Statistical Theory and Methods Probability Theory Computer science—Mathematics Mathematical statistics Statistics Probabilities R Programm Datenanalyse Statistische Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033701022&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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