Practical statistics for data scientists: 50+ essential concepts using R and Python
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Beijing
O'Reilly
May 2020
|
| Ausgabe: | Second edition |
| Schlagwörter: | |
| Links: | https://ebookcentral.proquest.com/lib/th-ab/detail.action?docID=6173908 https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=6173908 https://ebookcentral.proquest.com/lib/uniregensburg-ebooks/detail.action?docID=6173908 https://ebookcentral.proquest.com/lib/erlangen/detail.action?docID=6173908 |
| Umfang: | 1 Online-Ressource (xvi, 342 Seiten) |
| ISBN: | 9781492072911 |
Internformat
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| 505 | 8 | |a Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Exploratory Data Analysis -- 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 | |
| 505 | 8 | |a 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 -- 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 | |
| 505 | 8 | |a Visualizing Multiple Variables -- Further Reading -- Summary -- Chapter 2. Data and Sampling Distributions -- 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 | |
| 505 | 8 | |a Standard Normal and QQ-Plots -- Long-Tailed Distributions -- Further Reading -- Student's t-Distribution -- Further Reading -- Binomial Distribution -- Further Reading -- Chi-Square Distribution -- Further Reading -- F-Distribution -- Further Reading -- Poisson and Related Distributions -- Poisson Distributions -- Exponential Distribution -- Estimating the Failure Rate -- Weibull Distribution -- Further Reading -- Summary -- Chapter 3. Statistical Experiments and Significance Testing -- A/B Testing -- Why Have a Control Group? -- Why Just A/B? Why Not C, D,...? -- Further Reading | |
| 505 | 8 | |a 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 | |
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Datensatz im Suchindex
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| adam_text | |
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| author | Bruce, Peter C. 1953- Bruce, Andrew Gedeck, Peter |
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| author_facet | Bruce, Peter C. 1953- Bruce, Andrew Gedeck, Peter |
| author_role | aut aut aut |
| author_sort | Bruce, Peter C. 1953- |
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| building | Verbundindex |
| bvnumber | BV046803637 |
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| contents | Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Exploratory Data Analysis -- 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 -- 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 -- Chapter 2. Data and Sampling Distributions -- 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 -- Student's t-Distribution -- Further Reading -- Binomial Distribution -- Further Reading -- Chi-Square Distribution -- Further Reading -- F-Distribution -- Further Reading -- Poisson and Related Distributions -- Poisson Distributions -- Exponential Distribution -- Estimating the Failure Rate -- Weibull Distribution -- Further Reading -- Summary -- Chapter 3. Statistical Experiments and Significance Testing -- A/B Testing -- Why Have a Control Group? -- Why Just A/B? 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 |
| ctrlnum | (OCoLC)1190674899 (DE-599)BVBBV046803637 |
| discipline | Informatik Mathematik |
| edition | Second edition |
| format | Electronic eBook |
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| id | DE-604.BV046803637 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-30T17:03:44Z |
| institution | BVB |
| isbn | 9781492072911 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032212322 |
| oclc_num | 1190674899 |
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| owner_facet | DE-1050 DE-1043 DE-355 DE-BY-UBR DE-29 |
| physical | 1 Online-Ressource (xvi, 342 Seiten) |
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| publishDateSort | 2020 |
| publisher | O'Reilly |
| record_format | marc |
| spelling | Bruce, Peter C. 1953- Verfasser (DE-588)1104275260 aut Practical statistics for data scientists 50+ essential concepts using R and Python Peter Bruce, Andrew Bruce, and Peter Gedeck Second edition Beijing O'Reilly May 2020 1 Online-Ressource (xvi, 342 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Exploratory Data Analysis -- 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 -- 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 -- Chapter 2. Data and Sampling Distributions -- 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 -- Student's t-Distribution -- Further Reading -- Binomial Distribution -- Further Reading -- Chi-Square Distribution -- Further Reading -- F-Distribution -- Further Reading -- Poisson and Related Distributions -- Poisson Distributions -- Exponential Distribution -- Estimating the Failure Rate -- Weibull Distribution -- Further Reading -- Summary -- Chapter 3. Statistical Experiments and Significance Testing -- A/B Testing -- Why Have a Control Group? -- Why Just A/B? 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 Mathematical analysis / Statistical methods Quantitative research / Statistical methods Big data / Mathematics Big Data (DE-588)4802620-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Data Science (DE-588)1140936166 s Big Data (DE-588)4802620-7 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s Python Programmiersprache (DE-588)4434275-5 s R Programm (DE-588)4705956-4 s DE-604 Data Mining (DE-588)4428654-5 s Bruce, Andrew Verfasser (DE-588)170744183 aut Gedeck, Peter Verfasser (DE-588)1231483199 aut Erscheint auch als Druck-Ausgabe 978-1-492-07294-2 |
| spellingShingle | Bruce, Peter C. 1953- Bruce, Andrew Gedeck, Peter Practical statistics for data scientists 50+ essential concepts using R and Python Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Exploratory Data Analysis -- 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 -- 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 -- Chapter 2. Data and Sampling Distributions -- 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 -- Student's t-Distribution -- Further Reading -- Binomial Distribution -- Further Reading -- Chi-Square Distribution -- Further Reading -- F-Distribution -- Further Reading -- Poisson and Related Distributions -- Poisson Distributions -- Exponential Distribution -- Estimating the Failure Rate -- Weibull Distribution -- Further Reading -- Summary -- Chapter 3. Statistical Experiments and Significance Testing -- A/B Testing -- Why Have a Control Group? -- Why Just A/B? 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 Mathematical analysis / Statistical methods Quantitative research / Statistical methods Big data / Mathematics Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd R Programm (DE-588)4705956-4 gnd Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4428654-5 (DE-588)4705956-4 (DE-588)4434275-5 (DE-588)4123037-1 (DE-588)1140936166 (DE-588)4056995-0 |
| 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_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 Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd R Programm (DE-588)4705956-4 gnd Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | Mathematical analysis / Statistical methods Quantitative research / Statistical methods Big data / Mathematics Big Data Data Mining R Programm Python Programmiersprache Datenanalyse Data Science Statistik |
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