This is a modern textbook in statistical inference, using the principles of data science through R and the Tidyverse. It assumes minimal background knowledge of the reader: there is no algebra, no calculus, and no prior programming/coding experience.
This is a modern textbook in statistical inference, using the principles of data science through R and the Tidyverse. It assumes minimal background knowledge of the reader: there is no algebra, no calculus, and no prior programming/coding experience.
¿ Chester Ismay is a Data Science Evangelist for DataRobot and is based in Portland, Oregon, USA. ¿Albert Y. Kim is an Assistant Professor of Statistical and Data Sciences at Smith College in Northampton, Massachusetts, USA.
Inhaltsangabe
Preface 1 Getting Started with Data in R I Data Science via the tidyverse 2 Data Visualization 3 Data Wrangling 4 Data Importing & "Tidy" Data II Data Modeling via moderndive 5 Basic Regression 6 Multiple Regression III Statistical Inference via infer 7 Sampling 8 Bootstrapping & Confidence Intervals 9 Hypothesis Testing 10 Inference for Regression 11 Tell the Story with Data Appendix A Statistical Background B Information about R packages Used Bibliography Index
Preface 1 Getting Started with Data in R I Data Science via the tidyverse 2 Data Visualization 3 Data Wrangling 4 Data Importing & "Tidy" Data II Data Modeling via moderndive 5 Basic Regression 6 Multiple Regression III Statistical Inference via infer 7 Sampling 8 Bootstrapping & Confidence Intervals 9 Hypothesis Testing 10 Inference for Regression 11 Tell the Story with Data Appendix A Statistical Background B Information about R packages Used Bibliography Index
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