Statistics Slam Dunk, Video Edition

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Statistics Slam Dunk, Video Edition (Size: 2.7 GB)
  001. Chapter 1. Getting started.mp4 19.4 MB
  002. Chapter 1. Why R.mp4 30.7 MB
  003. Chapter 1. How this book works.mp4 25 MB
  004. Chapter 1. Summary.mp4 5.8 MB
  005. Chapter 2. Exploring data.mp4 18 MB
  006. Chapter 2. Importing data.mp4 6.2 MB
  007. Chapter 2. Wrangling data.mp4 29.5 MB
  008. Chapter 2. Variable breakdown.mp4 21.1 MB
  009. Chapter 2. Exploratory data analysis.mp4 110.3 MB
  010. Chapter 2. Writing data.mp4 1.7 MB
  011. Chapter 2. Summary.mp4 7.4 MB
  012. Chapter 3. Segmentation analysis.mp4 15.4 MB
  013. Chapter 3. Loading packages.mp4 4.6 MB
  014. Chapter 3. Importing and viewing data.mp4 4.6 MB
  015. Chapter 3. Creating another derived variable.mp4 8.6 MB
  016. Chapter 3. Visualizing means and medians.mp4 44.8 MB
  017. Chapter 3. Preliminary conclusions.mp4 9.4 MB
  018. Chapter 3. Sankey diagram.mp4 32.4 MB
  019. Chapter 3. Expected value analysis.mp4 25 MB
  020. Chapter 3. Hierarchical clustering.mp4 33.1 MB
  021. Chapter 3. Summary.mp4 8 MB
  022. Chapter 4. Constrained optimization.mp4 13.9 MB
  023. Chapter 4. Loading packages.mp4 5.7 MB
  024. Chapter 4. Importing data.mp4 2.7 MB
  025. Chapter 4. Knowing the data.mp4 8.9 MB
  026. Chapter 4. Visualizing the data.mp4 35.2 MB
  027. Chapter 4. Constrained optimization setup.mp4 13.1 MB
  028. Chapter 4. Constrained optimization construction.mp4 9.7 MB
  029. Chapter 4. Results.mp4 11.2 MB
  030. Chapter 4. Summary.mp4 7.5 MB
  031. Chapter 5. Regression models.mp4 18.1 MB
  032. Chapter 5. Importing data.mp4 2.2 MB
  033. Chapter 5. Knowing the data.mp4 13.7 MB
  034. Chapter 5. Identifying outliers.mp4 27.4 MB
  035. Chapter 5. Checking for normality.mp4 17.8 MB
  036. Chapter 5. Visualizing and testing correlations.mp4 14.1 MB
  037. Chapter 5. Multiple linear regression.mp4 67.5 MB
  038. Chapter 5. Regression tree.mp4 17 MB
  039. Chapter 5. Summary.mp4 10.9 MB
  040. Chapter 6. More wrangling and visualizing data.mp4 7 MB
  041. Chapter 6. Importing data.mp4 3.7 MB
  042. Chapter 6. Wrangling data.mp4 25.5 MB
  043. Chapter 6. Analysis.mp4 62.6 MB
  044. Chapter 6. Summary.mp4 5.7 MB
  045. Chapter 7. T-testing and effect size testing.mp4 12.8 MB
  046. Chapter 7. Importing data.mp4 853.1 KB
  047. Chapter 7. Wrangling data.mp4 9.6 MB
  048. Chapter 7. Analysis on 2018-19 data.mp4 60.5 MB
  049. Chapter 7. Analysis on 2019-20 data.mp4 27.2 MB
  050. Chapter 7. Summary.mp4 9.2 MB
  051. Chapter 8. Optimal stopping.mp4 9.6 MB
  052. Chapter 8. Importing images.mp4 3.4 MB
  053. Chapter 8. Importing and viewing data.mp4 5.4 MB
  054. Chapter 8. Exploring and wrangling data.mp4 26.9 MB
  055. Chapter 8. Analysis.mp4 74.9 MB
  056. Chapter 8. Summary.mp4 9.7 MB
  057. Chapter 9. Chi-square testing and more effect size testing.mp4 18.7 MB
  058. Chapter 9. Importing data.mp4 2.4 MB
  059. Chapter 9. Wrangling data.mp4 13.5 MB
  060. Chapter 9. Computing permutations.mp4 11.8 MB
  061. Chapter 9. Visualizing results.mp4 22.4 MB
  062. Chapter 9. Statistical test of significance.mp4 22.1 MB
  063. Chapter 9. Effect size testing.mp4 4.8 MB
  064. Chapter 9. Summary.mp4 6.1 MB
  065. Chapter 10. Doing more with ggplot2.mp4 8.4 MB
  066. Chapter 10. Importing and viewing data.mp4 12 MB
  067. Chapter 10. Salaries and salary cap analysis.mp4 23.1 MB
  068. Chapter 10. Analysis.mp4 93.4 MB
  069. Chapter 10. Summary.mp4 11.6 MB
  070. Chapter 11. K-means clustering.mp4 10.4 MB
  071. Chapter 11. Importing data.mp4 6.3 MB
  072. Chapter 11. A primer on standard deviations and z-scores.mp4 14.4 MB
  073. Chapter 11. Analysis.mp4 47.2 MB
  074. Chapter 11. K-means clustering.mp4 59.6 MB
  075. Chapter 11. Summary.mp4 8 MB
  076. Chapter 12. Computing and plotting inequality.mp4 17.6 MB
  077. Chapter 12. Loading packages.mp4 2.1 MB
  078. Chapter 12. Importing and viewing data.mp4 15 MB
  079. Chapter 12. Wrangling data.mp4 23 MB
  080. Chapter 12. Gini coefficients.mp4 13.7 MB
  081. Chapter 12. Lorenz curves.mp4 14.5 MB
  082. Chapter 12. Salary inequality and championships.mp4 37.6 MB
  083. Chapter 12. Salary inequality and wins and losses.mp4 14 MB
  084. Chapter 12. Gini coefficient bands versus winning percentage.mp4 9.1 MB
  085. Chapter 12. Summary.mp4 8.7 MB
  086. Chapter 13. More with Gini coefficients and Lorenz curves.mp4 14.3 MB
  087. Chapter 13. Importing and viewing data.mp4 2.8 MB
  088. Chapter 13. Wrangling data.mp4 19.1 MB
  089. Chapter 13. Gini coefficients.mp4 15.2 MB
  090. Chapter 13. Lorenz curves.mp4 16.6 MB
  091. Chapter 13. For loops.mp4 21.5 MB
  092. Chapter 13. User-defined functions.mp4 17.7 MB
  093. Chapter 13. Win share inequality and championships.mp4 56 MB
  094. Chapter 13. Win share inequality and wins and losses.mp4 7.6 MB
  095. Chapter 13. Gini coefficient bands versus winning percentage.mp4 12.4 MB
  096. Chapter 13. Summary.mp4 10 MB
  097. Chapter 14. Intermediate and advanced modeling.mp4 10.1 MB
  098. Chapter 14. Importing and wrangling data.mp4 40.7 MB
  099. Chapter 14. Exploring data.mp4 9.3 MB
  100. Chapter 14. Correlations.mp4 27.9 MB
  101. Chapter 14. Analysis of variance models.mp4 35.2 MB
  102. Chapter 14. Logistic regressions.mp4 53.1 MB
  103. Chapter 14. Paired data before and after.mp4 11.2 MB
  104. Chapter 14. Summary.mp4 9.6 MB
  105. Chapter 15. The Lindy effect.mp4 15.1 MB
  106. Chapter 15. Importing and viewing data.mp4 21.1 MB
  107. Chapter 15. Visualizing data.mp4 25.8 MB
  108. Chapter 15. Pareto charts.mp4 29.6 MB
  109. Chapter 15. Summary.mp4 4.3 MB
  110. Chapter 16. Randomness versus causality.mp4 12.2 MB
  111. Chapter 16. Importing and wrangling data.mp4 19.8 MB
  112. Chapter 16. Rule of succession and the hot hand.mp4 24.7 MB
  113. Chapter 16. Player-level analysis.mp4 35 MB
  114. Chapter 16. League-wide analysis.mp4 10 MB
  115. Chapter 16. Summary.mp4 7.4 MB
  116. Chapter 17. Collective intelligence.mp4 11.7 MB
  117. Chapter 17. Importing data.mp4 2.1 MB
  118. Chapter 17. Wrangling data.mp4 19.1 MB
  119. Chapter 17. Automated exploratory data analysis.mp4 79.4 MB
  120. Chapter 17. Results.mp4 45.1 MB
  121. Chapter 17. Summary.mp4 6.9 MB
  122. Chapter 18. Statistical dispersion methods.mp4 10.1 MB
  123. Chapter 18. Importing data.mp4 2.3 MB
  124. Chapter 18. Exploring and wrangling data.mp4 18.3 MB
  125. Chapter 18. Measures of statistical dispersion and intra-season parity.mp4 41.4 MB
  126. Chapter 18. Churn and inter-season parity.mp4 22.4 MB
  127. Chapter 18. Summary.mp4 3.3 MB
  128. Chapter 19. Data standardization.mp4 14.5 MB
  129. Chapter 19. Importing and viewing data.mp4 9.2 MB
  130. Chapter 19. Wrangling data.mp4 23.3 MB
  131. Chapter 19. Standardizing data.mp4 37.9 MB
  132. Chapter 19. Summary.mp4 5.4 MB
  133. Chapter 20. Finishing up.mp4 26.1 MB
  134. Chapter 20. Significance testing.mp4 26.2 MB
  135. Chapter 20. Effect size testing.mp4 19.1 MB
  136. Chapter 20. Modeling.mp4 18.4 MB
  137. Chapter 20. Operations research.mp4 26.3 MB
  138. Chapter 20. Probability.mp4 13.6 MB
  139. Chapter 20. Statistical dispersion.mp4 9.8 MB
  140. Chapter 20. Standardization.mp4 8.7 MB
  141. Chapter 20. Summary statistics and visualization.mp4 24.2 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 143 total files

Description


Statistics Slam Dunk, Video Edition

https://FreeCourseWeb.com

Released 1/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 17h 49m | Size: 2.71 GB

Learn statistics by analyzing professional basketball data! In this action-packed book, you’ll build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language.

Statistics Slam Dunk is an engaging how-to guide for statistical analysis with R. Each chapter contains an end-to-end data science or statistics project delving into NBA data and revealing real-world sporting insights. Written by a former basketball player turned business intelligence and analytics leader, you’ll get practical experience tidying, wrangling, exploring, testing, modeling, and otherwise analyzing data with the best and latest R packages and functions.

In Statistics Slam Dunk you’ll develop a toolbox of R programming skills including