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[] Udemy - Statistics for Data Science and Business Analysis

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文件列表

  1. 5. Practical example descriptive statistics/1. Practical example.mp4 160MB
  2. 17. Practical example regression analysis/1. Practical example regression analysis.mp4 129MB
  3. 9. Practical example inferential statistics/1. Practical example inferential statistics.mp4 103MB
  4. 10. Hypothesis testing Introduction/1. The null and the alternative hypothesis.mp4 92MB
  5. 10. Hypothesis testing Introduction/4. Establishing a rejection region and a significance level.mp4 83MB
  6. 7. Estimators and estimates/5. Calculating confidence intervals within a population with a known variance.mp4 78MB
  7. 3. The fundamentals of descriptive statistics/1. The various types of data we can work with.mp4 73MB
  8. 8. Confidence intervals advanced topics/1. Calculating confidence intervals for two means with dependent samples.mp4 70MB
  9. 12. Practical example hypothesis testing/1. Practical example hypothesis testing.mp4 69MB
  10. 1. Introduction/1. What does the course cover.mp4 69MB
  11. 6. Distributions/9. Understanding the central limit theorem.mp4 63MB
  12. 6. Distributions/2. What is a distribution.mp4 62MB
  13. 2. Sample or population data/1. Understanding the difference between a population and a sample.mp4 58MB
  14. 7. Estimators and estimates/7. Confidence interval clarifications.mp4 57MB
  15. 11. Hypothesis testing Let's start testing!/3. What is the p-value and why is it one of the most useful tools for statisticians.mp4 56MB
  16. 3. The fundamentals of descriptive statistics/3. Levels of measurement.mp4 54MB
  17. 11. Hypothesis testing Let's start testing!/1. Test for the mean. Population variance known.mp4 54MB
  18. 13. The fundamentals of regression analysis/5. The linear regression model made easy.mp4 51MB
  19. 4. Measures of central tendency, asymmetry, and variability/6. Measuring how data is spread out calculating variance.mp4 51MB
  20. 11. Hypothesis testing Let's start testing!/7. Test for the mean. Dependent samples.mp4 50MB
  21. 7. Estimators and estimates/3. Confidence intervals - an invaluable tool for decision making.mp4 50MB
  22. 6. Distributions/4. The Normal distribution.mp4 50MB
  23. 7. Estimators and estimates/1. Working with estimators and estimates.mp4 48MB
  24. 7. Estimators and estimates/12. What is a margin of error and why is it important in Statistics.mp4 47MB
  25. 13. The fundamentals of regression analysis/11. A practical example - Reinforced learning.mp4 46MB
  26. 4. Measures of central tendency, asymmetry, and variability/8. Standard deviation and coefficient of variation.mp4 45MB
  27. 10. Hypothesis testing Introduction/6. Type I error vs Type II error.mp4 44MB
  28. 14. Subtleties of regression analysis/12. The adjusted R-squared.mp4 44MB
  29. 14. Subtleties of regression analysis/1. Decomposing the linear regression model - understanding its nuts and bolts.mp4 42MB
  30. 11. Hypothesis testing Let's start testing!/5. Test for the mean. Population variance unknown.mp4 40MB
  31. 15. Assumptions for linear regression analysis/7. A3. Normality and homoscedasticity.mp4 40MB
  32. 3. The fundamentals of descriptive statistics/14. Cross tables and scatter plots.mp4 40MB
  33. 16. Dealing with categorical data/1. Dummy variables.mp4 38MB
  34. 4. Measures of central tendency, asymmetry, and variability/1. The main measures of central tendency mean, median and mode.mp4 37MB
  35. 14. Subtleties of regression analysis/7. Studying regression tables.mp4 37MB
  36. 3. The fundamentals of descriptive statistics/5. Categorical variables. Visualization techniques for categorical variables.mp4 37MB
  37. 14. Subtleties of regression analysis/3. What is R-squared and how does it help us.mp4 36MB
  38. 11. Hypothesis testing Let's start testing!/11. Test for the mean. Independent samples (Part 2).mp4 36MB
  39. 7. Estimators and estimates/8. Student's T distribution.mp4 35MB
  40. 15. Assumptions for linear regression analysis/5. A2. No endogeneity.mp4 32MB
  41. 7. Estimators and estimates/10. Calculating confidence intervals within a population with an unknown variance.mp4 32MB
  42. 11. Hypothesis testing Let's start testing!/9. Test for the mean. Independent samples (Part 1).mp4 30MB
  43. 4. Measures of central tendency, asymmetry, and variability/13. The correlation coefficient.mp4 29MB
  44. 8. Confidence intervals advanced topics/3. Calculating confidence intervals for two means with independent samples (part 1).mp4 29MB
  45. 4. Measures of central tendency, asymmetry, and variability/11. Calculating and understanding covariance.mp4 27MB
  46. 8. Confidence intervals advanced topics/5. Calculating confidence intervals for two means with independent samples (part 2).mp4 27MB
  47. 15. Assumptions for linear regression analysis/11. A5. No multicollinearity.mp4 27MB
  48. 15. Assumptions for linear regression analysis/9. A4. No autocorrelation.mp4 26MB
  49. 3. The fundamentals of descriptive statistics/8. Numerical variables. Using a frequency distribution table.mp4 26MB
  50. 13. The fundamentals of regression analysis/3. Correlation and causation.mp4 26MB
  51. 6. Distributions/11. Standard error.mp4 23MB
  52. 6. Distributions/6. The standard normal distribution.mp4 22MB
  53. 14. Subtleties of regression analysis/5. The ordinary least squares setting and its practical applications.mp4 20MB
  54. 8. Confidence intervals advanced topics/7. Calculating confidence intervals for two means with independent samples (part 3).mp4 20MB
  55. 4. Measures of central tendency, asymmetry, and variability/3. Measuring skewness.mp4 19MB
  56. 13. The fundamentals of regression analysis/1. Introduction to regression analysis.mp4 19MB
  57. 15. Assumptions for linear regression analysis/1. OLS assumptions.mp4 19MB
  58. 14. Subtleties of regression analysis/10. The multiple linear regression model.mp4 19MB
  59. 6. Distributions/1. Introduction to inferential statistics.mp4 15MB
  60. 14. Subtleties of regression analysis/14. What does the F-statistic show us and why do we need to understand it.mp4 14MB
  61. 3. The fundamentals of descriptive statistics/11. Histogram charts.mp4 14MB
  62. 13. The fundamentals of regression analysis/7. What is the difference between correlation and regression.mp4 13MB
  63. 15. Assumptions for linear regression analysis/3. A1. Linearity.mp4 12MB
  64. 13. The fundamentals of regression analysis/9. A geometrical representation of the linear regression model.mp4 5MB
  65. 9. Practical example inferential statistics/2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx 2MB
  66. 9. Practical example inferential statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx 2MB
  67. 9. Practical example inferential statistics/2.1 3.17.Practical-example.Confidence-intervals-exercise.xlsx 2MB
  68. 17. Practical example regression analysis/1.1 5.21. Regression_Analysis_practical_example.xlsx 1MB
  69. 11. Hypothesis testing Let's start testing!/3.1 Online p-value calculator.pdf 1MB
  70. 10. Hypothesis testing Introduction/1.1 Course notes_hypothesis_testing.pdf 656KB
  71. 10. Hypothesis testing Introduction/4.1 Course notes_hypothesis_testing.pdf 656KB
  72. 2. Sample or population data/1.2 Course notes_descriptive_statistics.pdf 482KB
  73. 3. The fundamentals of descriptive statistics/1.1 Course notes_descriptive_statistics.pdf 482KB
  74. 6. Distributions/1.1 Course notes_inferential statistics.pdf 382KB
  75. 6. Distributions/2.2 Course notes_inferential statistics.pdf 382KB
  76. 3. The fundamentals of descriptive statistics/13.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289KB
  77. 3. The fundamentals of descriptive statistics/7.1 Statistics - PDF with Excel Solutions that don't visualize properly.pdf 289KB
  78. 13. The fundamentals of regression analysis/1.1 Course notes_regression_analysis.pdf 270KB
  79. 13. The fundamentals of regression analysis/3.1 Course notes_regression_analysis.pdf 270KB
  80. 5. Practical example descriptive statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx 147KB
  81. 5. Practical example descriptive statistics/2.2 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx 146KB
  82. 5. Practical example descriptive statistics/2.1 2.13.Practical-example.Descriptive-statistics-exercise.xlsx 120KB
  83. 12. Practical example hypothesis testing/1.1 4.10.Hypothesis-testing-section-practical-example.xlsx 52KB
  84. 12. Practical example hypothesis testing/2.1 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx 44KB
  85. 12. Practical example hypothesis testing/2.2 4.10. Hypothesis testing section_practical example_exercise.xlsx 43KB
  86. 3. The fundamentals of descriptive statistics/7.2 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx 41KB
  87. 3. The fundamentals of descriptive statistics/16.2 2.6. Cross table and scatter plot_exercise_solution.xlsx 40KB
  88. 4. Measures of central tendency, asymmetry, and variability/3.1 2.8. Skewness_lesson.xlsx 35KB
  89. 3. The fundamentals of descriptive statistics/5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx 31KB
  90. 4. Measures of central tendency, asymmetry, and variability/12.2 2.11. Covariance_exercise_solution.xlsx 30KB
  91. 4. Measures of central tendency, asymmetry, and variability/15.1 2.12. Correlation_exercise_solution.xlsx 29KB
  92. 4. Measures of central tendency, asymmetry, and variability/15.2 2.12. Correlation_exercise.xlsx 29KB
  93. 3. The fundamentals of descriptive statistics/14.1 2.6. Cross table and scatter plot.xlsx 26KB
  94. 7. Estimators and estimates/5.1 3.9.The-z-table.xlsx 26KB
  95. 7. Estimators and estimates/6.2 3.9.The-z-table.xlsx 26KB
  96. 16. Dealing with categorical data/1.1 5.20. Dummy variables_lesson.xlsx 25KB
  97. 4. Measures of central tendency, asymmetry, and variability/13.1 2.12. Correlation_lesson.xlsx 25KB
  98. 4. Measures of central tendency, asymmetry, and variability/11.1 2.11. Covariance_lesson.xlsx 25KB
  99. 6. Distributions/8.1 3.4.Standard-normal-distribution-exercise-solution.xlsx 24KB
  100. 13. The fundamentals of regression analysis/11.1 5.6. Example_lesson.xlsx 24KB
  101. 1. Introduction/1.1 Statistics Glossary.xlsx 20KB
  102. 4. Measures of central tendency, asymmetry, and variability/12.1 2.11. Covariance_exercise.xlsx 20KB
  103. 2. Sample or population data/1.1 Glossary.xlsx 20KB
  104. 4. Measures of central tendency, asymmetry, and variability/5.2 2.8. Skewness_exercise_solution.xlsx 20KB
  105. 5. Practical example descriptive statistics/1. Practical example.srt 20KB
  106. 6. Distributions/2.1 3.2. What is a distribution_lesson.xlsx 19KB
  107. 3. The fundamentals of descriptive statistics/11.1 2.5. The Histogram_lesson.xlsx 19KB
  108. 14. Subtleties of regression analysis/12.1 5.12. Adjusted R-squared_lesson.xlsx 18KB
  109. 17. Practical example regression analysis/1. Practical example regression analysis.srt 18KB
  110. 3. The fundamentals of descriptive statistics/13.2 2.5.The-Histogram-exercise-solution.xlsx 17KB
  111. 3. The fundamentals of descriptive statistics/16.1 2.6. Cross table and scatter plot_exercise.xlsx 16KB
  112. 7. Estimators and estimates/10.2 3.11. The t-table.xlsx 16KB
  113. 7. Estimators and estimates/11.2 3.11. The t-table.xlsx 16KB
  114. 3. The fundamentals of descriptive statistics/13.3 2.5. The Histogram_exercise.xlsx 15KB
  115. 3. The fundamentals of descriptive statistics/7.3 2.3. Categorical variables. Visualization techniques_exercise.xlsx 15KB
  116. 11. Hypothesis testing Let's start testing!/5.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx 15KB
  117. 11. Hypothesis testing Let's start testing!/8.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx 14KB
  118. 8. Confidence intervals advanced topics/2.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx 14KB
  119. 8. Confidence intervals advanced topics/2.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx 14KB
  120. 9. Practical example inferential statistics/1. Practical example inferential statistics.srt 13KB
  121. 3. The fundamentals of descriptive statistics/10.2 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx 13KB
  122. 11. Hypothesis testing Let's start testing!/8.1 4.7. Test for the mean. Dependent samples_exercise.xlsx 13KB
  123. 11. Hypothesis testing Let's start testing!/6.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx 13KB
  124. 4. Measures of central tendency, asymmetry, and variability/10.2 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx 13KB
  125. 14. Subtleties of regression analysis/7.1 5.10.Regression-tables-lesson.xlsx 13KB
  126. 14. Subtleties of regression analysis/9.1 5.10. Regression tables_exercise_solution.xlsx 13KB
  127. 14. Subtleties of regression analysis/9.2 5.10. Regression tables_exercise.xlsx 12KB
  128. 3. The fundamentals of descriptive statistics/10.1 2.4.Numerical-variables.Frequency-distribution-table-exercise.xlsx 12KB
  129. 6. Distributions/8.2 3.4.Standard-normal-distribution-exercise.xlsx 12KB
  130. 4. Measures of central tendency, asymmetry, and variability/10.1 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx 12KB
  131. 3. The fundamentals of descriptive statistics/8.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx 11KB
  132. 11. Hypothesis testing Let's start testing!/13.1 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2-solution.xlsx 11KB
  133. 4. Measures of central tendency, asymmetry, and variability/2.1 2.7. Mean, median and mode_exercise_solution.xlsx 11KB
  134. 11. Hypothesis testing Let's start testing!/6.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx 11KB
  135. 11. Hypothesis testing Let's start testing!/10.2 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise-solution.xlsx 11KB
  136. 11. Hypothesis testing Let's start testing!/2.1 4.4. Test for the mean. Population variance known_exercise_solution.xlsx 11KB
  137. 7. Estimators and estimates/5.2 3.9. Population variance known, z-score_lesson.xlsx 11KB
  138. 7. Estimators and estimates/6.3 3.9. Population variance known, z-score_exercise_solution.xlsx 11KB
  139. 7. Estimators and estimates/11.1 3.11. Population variance unknown, t-score_exercise_solution.xlsx 11KB
  140. 4. Measures of central tendency, asymmetry, and variability/7.2 2.9. Variance_exercise_solution.xlsx 11KB
  141. 11. Hypothesis testing Let's start testing!/2.2 4.4. Test for the mean. Population variance known_exercise.xlsx 11KB
  142. 4. Measures of central tendency, asymmetry, and variability/8.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx 11KB
  143. 11. Hypothesis testing Let's start testing!/1.1 4.4. Test for the mean. Population variance known_lesson.xlsx 11KB
  144. 4. Measures of central tendency, asymmetry, and variability/2.2 2.7. Mean, median and mode_exercise.xlsx 11KB
  145. 7. Estimators and estimates/6.1 3.9. Population variance known, z-score_exercise.xlsx 11KB
  146. 4. Measures of central tendency, asymmetry, and variability/7.1 2.9. Variance_exercise.xlsx 11KB
  147. 7. Estimators and estimates/10.1 3.11. Population variance unknown, t-score_lesson.xlsx 11KB
  148. 11. Hypothesis testing Let's start testing!/10.1 4.8.Test-for-the-mean.Independent-samples-Part-1-exercise.xlsx 11KB
  149. 13. The fundamentals of regression analysis/3.2 5.2. Correlation and causation_lesson.xlsx 11KB
  150. 7. Estimators and estimates/11.3 3.11. Population variance unknown, t-score_exercise.xlsx 11KB
  151. 11. Hypothesis testing Let's start testing!/13.2 4.9.Test-for-the-mean.Independent-samples-Part-2-exercise-2.xlsx 11KB
  152. 4. Measures of central tendency, asymmetry, and variability/1.1 2.7. Mean, median and mode_lesson.xlsx 10KB
  153. 8. Confidence intervals advanced topics/1.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx 10KB
  154. 6. Distributions/6.1 3.4. Standard normal distribution_lesson.xlsx 10KB
  155. 8. Confidence intervals advanced topics/4.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx 10KB
  156. 4. Measures of central tendency, asymmetry, and variability/6.1 2.9. Variance_lesson.xlsx 10KB
  157. 8. Confidence intervals advanced topics/3.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx 10KB
  158. 8. Confidence intervals advanced topics/4.2 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise.xlsx 10KB
  159. 8. Confidence intervals advanced topics/6.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx 10KB
  160. 11. Hypothesis testing Let's start testing!/7.1 4.7. Test for the mean. Dependent samples_lesson.xlsx 10KB
  161. 11. Hypothesis testing Let's start testing!/9.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx 10KB
  162. 8. Confidence intervals advanced topics/5.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx 10KB
  163. 4. Measures of central tendency, asymmetry, and variability/5.1 2.8. Skewness_exercise.xlsx 9KB
  164. 10. Hypothesis testing Introduction/4. Establishing a rejection region and a significance level.srt 9KB
  165. 11. Hypothesis testing Let's start testing!/11.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx 9KB
  166. 8. Confidence intervals advanced topics/6.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx 9KB
  167. 7. Estimators and estimates/5. Calculating confidence intervals within a population with a known variance.srt 9KB
  168. 12. Practical example hypothesis testing/1. Practical example hypothesis testing.srt 8KB
  169. 8. Confidence intervals advanced topics/1. Calculating confidence intervals for two means with dependent samples.srt 8KB
  170. 13. The fundamentals of regression analysis/11. A practical example - Reinforced learning.srt 8KB
  171. 11. Hypothesis testing Let's start testing!/1. Test for the mean. Population variance known.srt 8KB
  172. 4. Measures of central tendency, asymmetry, and variability/6. Measuring how data is spread out calculating variance.srt 7KB
  173. 13. The fundamentals of regression analysis/5. The linear regression model made easy.srt 7KB
  174. 3. The fundamentals of descriptive statistics/5. Categorical variables. Visualization techniques for categorical variables.srt 7KB
  175. 10. Hypothesis testing Introduction/1. The null and the alternative hypothesis.srt 7KB
  176. 15. Assumptions for linear regression analysis/7. A3. Normality and homoscedasticity.srt 7KB
  177. 14. Subtleties of regression analysis/12. The adjusted R-squared.srt 7KB
  178. 7. Estimators and estimates/12. What is a margin of error and why is it important in Statistics.srt 7KB
  179. 14. Subtleties of regression analysis/3. What is R-squared and how does it help us.srt 7KB
  180. 3. The fundamentals of descriptive statistics/14. Cross tables and scatter plots.srt 7KB
  181. 16. Dealing with categorical data/1. Dummy variables.srt 7KB
  182. 3. The fundamentals of descriptive statistics/1. The various types of data we can work with.srt 6KB
  183. 11. Hypothesis testing Let's start testing!/7. Test for the mean. Dependent samples.srt 6KB
  184. 14. Subtleties of regression analysis/7. Studying regression tables.srt 6KB
  185. 4. Measures of central tendency, asymmetry, and variability/8. Standard deviation and coefficient of variation.srt 6KB
  186. 8. Confidence intervals advanced topics/3. Calculating confidence intervals for two means with independent samples (part 1).srt 6KB
  187. 1. Introduction/1. What does the course cover.srt 6KB
  188. 13. The fundamentals of regression analysis/3. Correlation and causation.srt 6KB
  189. 6. Distributions/2. What is a distribution.srt 6KB
  190. 11. Hypothesis testing Let's start testing!/5. Test for the mean. Population variance unknown.srt 6KB
  191. 2. Sample or population data/1. Understanding the difference between a population and a sample.srt 6KB
  192. 4. Measures of central tendency, asymmetry, and variability/1. The main measures of central tendency mean, median and mode.srt 6KB
  193. 6. Distributions/9. Understanding the central limit theorem.srt 6KB
  194. 7. Estimators and estimates/7. Confidence interval clarifications.srt 6KB
  195. 15. Assumptions for linear regression analysis/5. A2. No endogeneity.srt 5KB
  196. 10. Hypothesis testing Introduction/6. Type I error vs Type II error.srt 5KB
  197. 11. Hypothesis testing Let's start testing!/11. Test for the mean. Independent samples (Part 2).srt 5KB
  198. 11. Hypothesis testing Let's start testing!/9. Test for the mean. Independent samples (Part 1).srt 5KB
  199. 7. Estimators and estimates/10. Calculating confidence intervals within a population with an unknown variance.srt 5KB
  200. 11. Hypothesis testing Let's start testing!/3. What is the p-value and why is it one of the most useful tools for statisticians.srt 5KB
  201. 6. Distributions/4. The Normal distribution.srt 5KB
  202. 15. Assumptions for linear regression analysis/11. A5. No multicollinearity.srt 5KB
  203. 4. Measures of central tendency, asymmetry, and variability/11. Calculating and understanding covariance.srt 5KB
  204. 15. Assumptions for linear regression analysis/9. A4. No autocorrelation.srt 5KB
  205. 4. Measures of central tendency, asymmetry, and variability/13. The correlation coefficient.srt 5KB
  206. 3. The fundamentals of descriptive statistics/3. Levels of measurement.srt 5KB
  207. 14. Subtleties of regression analysis/1. Decomposing the linear regression model - understanding its nuts and bolts.srt 4KB
  208. 8. Confidence intervals advanced topics/5. Calculating confidence intervals for two means with independent samples (part 2).srt 4KB
  209. 3. The fundamentals of descriptive statistics/8. Numerical variables. Using a frequency distribution table.srt 4KB
  210. 7. Estimators and estimates/8. Student's T distribution.srt 4KB
  211. 6. Distributions/6. The standard normal distribution.srt 4KB
  212. 7. Estimators and estimates/1. Working with estimators and estimates.srt 4KB
  213. 14. Subtleties of regression analysis/10. The multiple linear regression model.srt 4KB
  214. 4. Measures of central tendency, asymmetry, and variability/3. Measuring skewness.srt 4KB
  215. 15. Assumptions for linear regression analysis/1. OLS assumptions.srt 3KB
  216. 3. The fundamentals of descriptive statistics/11. Histogram charts.srt 3KB
  217. 7. Estimators and estimates/3. Confidence intervals - an invaluable tool for decision making.srt 3KB
  218. 14. Subtleties of regression analysis/5. The ordinary least squares setting and its practical applications.srt 3KB
  219. 14. Subtleties of regression analysis/14. What does the F-statistic show us and why do we need to understand it.srt 3KB
  220. 15. Assumptions for linear regression analysis/3. A1. Linearity.srt 2KB
  221. 10. Hypothesis testing Introduction/2. Further reading on null and alternative hypotheses.html 2KB
  222. 13. The fundamentals of regression analysis/7. What is the difference between correlation and regression.srt 2KB
  223. 6. Distributions/11. Standard error.srt 2KB
  224. 8. Confidence intervals advanced topics/7. Calculating confidence intervals for two means with independent samples (part 3).srt 2KB
  225. 18. Bonus lecture/1. Bonus lecture Next steps.html 2KB
  226. 13. The fundamentals of regression analysis/9. A geometrical representation of the linear regression model.srt 2KB
  227. 13. The fundamentals of regression analysis/1. Introduction to regression analysis.srt 2KB
  228. 6. Distributions/1. Introduction to inferential statistics.srt 2KB
  229. 1. Introduction/2. Download all resources.html 716B
  230. 11. Hypothesis testing Let's start testing!/12. Test for the mean. Independent samples (Part 2).html 160B
  231. 13. The fundamentals of regression analysis/10. A geometrical representation of the linear regression model.html 160B
  232. 13. The fundamentals of regression analysis/4. Correlation and causation.html 160B
  233. 14. Subtleties of regression analysis/11. The multiple linear regression model.html 160B
  234. 14. Subtleties of regression analysis/6. The ordinary least squares setting and its practical applications.html 160B
  235. 14. Subtleties of regression analysis/8. Studying regression tables.html 160B
  236. 15. Assumptions for linear regression analysis/4. A1. Linearity.html 160B
  237. 15. Assumptions for linear regression analysis/8. A3. Normality and homoscedasticity.html 160B
  238. 3. The fundamentals of descriptive statistics/12. Histogram charts.html 160B
  239. 3. The fundamentals of descriptive statistics/15. Cross Tables and Scatter Plots.html 160B
  240. 3. The fundamentals of descriptive statistics/6. Categorical variables. Visualization Techniques.html 160B
  241. 3. The fundamentals of descriptive statistics/9. Numerical variables. Using a frequency distribution table.html 160B
  242. 4. Measures of central tendency, asymmetry, and variability/14. Correlation.html 160B
  243. 4. Measures of central tendency, asymmetry, and variability/4. Skewness.html 160B
  244. 4. Measures of central tendency, asymmetry, and variability/9. Standard deviation.html 160B
  245. 6. Distributions/12. Standard error.html 160B
  246. 6. Distributions/7. The standard normal distribution.html 160B
  247. 10. Hypothesis testing Introduction/3. Null vs alternative.html 159B
  248. 10. Hypothesis testing Introduction/5. Rejection region and significance level.html 159B
  249. 10. Hypothesis testing Introduction/7. Type I error vs type II error.html 159B
  250. 11. Hypothesis testing Let's start testing!/4. p-value.html 159B
  251. 13. The fundamentals of regression analysis/2. Introduction.html 159B
  252. 13. The fundamentals of regression analysis/6. The linear regression model.html 159B
  253. 13. The fundamentals of regression analysis/8. Correlation vs regression.html 159B
  254. 14. Subtleties of regression analysis/13. The adjusted R-squared.html 159B
  255. 14. Subtleties of regression analysis/2. Decomposition.html 159B
  256. 14. Subtleties of regression analysis/4. R-squared.html 159B
  257. 15. Assumptions for linear regression analysis/10. A4. No autocorrelation.html 159B
  258. 15. Assumptions for linear regression analysis/12. A5. No multicollinearity.html 159B
  259. 15. Assumptions for linear regression analysis/2. OLS assumptions.html 159B
  260. 15. Assumptions for linear regression analysis/6. A2. No endogeneity.html 159B
  261. 2. Sample or population data/2. Population vs sample.html 159B
  262. 3. The fundamentals of descriptive statistics/2. Types of data.html 159B
  263. 3. The fundamentals of descriptive statistics/4. Levels of measurement.html 159B
  264. 6. Distributions/10. The central limit theorem.html 159B
  265. 6. Distributions/3. What is a distribution.html 159B
  266. 6. Distributions/5. The Normal distribution.html 159B
  267. 7. Estimators and estimates/13. Margin of error.html 159B
  268. 7. Estimators and estimates/2. Estimators and estimates.html 159B
  269. 7. Estimators and estimates/4. Confidence intervals.html 159B
  270. 7. Estimators and estimates/9. Student's T distribution.html 159B
  271. [Tutorialsplanet.NET].url 128B
  272. 11. Hypothesis testing Let's start testing!/2. Test for the mean. Population variance known. Exercise.html 86B
  273. 11. Hypothesis testing Let's start testing!/6. Test for the mean. Population variance unknown. Exercise.html 86B
  274. 11. Hypothesis testing Let's start testing!/8. Test for the mean. Dependent samples. Exercise.html 86B
  275. 12. Practical example hypothesis testing/2. Practical example hypothesis testing.html 86B
  276. 14. Subtleties of regression analysis/9. Regression tables. Exercise.html 86B
  277. 11. Hypothesis testing Let's start testing!/10. Test for the mean. Independent samples (Part 1).html 82B
  278. 11. Hypothesis testing Let's start testing!/13. Test for the mean. Independent samples (Part 2). Exercise.html 82B
  279. 3. The fundamentals of descriptive statistics/10. Numerical variables. Using a frequency distribution table. Exercise.html 81B
  280. 3. The fundamentals of descriptive statistics/13. Histogram charts. Exercise.html 81B
  281. 3. The fundamentals of descriptive statistics/16. Cross tables and scatter plots. Exercise.html 81B
  282. 3. The fundamentals of descriptive statistics/7. Categorical variables. Visualization techniques. Exercise.html 81B
  283. 4. Measures of central tendency, asymmetry, and variability/10. Standard deviation and coefficient of variation. Exercise.html 81B
  284. 4. Measures of central tendency, asymmetry, and variability/12. Covariance. Exercise.html 81B
  285. 4. Measures of central tendency, asymmetry, and variability/15. Correlation coefficient.html 81B
  286. 4. Measures of central tendency, asymmetry, and variability/2. Mean, median and mode. Exercise.html 81B
  287. 4. Measures of central tendency, asymmetry, and variability/5. Skewness. Exercise.html 81B
  288. 4. Measures of central tendency, asymmetry, and variability/7. Variance. Exercise.html 81B
  289. 5. Practical example descriptive statistics/2. Practical example descriptive statistics.html 81B
  290. 6. Distributions/8. Standard Normal Distribution. Exercise.html 81B
  291. 7. Estimators and estimates/11. Population variance unknown. T-score. Exercise.html 81B
  292. 7. Estimators and estimates/6. Confidence intervals. Population variance known. Exercise.html 81B
  293. 8. Confidence intervals advanced topics/2. Confidence intervals. Two means. Dependent samples. Exercise.html 81B
  294. 8. Confidence intervals advanced topics/4. Confidence intervals. Two means. Independent samples (Part 1). Exercise.html 81B
  295. 8. Confidence intervals advanced topics/6. Confidence intervals. Two means. Independent samples (Part 2). Exercise.html 81B
  296. 9. Practical example inferential statistics/2. Practical example inferential statistics.html 81B