• Урок 1. 00:04:29
    [Important] Getting the most out of this course
  • Урок 2. 00:04:10
    About using MATLAB or Python
  • Урок 3. 00:08:48
    Statistics guessing game!
  • Урок 4. 00:05:17
    Using the Q&A forum
  • Урок 5. 00:01:53
    (optional) Entering time-stamped notes in the Udemy video player
  • Урок 6. 00:03:13
    Should you memorize statistical formulas?
  • Урок 7. 00:04:03
    Arithmetic and exponents
  • Урок 8. 00:05:54
    Scientific notation
  • Урок 9. 00:04:22
    Summation notation
  • Урок 10. 00:03:05
    Absolute value
  • Урок 11. 00:05:54
    Natural exponent and logarithm
  • Урок 12. 00:08:59
    The logistic function
  • Урок 13. 00:06:31
    Rank and tied-rank
  • Урок 14. 00:03:49
    Download materials for the entire course!
  • Урок 15. 00:01:54
    Is "data" singular or plural?!?!!?!
  • Урок 16. 00:06:10
    Where do data come from and what do they mean?
  • Урок 17. 00:14:57
    Types of data: categorical, numerical, etc
  • Урок 18. 00:08:59
    Code: representing types of data on computers
  • Урок 19. 00:12:03
    Sample vs. population data
  • Урок 20. 00:05:32
    Samples, case reports, and anecdotes
  • Урок 21. 00:06:58
    The ethics of making up data
  • Урок 22. 00:11:39
    Bar plots
  • Урок 23. 00:17:00
    Code: bar plots
  • Урок 24. 00:05:42
    Box-and-whisker plots
  • Урок 25. 00:08:42
    Code: box plots
  • Урок 26. 00:02:32
    "Unsupervised learning": Boxplots of normal and uniform noise
  • Урок 27. 00:11:17
    Histograms
  • Урок 28. 00:16:41
    Code: histograms
  • Урок 29. 00:02:23
    "Unsupervised learning": Histogram proportion
  • Урок 30. 00:06:00
    Pie charts
  • Урок 31. 00:13:23
    Code: pie charts
  • Урок 32. 00:06:12
    When to use lines instead of bars
  • Урок 33. 00:09:05
    Linear vs. logarithmic axis scaling
  • Урок 34. 00:07:25
    Code: line plots
  • Урок 35. 00:01:45
    "Unsupervised learning": log-scaled plots
  • Урок 36. 00:04:32
    Descriptive vs. inferential statistics
  • Урок 37. 00:07:30
    Accuracy, precision, resolution
  • Урок 38. 00:11:27
    Data distributions
  • Урок 39. 00:32:09
    Code: data from different distributions
  • Урок 40. 00:01:58
    "Unsupervised learning": histograms of distributions
  • Урок 41. 00:05:30
    The beauty and simplicity of Normal
  • Урок 42. 00:12:48
    Measures of central tendency (mean)
  • Урок 43. 00:12:18
    Measures of central tendency (median, mode)
  • Урок 44. 00:13:59
    Code: computing central tendency
  • Урок 45. 00:03:09
    "Unsupervised learning": central tendencies with outliers
  • Урок 46. 00:17:49
    Measures of dispersion (variance, standard deviation)
  • Урок 47. 00:26:34
    Code: Computing dispersion
  • Урок 48. 00:04:54
    Interquartile range (IQR)
  • Урок 49. 00:15:59
    Code: IQR
  • Урок 50. 00:07:23
    QQ plots
  • Урок 51. 00:15:35
    Code: QQ plots
  • Урок 52. 00:08:24
    Statistical "moments"
  • Урок 53. 00:10:01
    Histograms part 2: Number of bins
  • Урок 54. 00:12:25
    Code: Histogram bins
  • Урок 55. 00:03:20
    Violin plots
  • Урок 56. 00:10:10
    Code: violin plots
  • Урок 57. 00:02:32
    "Unsupervised learning": asymmetric violin plots
  • Урок 58. 00:11:03
    Shannon entropy
  • Урок 59. 00:20:16
    Code: entropy
  • Урок 60. 00:01:27
    "Unsupervised learning": entropy and number of bins
  • Урок 61. 00:04:11
    Garbage in, garbage out (GIGO)
  • Урок 62. 00:09:26
    Z-score standardization
  • Урок 63. 00:12:51
    Code: z-score
  • Урок 64. 00:05:07
    Min-max scaling
  • Урок 65. 00:08:17
    Code: min-max scaling
  • Урок 66. 00:02:36
    "Unsupervised learning": Invert the min-max scaling
  • Урок 67. 00:14:27
    What are outliers and why are they dangerous?
  • Урок 68. 00:09:27
    Removing outliers: z-score method
  • Урок 69. 00:04:05
    The modified z-score method
  • Урок 70. 00:22:31
    Code: z-score for outlier removal
  • Урок 71. 00:02:39
    "Unsupervised learning": z vs. modified-z
  • Урок 72. 00:09:27
    Multivariate outlier detection
  • Урок 73. 00:09:02
    Code: Euclidean distance for outlier removal
  • Урок 74. 00:05:48
    Removing outliers by data trimming
  • Урок 75. 00:11:04
    Code: Data trimming to remove outliers
  • Урок 76. 00:04:41
    Non-parametric solutions to outliers
  • Урок 77. 00:03:05
    An outlier lecture on personal accountability
  • Урок 78. 00:12:18
    What is probability?
  • Урок 79. 00:09:26
    Probability vs. proportion
  • Урок 80. 00:10:29
    Computing probabilities
  • Урок 81. 00:14:35
    Code: compute probabilities
  • Урок 82. 00:04:59
    Probability and odds
  • Урок 83. 00:02:31
    "Unsupervised learning": probabilities of odds-space
  • Урок 84. 00:13:07
    Probability mass vs. density
  • Урок 85. 00:11:38
    Code: compute probability mass functions
  • Урок 86. 00:10:45
    Cumulative probability distributions
  • Урок 87. 00:09:42
    Code: cdfs and pdfs
  • Урок 88. 00:02:26
    "Unsupervised learning": cdf's for various distributions
  • Урок 89. 00:18:32
    Creating sample estimate distributions
  • Урок 90. 00:02:54
    Monte Carlo sampling
  • Урок 91. 00:08:42
    Sampling variability, noise, and other annoyances
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    Code: sampling variability
  • Урок 93. 00:10:10
    Expected value
  • Урок 94. 00:12:46
    Conditional probability
  • Урок 95. 00:20:13
    Code: conditional probabilities
  • Урок 96. 00:06:25
    Tree diagrams for conditional probabilities
  • Урок 97. 00:09:51
    The Law of Large Numbers
  • Урок 98. 00:19:24
    Code: Law of Large Numbers in action
  • Урок 99. 00:10:35
    The Central Limit Theorem
  • Урок 100. 00:16:22
    Code: the CLT in action
  • Урок 101. 00:02:10
    "Unsupervised learning": Averaging pairs of numbers
  • Урок 102. 00:16:46
    IVs, DVs, models, and other stats lingo
  • Урок 103. 00:15:09
    What is an hypothesis and how do you specify one?
  • Урок 104. 00:10:39
    Sample distributions under null and alternative hypotheses
  • Урок 105. 00:18:57
    P-values: definition, tails, and misinterpretations
  • Урок 106. 00:06:52
    P-z combinations that you should memorize
  • Урок 107. 00:12:22
    Degrees of freedom
  • Урок 108. 00:14:19
    Type 1 and Type 2 errors
  • Урок 109. 00:09:13
    Parametric vs. non-parametric tests
  • Урок 110. 00:08:34
    Multiple comparisons and Bonferroni correction
  • Урок 111. 00:06:52
    Statistical vs. theoretical vs. clinical significance
  • Урок 112. 00:11:31
    Cross-validation
  • Урок 113. 00:11:13
    Statistical significance vs. classification accuracy
  • Урок 114. 00:13:14
    Purpose and interpretation of the t-test
  • Урок 115. 00:08:10
    One-sample t-test
  • Урок 116. 00:20:47
    Code: One-sample t-test
  • Урок 117. 00:02:51
    "Unsupervised learning": The role of variance
  • Урок 118. 00:13:07
    Two-samples t-test
  • Урок 119. 00:22:10
    Code: Two-samples t-test
  • Урок 120. 00:04:46
    "Unsupervised learning": Importance of N for t-test
  • Урок 121. 00:07:37
    Wilcoxon signed-rank (nonparametric t-test)
  • Урок 122. 00:18:35
    Code: Signed-rank test
  • Урок 123. 00:06:04
    Mann-Whitney U test (nonparametric t-test)
  • Урок 124. 00:05:22
    Code: Mann-Whitney U test
  • Урок 125. 00:11:27
    Permutation testing for t-test significance
  • Урок 126. 00:25:27
    Code: permutation testing
  • Урок 127. 00:05:22
    "Unsupervised learning": How many permutations?
  • Урок 128. 00:08:46
    What are confidence intervals and why do we need them?
  • Урок 129. 00:06:44
    Computing confidence intervals via formula
  • Урок 130. 00:17:12
    Code: compute confidence intervals by formula
  • Урок 131. 00:08:59
    Confidence intervals via bootstrapping (resampling)
  • Урок 132. 00:14:34
    Code: bootstrapping confidence intervals
  • Урок 133. 00:01:26
    "Unsupervised learning:" Confidence intervals for variance
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    Misconceptions about confidence intervals
  • Урок 135. 00:18:21
    Motivation and description of correlation
  • Урок 136. 00:14:10
    Covariance and correlation: formulas
  • Урок 137. 00:27:50
    Code: correlation coefficient
  • Урок 138. 00:13:51
    Code: Simulate data with specified correlation
  • Урок 139. 00:09:35
    Correlation matrix
  • Урок 140. 00:20:26
    Code: correlation matrix
  • Урок 141. 00:02:52
    "Unsupervised learning": average correlation matrices
  • Урок 142. 00:04:17
    "Unsupervised learning": correlation to covariance matrix
  • Урок 143. 00:10:24
    Partial correlation
  • Урок 144. 00:19:56
    Code: partial correlation
  • Урок 145. 00:06:44
    The problem with Pearson
  • Урок 146. 00:07:18
    Nonparametric correlation: Spearman rank
  • Урок 147. 00:06:55
    Fisher-Z transformation for correlations
  • Урок 148. 00:07:41
    Code: Spearman correlation and Fisher-Z
  • Урок 149. 00:01:29
    "Unsupervised learning": Spearman correlation
  • Урок 150. 00:02:26
    "Unsupervised learning": confidence interval on correlation
  • Урок 151. 00:10:38
    Kendall's correlation for ordinal data
  • Урок 152. 00:18:10
    Code: Kendall correlation
  • Урок 153. 00:02:40
    "Unsupervised learning": Does Kendall vs. Pearson matter?
  • Урок 154. 00:04:42
    The subgroups correlation paradox
  • Урок 155. 00:05:27
    Cosine similarity
  • Урок 156. 00:21:20
    Code: Cosine similarity vs. Pearson correlation
  • Урок 157. 00:17:52
    ANOVA intro, part1
  • Урок 158. 00:19:57
    ANOVA intro, part 2
  • Урок 159. 00:18:14
    Sum of squares
  • Урок 160. 00:07:29
    The F-test and the ANOVA table
  • Урок 161. 00:12:39
    The omnibus F-test and post-hoc comparisons
  • Урок 162. 00:19:53
    The two-way ANOVA
  • Урок 163. 00:13:25
    One-way ANOVA example
  • Урок 164. 00:16:35
    Code: One-way ANOVA (independent samples)
  • Урок 165. 00:12:18
    Code: One-way repeated-measures ANOVA
  • Урок 166. 00:11:18
    Two-way ANOVA example
  • Урок 167. 00:14:29
    Code: Two-way mixed ANOVA
  • Урок 168. 00:19:54
    Introduction to GLM / regression
  • Урок 169. 00:09:47
    Least-squares solution to the GLM
  • Урок 170. 00:16:18
    Evaluating regression models: R2 and F
  • Урок 171. 00:13:18
    Simple regression
  • Урок 172. 00:09:13
    Code: simple regression
  • Урок 173. 00:01:06
    "Unsupervised learning": Compute R2 and F
  • Урок 174. 00:13:02
    Multiple regression
  • Урок 175. 00:12:19
    Standardizing regression coefficients
  • Урок 176. 00:18:43
    Code: Multiple regression
  • Урок 177. 00:08:57
    Polynomial regression models
  • Урок 178. 00:15:47
    Code: polynomial modeling
  • Урок 179. 00:00:53
    "Unsupervised learning": Polynomial design matrix
  • Урок 180. 00:16:56
    Logistic regression
  • Урок 181. 00:09:28
    Code: Logistic regression
  • Урок 182. 00:16:20
    Under- and over-fitting
  • Урок 183. 00:01:57
    "Unsupervised learning": Overfit data
  • Урок 184. 00:12:26
    Comparing "nested" models
  • Урок 185. 00:06:37
    What to do about missing data
  • Урок 186. 00:09:48
    What is statistical power and why is it important?
  • Урок 187. 00:11:23
    Estimating statistical power and sample size
  • Урок 188. 00:04:11
    Compute power and sample size using G*Power
  • Урок 189. 00:13:47
    K-means clustering
  • Урок 190. 00:22:12
    Code: k-means clustering
  • Урок 191. 00:01:54
    "Unsupervised learning:" K-means and normalization
  • Урок 192. 00:01:27
    "Unsupervised learning:" K-means on a Gauss blur
  • Урок 193. 00:14:19
    Clustering via dbscan
  • Урок 194. 00:33:04
    Code: dbscan
  • Урок 195. 00:03:05
    "Unsupervised learning": dbscan vs. k-means
  • Урок 196. 00:06:21
    K-nearest neighbor classification
  • Урок 197. 00:11:49
    Code: KNN
  • Урок 198. 00:16:35
    Principal components analysis (PCA)
  • Урок 199. 00:17:33
    Code: PCA
  • Урок 200. 00:01:36
    "Unsupervised learning:" K-means on PC data
  • Урок 201. 00:12:46
    Independent components analysis (ICA)
  • Урок 202. 00:12:41
    Code: ICA
  • Урок 203. 00:05:30
    The two perspectives of the world
  • Урок 204. 00:12:31
    d-prime
  • Урок 205. 00:15:03
    Code: d-prime
  • Урок 206. 00:08:03
    Response bias
  • Урок 207. 00:04:16
    Code: Response bias
  • Урок 208. 00:07:35
    Receiver operating characteristics (ROC)
  • Урок 209. 00:08:11
    Code: ROC curves
  • Урок 210. 00:01:34
    "Unsupervised learning": Make this plot look nicer!
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