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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.ME].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| 001 [Important] Getting the most out of this course_en.srt |
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| 001 [Important] Getting the most out of this course_en.vtt |
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| 001 [Important] Getting the most out of this course.mp4 |
38.26Мб |
| 001 About deep learning.html |
619б |
| 001 ANOVA intro, part1_en.srt |
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| 001 ANOVA intro, part1_en.vtt |
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| 001 ANOVA intro, part1.mp4 |
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| 001 Bar plots_en.srt |
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| 001 Bar plots_en.vtt |
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| 001 Bar plots.mp4 |
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| 001 Descriptive vs. inferential statistics_en.srt |
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| 001 Descriptive vs. inferential statistics_en.vtt |
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| 001 Descriptive vs. inferential statistics.mp4 |
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| 001 Download materials for the entire course__en.srt |
5.42Кб |
| 001 Download materials for the entire course__en.vtt |
4.77Кб |
| 001 Download materials for the entire course_.mp4 |
14.46Мб |
| 001 Garbage in, garbage out (GIGO)_en.srt |
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| 001 Garbage in, garbage out (GIGO)_en.vtt |
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| 001 Garbage in, garbage out (GIGO).mp4 |
11.55Мб |
| 001 Introduction to GLM _ regression_en.srt |
29.73Кб |
| 001 Introduction to GLM _ regression_en.vtt |
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| 001 Introduction to GLM _ regression.mp4 |
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| 001 Is _data_ singular or plural________en.srt |
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| 001 Is _data_ singular or plural________en.vtt |
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| 001 Is _data_ singular or plural_______.mp4 |
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| 001 IVs, DVs, models, and other stats lingo_en.srt |
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| 001 IVs, DVs, models, and other stats lingo_en.vtt |
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| 001 IVs, DVs, models, and other stats lingo.mp4 |
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| 001 K-means clustering_en.srt |
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| 001 K-means clustering_en.vtt |
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| 001 K-means clustering.mp4 |
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| 001 Motivation and description of correlation_en.srt |
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| 001 Motivation and description of correlation_en.vtt |
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| 001 Motivation and description of correlation.mp4 |
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| 001 Note about the code for this section.html |
135б |
| 001 Purpose and interpretation of the t-test_en.srt |
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| 001 Purpose and interpretation of the t-test_en.vtt |
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| 001 Purpose and interpretation of the t-test.mp4 |
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| 001 Should you memorize statistical formulas__en.srt |
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| 001 Should you memorize statistical formulas__en.vtt |
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| 001 Should you memorize statistical formulas_.mp4 |
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| 001 The two perspectives of the world_en.srt |
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| 001 The two perspectives of the world_en.vtt |
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| 001 The two perspectives of the world.mp4 |
13.91Мб |
| 001 What are confidence intervals and why do we need them__en.srt |
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| 001 What are confidence intervals and why do we need them__en.vtt |
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| 001 What are confidence intervals and why do we need them_.mp4 |
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| 001 What is probability__en.srt |
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| 001 What is probability__en.vtt |
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| 001 What is probability_.mp4 |
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| 001 What is statistical power and why is it important__en.srt |
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| 001 What is statistical power and why is it important__en.vtt |
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| 001 What is statistical power and why is it important_.mp4 |
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| 002 About using MATLAB or Python_en.srt |
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| 002 About using MATLAB or Python_en.vtt |
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| 002 About using MATLAB or Python.mp4 |
27.11Мб |
| 002 Accuracy, precision, resolution_en.srt |
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| 002 Accuracy, precision, resolution_en.vtt |
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| 002 Accuracy, precision, resolution.mp4 |
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| 002 ANOVA intro, part 2_en.srt |
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| 002 ANOVA intro, part 2_en.vtt |
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| 002 ANOVA intro, part 2.mp4 |
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| 002 Arithmetic and exponents_en.srt |
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| 002 Arithmetic and exponents_en.vtt |
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| 002 Arithmetic and exponents.mp4 |
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| 002 Bonus content.html |
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| 002 Code_ bar plots_en.srt |
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| 002 Code_ bar plots_en.vtt |
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| 002 Code_ bar plots.mp4 |
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| 002 Code_ k-means clustering_en.srt |
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| 002 Code_ k-means clustering_en.vtt |
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| 002 Code_ k-means clustering.mp4 |
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| 002 Computing confidence intervals via formula_en.srt |
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| 002 Computing confidence intervals via formula_en.vtt |
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| 002 Computing confidence intervals via formula.mp4 |
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| 002 Covariance and correlation_ formulas_en.srt |
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| 002 Covariance and correlation_ formulas_en.vtt |
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| 002 Covariance and correlation_ formulas.mp4 |
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| 002 d-prime_en.srt |
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| 002 d-prime_en.vtt |
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| 002 d-prime.mp4 |
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| 002 Estimating statistical power and sample size_en.srt |
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| 002 Estimating statistical power and sample size_en.vtt |
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| 002 Estimating statistical power and sample size.mp4 |
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| 002 Introduction_en.srt |
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| 002 Introduction_en.vtt |
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| 002 Introduction.mp4 |
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| 002 Least-squares solution to the GLM_en.srt |
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| 002 Least-squares solution to the GLM_en.vtt |
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| 002 Least-squares solution to the GLM.mp4 |
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| 002 One-sample t-test_en.srt |
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| 002 One-sample t-test_en.vtt |
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| 002 One-sample t-test.mp4 |
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| 002 Probability vs. proportion_en.srt |
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| 002 Probability vs. proportion_en.vtt |
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| 002 Probability vs. proportion.mp4 |
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| 002 What is an hypothesis and how do you specify one__en.srt |
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| 002 What is an hypothesis and how do you specify one__en.vtt |
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| 002 What is an hypothesis and how do you specify one_.mp4 |
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| 002 Where do data come from and what do they mean__en.srt |
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| 002 Where do data come from and what do they mean__en.vtt |
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| 002 Where do data come from and what do they mean_.mp4 |
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| 002 Z-score standardization_en.srt |
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| 002 Z-score standardization_en.vtt |
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| 002 Z-score standardization.mp4 |
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| 003 _Unsupervised learning__ K-means and normalization_en.srt |
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| 003 _Unsupervised learning__ K-means and normalization_en.vtt |
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| 003 _Unsupervised learning__ K-means and normalization.mp4 |
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| 003 Box-and-whisker plots_en.srt |
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| 003 Box-and-whisker plots_en.vtt |
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| 003 Box-and-whisker plots.mp4 |
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| 003 Code_ compute confidence intervals by formula_en.srt |
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| 003 Code_ compute confidence intervals by formula_en.vtt |
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| 003 Code_ compute confidence intervals by formula.mp4 |
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| 003 Code_ correlation coefficient_en.srt |
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| 003 Code_ correlation coefficient_en.vtt |
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| 003 Code_ correlation coefficient.mp4 |
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| 003 Code_ d-prime_en.srt |
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| 003 Code_ d-prime_en.vtt |
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| 003 Code_ d-prime.mp4 |
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| 003 Code_ One-sample t-test_en.srt |
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| 003 Code_ One-sample t-test_en.vtt |
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| 003 Code_ One-sample t-test.mp4 |
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| 003 Code_ z-score_en.srt |
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| 003 Code_ z-score_en.vtt |
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| 003 Code_ z-score.mp4 |
66.77Мб |
| 003 Compute power and sample size using G_Power_en.srt |
6.82Кб |
| 003 Compute power and sample size using G_Power_en.vtt |
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| 003 Compute power and sample size using G_Power.mp4 |
31.20Мб |
| 003 Computing probabilities_en.srt |
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| 003 Computing probabilities_en.vtt |
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| 003 Computing probabilities.mp4 |
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| 003 Data distributions_en.srt |
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| 003 Data distributions_en.vtt |
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| 003 Data distributions.mp4 |
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| 003 Evaluating regression models_ R2 and F_en.srt |
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| 003 Evaluating regression models_ R2 and F_en.vtt |
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| 003 Evaluating regression models_ R2 and F.mp4 |
38.06Мб |
| 003 MATLAB_ Import and clean the marriage data_en.srt |
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| 003 MATLAB_ Import and clean the marriage data_en.vtt |
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| 003 MATLAB_ Import and clean the marriage data.mp4 |
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| 003 Sample distributions under null and alternative hypotheses_en.srt |
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| 003 Sample distributions under null and alternative hypotheses_en.vtt |
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| 003 Sample distributions under null and alternative hypotheses.mp4 |
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| 003 Scientific notation_en.srt |
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| 003 Scientific notation_en.vtt |
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| 003 Scientific notation.mp4 |
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| 003 Statistics guessing game__en.srt |
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| 003 Statistics guessing game__en.vtt |
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| 003 Statistics guessing game_.mp4 |
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| 003 Sum of squares_en.srt |
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| 003 Sum of squares_en.vtt |
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| 003 Sum of squares.mp4 |
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| 003 Types of data_ categorical, numerical, etc_en.srt |
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| 003 Types of data_ categorical, numerical, etc_en.vtt |
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| 003 Types of data_ categorical, numerical, etc.mp4 |
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| 004 _Unsupervised learning__ K-means on a Gauss blur_en.srt |
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| 004 _Unsupervised learning__ K-means on a Gauss blur_en.vtt |
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| 004 _Unsupervised learning__ K-means on a Gauss blur.mp4 |
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| 004 _Unsupervised learning__ The role of variance_en.srt |
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| 004 _Unsupervised learning__ The role of variance_en.vtt |
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| 004 _Unsupervised learning__ The role of variance.mp4 |
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| 004 Code_ box plots_en.srt |
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| 004 Code_ box plots_en.vtt |
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| 004 Code_ box plots.mp4 |
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| 004 Code_ compute probabilities_en.srt |
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| 004 Code_ compute probabilities_en.vtt |
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| 004 Code_ compute probabilities.mp4 |
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| 004 Code_ data from different distributions_en.srt |
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| 004 Code_ data from different distributions_en.vtt |
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| 004 Code_ data from different distributions.mp4 |
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| 004 Code_ representing types of data on computers_en.srt |
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| 004 Code_ representing types of data on computers_en.vtt |
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| 004 Code_ representing types of data on computers.mp4 |
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| 004 Code_ Simulate data with specified correlation_en.srt |
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| 004 Code_ Simulate data with specified correlation_en.vtt |
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| 004 Code_ Simulate data with specified correlation.mp4 |
70.12Мб |
| 004 Confidence intervals via bootstrapping (resampling)_en.srt |
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| 004 Confidence intervals via bootstrapping (resampling)_en.vtt |
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| 004 Confidence intervals via bootstrapping (resampling).mp4 |
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| 004 MATLAB_ Import the divorce data_en.srt |
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| 004 MATLAB_ Import the divorce data_en.vtt |
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| 004 MATLAB_ Import the divorce data.mp4 |
96.29Мб |
| 004 Min-max scaling_en.srt |
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| 004 Min-max scaling_en.vtt |
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| 004 Min-max scaling.mp4 |
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| 004 P-values_ definition, tails, and misinterpretations_en.srt |
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| 004 P-values_ definition, tails, and misinterpretations_en.vtt |
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| 004 P-values_ definition, tails, and misinterpretations.mp4 |
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| 004 Response bias_en.srt |
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| 004 Response bias_en.vtt |
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| 004 Response bias.mp4 |
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| 004 Simple regression_en.srt |
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| 004 Simple regression_en.vtt |
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| 004 Simple regression.mp4 |
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| 004 Summation notation_en.srt |
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| 004 Summation notation_en.vtt |
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| 004 Summation notation.mp4 |
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| 004 The F-test and the ANOVA table_en.srt |
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| 004 The F-test and the ANOVA table_en.vtt |
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| 004 The F-test and the ANOVA table.mp4 |
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| 004 Using the Q&A forum_en.srt |
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| 004 Using the Q&A forum_en.vtt |
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| 004 Using the Q&A forum.mp4 |
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| 005 _Unsupervised learning__ Boxplots of normal and uniform noise_en.srt |
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| 005 _Unsupervised learning__ Boxplots of normal and uniform noise_en.vtt |
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| 005 _Unsupervised learning__ Boxplots of normal and uniform noise.mp4 |
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| 005 _Unsupervised learning__ histograms of distributions_en.srt |
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| 005 _Unsupervised learning__ histograms of distributions_en.vtt |
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| 005 _Unsupervised learning__ histograms of distributions.mp4 |
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| 005 (optional) Entering time-stamped notes in the Udemy video player_en.srt |
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| 005 (optional) Entering time-stamped notes in the Udemy video player_en.vtt |
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| 005 (optional) Entering time-stamped notes in the Udemy video player.mp4 |
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| 005 Absolute value_en.srt |
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| 005 Absolute value_en.vtt |
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| 005 Absolute value.mp4 |
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| 005 Clustering via dbscan_en.srt |
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| 005 Clustering via dbscan_en.vtt |
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| 005 Clustering via dbscan.mp4 |
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| 005 Code_ bootstrapping confidence intervals_en.srt |
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| 005 Code_ bootstrapping confidence intervals_en.vtt |
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| 005 Code_ bootstrapping confidence intervals.mp4 |
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| 005 Code_ min-max scaling_en.srt |
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| 005 Code_ min-max scaling_en.vtt |
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| 005 Code_ min-max scaling.mp4 |
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| 005 Code_ Response bias_en.srt |
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| 005 Code_ Response bias_en.vtt |
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| 005 Code_ Response bias.mp4 |
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| 005 Code_ simple regression_en.srt |
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| 005 Code_ simple regression_en.vtt |
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| 005 Code_ simple regression.mp4 |
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| 005 Correlation matrix_en.srt |
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| 005 Correlation matrix_en.vtt |
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| 005 Correlation matrix.mp4 |
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| 005 MATLAB_ More data visualizations_en.srt |
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| 005 MATLAB_ More data visualizations_en.vtt |
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| 005 MATLAB_ More data visualizations.mp4 |
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| 005 Probability and odds_en.srt |
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| 005 Probability and odds_en.vtt |
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| 005 Probability and odds.mp4 |
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| 005 P-z combinations that you should memorize_en.srt |
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| 005 P-z combinations that you should memorize_en.vtt |
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| 005 P-z combinations that you should memorize.mp4 |
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| 005 Sample vs. population data_en.srt |
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| 005 Sample vs. population data_en.vtt |
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| 005 Sample vs. population data.mp4 |
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| 005 The omnibus F-test and post-hoc comparisons_en.srt |
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| 005 The omnibus F-test and post-hoc comparisons_en.vtt |
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| 005 The omnibus F-test and post-hoc comparisons.mp4 |
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| 005 Two-samples t-test_en.srt |
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| 005 Two-samples t-test_en.vtt |
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| 005 Two-samples t-test.mp4 |
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| 006 _Unsupervised learning__ Compute R2 and F_en.srt |
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| 006 _Unsupervised learning__ Compute R2 and F_en.vtt |
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| 006 _Unsupervised learning__ Compute R2 and F.mp4 |
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| 006 _Unsupervised learning__ Confidence intervals for variance_en.srt |
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| 006 _Unsupervised learning__ Confidence intervals for variance_en.vtt |
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| 006 _Unsupervised learning__ Confidence intervals for variance.mp4 |
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| 006 _Unsupervised learning__ Invert the min-max scaling_en.srt |
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| 006 _Unsupervised learning__ Invert the min-max scaling_en.vtt |
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| 006 _Unsupervised learning__ Invert the min-max scaling.mp4 |
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| 006 _Unsupervised learning__ probabilities of odds-space_en.srt |
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| 006 _Unsupervised learning__ probabilities of odds-space_en.vtt |
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| 006 _Unsupervised learning__ probabilities of odds-space.mp4 |
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| 006 Code_ correlation matrix_en.srt |
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| 006 Code_ correlation matrix_en.vtt |
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| 006 Code_ correlation matrix.mp4 |
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| 006 Code_ dbscan_en.srt |
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| 006 Code_ dbscan_en.vtt |
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| 006 Code_ dbscan.mp4 |
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| 006 Code_ Two-samples t-test_en.srt |
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| 006 Code_ Two-samples t-test_en.vtt |
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| 006 Code_ Two-samples t-test.mp4 |
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| 006 Degrees of freedom_en.srt |
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| 006 Degrees of freedom_en.vtt |
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| 006 Degrees of freedom.mp4 |
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| 006 F-score_en.srt |
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| 006 F-score_en.vtt |
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| 006 F-score.mp4 |
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| 006 Histograms_en.srt |
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| 006 Histograms_en.vtt |
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| 006 Histograms.mp4 |
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| 006 MATLAB_ Inferential statistics_en.srt |
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| 006 MATLAB_ Inferential statistics_en.vtt |
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| 006 MATLAB_ Inferential statistics.mp4 |
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| 006 Natural exponent and logarithm_en.srt |
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| 006 Natural exponent and logarithm_en.vtt |
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| 006 Natural exponent and logarithm.mp4 |
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| 006 Samples, case reports, and anecdotes_en.srt |
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| 006 Samples, case reports, and anecdotes_en.vtt |
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| 006 Samples, case reports, and anecdotes.mp4 |
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| 006 The beauty and simplicity of Normal_en.srt |
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| 006 The beauty and simplicity of Normal_en.vtt |
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| 006 The beauty and simplicity of Normal.mp4 |
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| 006 The two-way ANOVA_en.srt |
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| 006 The two-way ANOVA_en.vtt |
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| 006 The two-way ANOVA.mp4 |
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| 007 _Unsupervised learning__ average correlation matrices_en.srt |
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| 007 _Unsupervised learning__ average correlation matrices_en.vtt |
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| 007 _Unsupervised learning__ average correlation matrices.mp4 |
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| 007 _Unsupervised learning__ dbscan vs. k-means_en.srt |
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| 007 _Unsupervised learning__ dbscan vs. k-means_en.vtt |
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| 007 _Unsupervised learning__ dbscan vs. k-means.mp4 |
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| 007 _Unsupervised learning__ Importance of N for t-test_en.srt |
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| 007 _Unsupervised learning__ Importance of N for t-test_en.vtt |
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| 007 _Unsupervised learning__ Importance of N for t-test.mp4 |
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| 007 Code_ histograms_en.srt |
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| 007 Code_ histograms_en.vtt |
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| 007 Code_ histograms.mp4 |
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| 007 Measures of central tendency (mean)_en.srt |
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| 007 Measures of central tendency (mean)_en.vtt |
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| 007 Measures of central tendency (mean).mp4 |
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| 007 Misconceptions about confidence intervals_en.srt |
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| 007 Misconceptions about confidence intervals_en.vtt |
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| 007 Misconceptions about confidence intervals.mp4 |
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| 007 Multiple regression_en.srt |
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| 007 Multiple regression_en.vtt |
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| 007 Multiple regression.mp4 |
45.14Мб |
| 007 One-way ANOVA example_en.srt |
20.60Кб |
| 007 One-way ANOVA example_en.vtt |
17.68Кб |
| 007 One-way ANOVA example.mp4 |
44.32Мб |
| 007 Probability mass vs. density_en.srt |
18.42Кб |
| 007 Probability mass vs. density_en.vtt |
15.95Кб |
| 007 Probability mass vs. density.mp4 |
134.14Мб |
| 007 Python_ Import and clean the marriage data_en.srt |
29.28Кб |
| 007 Python_ Import and clean the marriage data_en.vtt |
25.48Кб |
| 007 Python_ Import and clean the marriage data.mp4 |
249.82Мб |
| 007 Receiver operating characteristics (ROC)_en.srt |
10.95Кб |
| 007 Receiver operating characteristics (ROC)_en.vtt |
9.58Кб |
| 007 Receiver operating characteristics (ROC).mp4 |
64.37Мб |
| 007 The ethics of making up data_en.srt |
10.29Кб |
| 007 The ethics of making up data_en.vtt |
8.90Кб |
| 007 The ethics of making up data.mp4 |
19.65Мб |
| 007 The logistic function_en.srt |
13.12Кб |
| 007 The logistic function_en.vtt |
11.28Кб |
| 007 The logistic function.mp4 |
17.90Мб |
| 007 Type 1 and Type 2 errors_en.srt |
22.20Кб |
| 007 Type 1 and Type 2 errors_en.vtt |
19.01Кб |
| 007 Type 1 and Type 2 errors.mp4 |
45.90Мб |
| 007 What are outliers and why are they dangerous__en.srt |
21.54Кб |
| 007 What are outliers and why are they dangerous__en.vtt |
18.52Кб |
| 007 What are outliers and why are they dangerous_.mp4 |
43.00Мб |
| 008 _Unsupervised learning__ correlation to covariance matrix_en.srt |
5.81Кб |
| 008 _Unsupervised learning__ correlation to covariance matrix_en.vtt |
5.12Кб |
| 008 _Unsupervised learning__ correlation to covariance matrix.mp4 |
10.13Мб |
| 008 _Unsupervised learning__ Histogram proportion_en.srt |
3.40Кб |
| 008 _Unsupervised learning__ Histogram proportion_en.vtt |
2.98Кб |
| 008 _Unsupervised learning__ Histogram proportion.mp4 |
11.79Мб |
| 008 Code_ compute probability mass functions_en.srt |
16.01Кб |
| 008 Code_ compute probability mass functions_en.vtt |
14.03Кб |
| 008 Code_ compute probability mass functions.mp4 |
66.17Мб |
| 008 Code_ One-way ANOVA (independent samples)_en.srt |
25.72Кб |
| 008 Code_ One-way ANOVA (independent samples)_en.vtt |
21.93Кб |
| 008 Code_ One-way ANOVA (independent samples).mp4 |
172.72Мб |
| 008 Code_ ROC curves_en.srt |
11.67Кб |
| 008 Code_ ROC curves_en.vtt |
10.14Кб |
| 008 Code_ ROC curves.mp4 |
54.62Мб |
| 008 K-nearest neighbor classification_en.srt |
8.99Кб |
| 008 K-nearest neighbor classification_en.vtt |
7.84Кб |
| 008 K-nearest neighbor classification.mp4 |
12.47Мб |
| 008 Measures of central tendency (median, mode)_en.srt |
18.21Кб |
| 008 Measures of central tendency (median, mode)_en.vtt |
15.71Кб |
| 008 Measures of central tendency (median, mode).mp4 |
34.26Мб |
| 008 Parametric vs. non-parametric tests_en.srt |
12.87Кб |
| 008 Parametric vs. non-parametric tests_en.vtt |
11.30Кб |
| 008 Parametric vs. non-parametric tests.mp4 |
87.45Мб |
| 008 Python_ Import the divorce data_en.srt |
18.53Кб |
| 008 Python_ Import the divorce data_en.vtt |
16.08Кб |
| 008 Python_ Import the divorce data.mp4 |
137.14Мб |
| 008 Rank and tied-rank_en.srt |
9.55Кб |
| 008 Rank and tied-rank_en.vtt |
8.22Кб |
| 008 Rank and tied-rank.mp4 |
12.92Мб |
| 008 Removing outliers_ z-score method_en.srt |
14.14Кб |
| 008 Removing outliers_ z-score method_en.vtt |
12.21Кб |
| 008 Removing outliers_ z-score method.mp4 |
33.51Мб |
| 008 Standardizing regression coefficients_en.srt |
18.34Кб |
| 008 Standardizing regression coefficients_en.vtt |
15.73Кб |
| 008 Standardizing regression coefficients.mp4 |
75.19Мб |
| 008 Wilcoxon signed-rank (nonparametric t-test)_en.srt |
10.43Кб |
| 008 Wilcoxon signed-rank (nonparametric t-test)_en.vtt |
9.09Кб |
| 008 Wilcoxon signed-rank (nonparametric t-test).mp4 |
25.98Мб |
| 009 _Unsupervised learning__ Make this plot look nicer__en.srt |
2.35Кб |
| 009 _Unsupervised learning__ Make this plot look nicer__en.vtt |
2.06Кб |
| 009 _Unsupervised learning__ Make this plot look nicer_.mp4 |
11.50Мб |
| 009 Code_ computing central tendency_en.srt |
20.14Кб |
| 009 Code_ computing central tendency_en.vtt |
17.41Кб |
| 009 Code_ computing central tendency.mp4 |
66.60Мб |
| 009 Code_ KNN_en.srt |
18.21Кб |
| 009 Code_ KNN_en.vtt |
15.49Кб |
| 009 Code_ KNN.mp4 |
108.34Мб |
| 009 Code_ Multiple regression_en.srt |
27.91Кб |
| 009 Code_ Multiple regression_en.vtt |
23.90Кб |
| 009 Code_ Multiple regression.mp4 |
170.95Мб |
| 009 Code_ One-way repeated-measures ANOVA_en.srt |
18.38Кб |
| 009 Code_ One-way repeated-measures ANOVA_en.vtt |
15.87Кб |
| 009 Code_ One-way repeated-measures ANOVA.mp4 |
73.10Мб |
| 009 Code_ Signed-rank test_en.srt |
26.90Кб |
| 009 Code_ Signed-rank test_en.vtt |
23.02Кб |
| 009 Code_ Signed-rank test.mp4 |
161.85Мб |
| 009 Cumulative distribution functions_en.srt |
20.37Кб |
| 009 Cumulative distribution functions_en.vtt |
17.73Кб |
| 009 Cumulative distribution functions.mp4 |
45.41Мб |
| 009 Multiple comparisons and Bonferroni correction_en.srt |
12.50Кб |
| 009 Multiple comparisons and Bonferroni correction_en.vtt |
10.79Кб |
| 009 Multiple comparisons and Bonferroni correction.mp4 |
29.56Мб |
| 009 Partial correlation_en.srt |
15.42Кб |
| 009 Partial correlation_en.vtt |
13.36Кб |
| 009 Partial correlation.mp4 |
59.34Мб |
| 009 Pie charts_en.srt |
8.47Кб |
| 009 Pie charts_en.vtt |
7.33Кб |
| 009 Pie charts.mp4 |
16.53Мб |
| 009 Python_ Inferential statistics_en.srt |
16.08Кб |
| 009 Python_ Inferential statistics_en.vtt |
14.05Кб |
| 009 Python_ Inferential statistics.mp4 |
115.54Мб |
| 009 The modified z-score method_en.srt |
5.90Кб |
| 009 The modified z-score method_en.vtt |
5.12Кб |
| 009 The modified z-score method.mp4 |
9.62Мб |
| 010 _Unsupervised learning__ central tendencies with outliers_en.srt |
4.31Кб |
| 010 _Unsupervised learning__ central tendencies with outliers_en.vtt |
3.78Кб |
| 010 _Unsupervised learning__ central tendencies with outliers.mp4 |
16.74Мб |
| 010 Code_ cdfs and pdfs_en.srt |
14.49Кб |
| 010 Code_ cdfs and pdfs_en.vtt |
12.56Кб |
| 010 Code_ cdfs and pdfs.mp4 |
95.94Мб |
| 010 Code_ partial correlation_en.srt |
29.41Кб |
| 010 Code_ partial correlation_en.vtt |
25.23Кб |
| 010 Code_ partial correlation.mp4 |
108.27Мб |
| 010 Code_ pie charts_en.srt |
19.38Кб |
| 010 Code_ pie charts_en.vtt |
16.66Кб |
| 010 Code_ pie charts.mp4 |
78.92Мб |
| 010 Code_ z-score for outlier removal_en.srt |
33.64Кб |
| 010 Code_ z-score for outlier removal_en.vtt |
28.78Кб |
| 010 Code_ z-score for outlier removal.mp4 |
136.89Мб |
| 010 Mann-Whitney U test (nonparametric t-test)_en.srt |
8.84Кб |
| 010 Mann-Whitney U test (nonparametric t-test)_en.vtt |
7.66Кб |
| 010 Mann-Whitney U test (nonparametric t-test).mp4 |
20.32Мб |
| 010 Polynomial regression models_en.srt |
12.22Кб |
| 010 Polynomial regression models_en.vtt |
10.65Кб |
| 010 Polynomial regression models.mp4 |
48.15Мб |
| 010 Principal components analysis (PCA)_en.srt |
23.28Кб |
| 010 Principal components analysis (PCA)_en.vtt |
20.26Кб |
| 010 Principal components analysis (PCA).mp4 |
42.56Мб |
| 010 Statistical vs. theoretical vs. clinical significance_en.srt |
9.98Кб |
| 010 Statistical vs. theoretical vs. clinical significance_en.vtt |
8.64Кб |
| 010 Statistical vs. theoretical vs. clinical significance.mp4 |
19.08Мб |
| 010 Take-home messages_en.srt |
8.72Кб |
| 010 Take-home messages_en.vtt |
7.60Кб |
| 010 Take-home messages.mp4 |
43.80Мб |
| 010 Two-way ANOVA example_en.srt |
16.08Кб |
| 010 Two-way ANOVA example_en.vtt |
14.00Кб |
| 010 Two-way ANOVA example.mp4 |
35.95Мб |
| 011 _Unsupervised learning__ cdf's for various distributions_en.srt |
3.32Кб |
| 011 _Unsupervised learning__ cdf's for various distributions_en.vtt |
2.94Кб |
| 011 _Unsupervised learning__ cdf's for various distributions.mp4 |
9.31Мб |
| 011 _Unsupervised learning__ z vs. modified-z_en.srt |
3.84Кб |
| 011 _Unsupervised learning__ z vs. modified-z_en.vtt |
3.36Кб |
| 011 _Unsupervised learning__ z vs. modified-z.mp4 |
9.02Мб |
| 011 Code_ Mann-Whitney U test_en.srt |
7.75Кб |
| 011 Code_ Mann-Whitney U test_en.vtt |
6.70Кб |
| 011 Code_ Mann-Whitney U test.mp4 |
52.05Мб |
| 011 Code_ PCA_en.srt |
26.52Кб |
| 011 Code_ PCA_en.vtt |
22.60Кб |
| 011 Code_ PCA.mp4 |
175.10Мб |
| 011 Code_ polynomial modeling_en.srt |
22.44Кб |
| 011 Code_ polynomial modeling_en.vtt |
19.30Кб |
| 011 Code_ polynomial modeling.mp4 |
129.08Мб |
| 011 Code_ Two-way mixed ANOVA_en.srt |
21.44Кб |
| 011 Code_ Two-way mixed ANOVA_en.vtt |
18.37Кб |
| 011 Code_ Two-way mixed ANOVA.mp4 |
114.16Мб |
| 011 Cross-validation_en.srt |
16.44Кб |
| 011 Cross-validation_en.vtt |
14.32Кб |
| 011 Cross-validation.mp4 |
28.25Мб |
| 011 Measures of dispersion (variance, standard deviation)_en.srt |
26.26Кб |
| 011 Measures of dispersion (variance, standard deviation)_en.vtt |
22.56Кб |
| 011 Measures of dispersion (variance, standard deviation).mp4 |
54.12Мб |
| 011 The problem with Pearson_en.srt |
9.89Кб |
| 011 The problem with Pearson_en.vtt |
8.65Кб |
| 011 The problem with Pearson.mp4 |
16.57Мб |
| 011 When to use lines instead of bars_en.srt |
8.62Кб |
| 011 When to use lines instead of bars_en.vtt |
7.47Кб |
| 011 When to use lines instead of bars.mp4 |
17.98Мб |
| 012 _Unsupervised learning__ K-means on PC data_en.srt |
2.21Кб |
| 012 _Unsupervised learning__ K-means on PC data_en.vtt |
1.95Кб |
| 012 _Unsupervised learning__ K-means on PC data.mp4 |
11.52Мб |
| 012 _Unsupervised learning__ Polynomial design matrix_en.srt |
1.11Кб |
| 012 _Unsupervised learning__ Polynomial design matrix_en.vtt |
1010б |
| 012 _Unsupervised learning__ Polynomial design matrix.mp4 |
4.74Мб |
| 012 Code_ Computing dispersion_en.srt |
37.23Кб |
| 012 Code_ Computing dispersion_en.vtt |
32.31Кб |
| 012 Code_ Computing dispersion.mp4 |
266.09Мб |
| 012 Creating sample estimate distributions_en.srt |
27.73Кб |
| 012 Creating sample estimate distributions_en.vtt |
23.83Кб |
| 012 Creating sample estimate distributions.mp4 |
124.85Мб |
| 012 Linear vs. logarithmic axis scaling_en.srt |
12.48Кб |
| 012 Linear vs. logarithmic axis scaling_en.vtt |
10.79Кб |
| 012 Linear vs. logarithmic axis scaling.mp4 |
25.64Мб |
| 012 Multivariate outlier detection_en.srt |
14.35Кб |
| 012 Multivariate outlier detection_en.vtt |
12.28Кб |
| 012 Multivariate outlier detection.mp4 |
25.05Мб |
| 012 Nonparametric correlation_ Spearman rank_en.srt |
10.73Кб |
| 012 Nonparametric correlation_ Spearman rank_en.vtt |
9.30Кб |
| 012 Nonparametric correlation_ Spearman rank.mp4 |
23.72Мб |
| 012 Permutation testing for t-test significance_en.srt |
16.36Кб |
| 012 Permutation testing for t-test significance_en.vtt |
14.20Кб |
| 012 Permutation testing for t-test significance.mp4 |
63.48Мб |
| 012 Statistical significance vs. classification accuracy_en.srt |
17.02Кб |
| 012 Statistical significance vs. classification accuracy_en.vtt |
14.68Кб |
| 012 Statistical significance vs. classification accuracy.mp4 |
42.50Мб |
| 013 Code_ Euclidean distance for outlier removal_en.srt |
12.77Кб |
| 013 Code_ Euclidean distance for outlier removal_en.vtt |
11.04Кб |
| 013 Code_ Euclidean distance for outlier removal.mp4 |
43.72Мб |
| 013 Code_ line plots_en.srt |
10.87Кб |
| 013 Code_ line plots_en.vtt |
9.41Кб |
| 013 Code_ line plots.mp4 |
37.29Мб |
| 013 Code_ permutation testing_en.srt |
37.10Кб |
| 013 Code_ permutation testing_en.vtt |
31.77Кб |
| 013 Code_ permutation testing.mp4 |
240.90Мб |
| 013 Fisher-Z transformation for correlations_en.srt |
9.87Кб |
| 013 Fisher-Z transformation for correlations_en.vtt |
8.63Кб |
| 013 Fisher-Z transformation for correlations.mp4 |
28.48Мб |
| 013 Independent components analysis (ICA)_en.srt |
17.25Кб |
| 013 Independent components analysis (ICA)_en.vtt |
15.07Кб |
| 013 Independent components analysis (ICA).mp4 |
45.52Мб |
| 013 Interquartile range (IQR)_en.srt |
7.01Кб |
| 013 Interquartile range (IQR)_en.vtt |
6.10Кб |
| 013 Interquartile range (IQR).mp4 |
9.84Мб |
| 013 Logistic regression_en.srt |
25.46Кб |
| 013 Logistic regression_en.vtt |
21.81Кб |
| 013 Logistic regression.mp4 |
52.70Мб |
| 013 Monte Carlo sampling_en.srt |
3.82Кб |
| 013 Monte Carlo sampling_en.vtt |
3.35Кб |
| 013 Monte Carlo sampling.mp4 |
8.83Мб |
| 014 _Unsupervised learning__ How many permutations__en.srt |
7.74Кб |
| 014 _Unsupervised learning__ How many permutations__en.vtt |
6.73Кб |
| 014 _Unsupervised learning__ How many permutations_.mp4 |
32.50Мб |
| 014 _Unsupervised learning__ log-scaled plots_en.srt |
2.47Кб |
| 014 _Unsupervised learning__ log-scaled plots_en.vtt |
2.14Кб |
| 014 _Unsupervised learning__ log-scaled plots.mp4 |
3.73Мб |
| 014 Code_ ICA_en.srt |
18.44Кб |
| 014 Code_ ICA_en.vtt |
15.90Кб |
| 014 Code_ ICA.mp4 |
73.36Мб |
| 014 Code_ IQR_en.srt |
23.44Кб |
| 014 Code_ IQR_en.vtt |
20.15Кб |
| 014 Code_ IQR.mp4 |
83.39Мб |
| 014 Code_ Logistic regression_en.srt |
14.18Кб |
| 014 Code_ Logistic regression_en.vtt |
12.14Кб |
| 014 Code_ Logistic regression.mp4 |
81.23Мб |
| 014 Code_ Spearman correlation and Fisher-Z_en.srt |
11.10Кб |
| 014 Code_ Spearman correlation and Fisher-Z_en.vtt |
9.61Кб |
| 014 Code_ Spearman correlation and Fisher-Z.mp4 |
42.71Мб |
| 014 Removing outliers by data trimming_en.srt |
8.53Кб |
| 014 Removing outliers by data trimming_en.vtt |
7.40Кб |
| 014 Removing outliers by data trimming.mp4 |
16.90Мб |
| 014 Sampling variability, noise, and other annoyances_en.srt |
13.06Кб |
| 014 Sampling variability, noise, and other annoyances_en.vtt |
11.37Кб |
| 014 Sampling variability, noise, and other annoyances.mp4 |
106.08Мб |
| 015 _Unsupervised learning__ Spearman correlation_en.srt |
1.86Кб |
| 015 _Unsupervised learning__ Spearman correlation_en.vtt |
1.64Кб |
| 015 _Unsupervised learning__ Spearman correlation.mp4 |
15.95Мб |
| 015 Code_ Data trimming to remove outliers_en.srt |
16.30Кб |
| 015 Code_ Data trimming to remove outliers_en.vtt |
14.05Кб |
| 015 Code_ Data trimming to remove outliers.mp4 |
65.29Мб |
| 015 Code_ sampling variability_en.srt |
38.25Кб |
| 015 Code_ sampling variability_en.vtt |
32.90Кб |
| 015 Code_ sampling variability.mp4 |
154.75Мб |
| 015 QQ plots_en.srt |
10.18Кб |
| 015 QQ plots_en.vtt |
8.88Кб |
| 015 QQ plots.mp4 |
16.22Мб |
| 015 Under- and over-fitting_en.srt |
25.37Кб |
| 015 Under- and over-fitting_en.vtt |
21.71Кб |
| 015 Under- and over-fitting.mp4 |
120.86Мб |
| 016 _Unsupervised learning__ confidence interval on correlation_en.srt |
3.32Кб |
| 016 _Unsupervised learning__ confidence interval on correlation_en.vtt |
2.93Кб |
| 016 _Unsupervised learning__ confidence interval on correlation.mp4 |
10.31Мб |
| 016 _Unsupervised learning__ Overfit data_en.srt |
2.69Кб |
| 016 _Unsupervised learning__ Overfit data_en.vtt |
2.36Кб |
| 016 _Unsupervised learning__ Overfit data.mp4 |
4.82Мб |
| 016 Code_ QQ plots_en.srt |
23.48Кб |
| 016 Code_ QQ plots_en.vtt |
20.10Кб |
| 016 Code_ QQ plots.mp4 |
90.30Мб |
| 016 Expected value_en.srt |
15.37Кб |
| 016 Expected value_en.vtt |
13.21Кб |
| 016 Expected value.mp4 |
59.63Мб |
| 016 Non-parametric solutions to outliers_en.srt |
6.34Кб |
| 016 Non-parametric solutions to outliers_en.vtt |
5.57Кб |
| 016 Non-parametric solutions to outliers.mp4 |
22.96Мб |
| 017 Comparing _nested_ models_en.srt |
17.28Кб |
| 017 Comparing _nested_ models_en.vtt |
15.11Кб |
| 017 Comparing _nested_ models.mp4 |
39.07Мб |
| 017 Conditional probability_en.srt |
18.81Кб |
| 017 Conditional probability_en.vtt |
16.12Кб |
| 017 Conditional probability.mp4 |
85.68Мб |
| 017 Kendall's correlation for ordinal data_en.srt |
15.22Кб |
| 017 Kendall's correlation for ordinal data_en.vtt |
13.13Кб |
| 017 Kendall's correlation for ordinal data.mp4 |
30.15Мб |
| 017 Nonlinear data transformations_en.srt |
19.80Кб |
| 017 Nonlinear data transformations_en.vtt |
17.38Кб |
| 017 Nonlinear data transformations.mp4 |
33.69Мб |
| 017 Statistical _moments__en.srt |
13.06Кб |
| 017 Statistical _moments__en.vtt |
11.16Кб |
| 017 Statistical _moments_.mp4 |
21.68Мб |
| 018 An outlier lecture on personal accountability_en.srt |
4.12Кб |
| 018 An outlier lecture on personal accountability_en.vtt |
3.65Кб |
| 018 An outlier lecture on personal accountability.mp4 |
17.70Мб |
| 018 Code_ conditional probabilities_en.srt |
29.61Кб |
| 018 Code_ conditional probabilities_en.vtt |
25.37Кб |
| 018 Code_ conditional probabilities.mp4 |
115.08Мб |
| 018 Code_ Kendall correlation_en.srt |
17.59Кб |
| 018 Code_ Kendall correlation_en.vtt |
22.96Кб |
| 018 Code_ Kendall correlation.mp4 |
184.22Мб |
| 018 Histograms part 2_ Number of bins_en.srt |
14.32Кб |
| 018 Histograms part 2_ Number of bins_en.vtt |
12.41Кб |
| 018 Histograms part 2_ Number of bins.mp4 |
23.50Мб |
| 018 What to do about missing data_en.srt |
9.59Кб |
| 018 What to do about missing data_en.vtt |
8.36Кб |
| 018 What to do about missing data.mp4 |
16.05Мб |
| 019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.srt |
3.31Кб |
| 019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.vtt |
2.95Кб |
| 019 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4 |
14.95Мб |
| 019 Code_ Histogram bins_en.srt |
17.86Кб |
| 019 Code_ Histogram bins_en.vtt |
15.40Кб |
| 019 Code_ Histogram bins.mp4 |
118.12Мб |
| 019 Tree diagrams for conditional probabilities_en.srt |
9.92Кб |
| 019 Tree diagrams for conditional probabilities_en.vtt |
8.57Кб |
| 019 Tree diagrams for conditional probabilities.mp4 |
13.50Мб |
| 020 The Law of Large Numbers_en.srt |
14.41Кб |
| 020 The Law of Large Numbers_en.vtt |
12.47Кб |
| 020 The Law of Large Numbers.mp4 |
40.55Мб |
| 020 The subgroups correlation paradox_en.srt |
6.98Кб |
| 020 The subgroups correlation paradox_en.vtt |
6.13Кб |
| 020 The subgroups correlation paradox.mp4 |
21.57Мб |
| 020 Violin plots_en.srt |
4.98Кб |
| 020 Violin plots_en.vtt |
4.30Кб |
| 020 Violin plots.mp4 |
6.47Мб |
| 021 Code_ Law of Large Numbers in action_en.srt |
27.83Кб |
| 021 Code_ Law of Large Numbers in action_en.vtt |
23.84Кб |
| 021 Code_ Law of Large Numbers in action.mp4 |
165.60Мб |
| 021 Code_ violin plots_en.srt |
15.42Кб |
| 021 Code_ violin plots_en.vtt |
13.19Кб |
| 021 Code_ violin plots.mp4 |
104.96Мб |
| 021 Cosine similarity_en.srt |
7.49Кб |
| 021 Cosine similarity_en.vtt |
6.54Кб |
| 021 Cosine similarity.mp4 |
14.20Мб |
| 022 _Unsupervised learning__ asymmetric violin plots_en.srt |
3.84Кб |
| 022 _Unsupervised learning__ asymmetric violin plots_en.vtt |
3.31Кб |
| 022 _Unsupervised learning__ asymmetric violin plots.mp4 |
17.32Мб |
| 022 Code_ Cosine similarity vs. Pearson correlation_en.srt |
31.27Кб |
| 022 Code_ Cosine similarity vs. Pearson correlation_en.vtt |
26.93Кб |
| 022 Code_ Cosine similarity vs. Pearson correlation.mp4 |
102.19Мб |
| 022 The Central Limit Theorem_en.srt |
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| 022 The Central Limit Theorem_en.vtt |
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| 022 The Central Limit Theorem.mp4 |
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| 023 Code_ the CLT in action_en.srt |
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| 023 Shannon entropy_en.srt |
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| 023 Shannon entropy_en.vtt |
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| 024 _Unsupervised learning__ Averaging pairs of numbers_en.srt |
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| 024 _Unsupervised learning__ Averaging pairs of numbers_en.vtt |
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| 024 Code_ entropy_en.srt |
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| 025 _Unsupervised learning__ entropy and number of bins_en.srt |
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| 025 _Unsupervised learning__ entropy and number of bins.mp4 |
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| 25299297-stats-intro-GuessTheTest.zip |
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| 35855730-state-marriage-rates-90-95-99-19.xlsx |
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