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| [CourseClub.Me].url |
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| [GigaCourse.Com].url |
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| 001 [Important] Getting the most out of this course_en.srt |
6.08KB |
| 001 [Important] Getting the most out of this course_en.vtt |
5.36KB |
| 001 [Important] Getting the most out of this course.mp4 |
38.26MB |
| 001 About deep learning.html |
619B |
| 001 ANOVA intro, part1_en.srt |
26.20KB |
| 001 ANOVA intro, part1_en.vtt |
22.63KB |
| 001 ANOVA intro, part1.mp4 |
137.72MB |
| 001 Bar plots_en.srt |
17.04KB |
| 001 Bar plots_en.vtt |
15.28KB |
| 001 Bar plots.mp4 |
36.83MB |
| 001 Descriptive vs. inferential statistics_en.srt |
6.38KB |
| 001 Descriptive vs. inferential statistics_en.vtt |
5.58KB |
| 001 Descriptive vs. inferential statistics.mp4 |
21.48MB |
| 001 Download materials for the entire course__en.srt |
5.42KB |
| 001 Download materials for the entire course__en.vtt |
4.77KB |
| 001 Download materials for the entire course_.mp4 |
14.46MB |
| 001 Garbage in, garbage out (GIGO)_en.srt |
5.69KB |
| 001 Garbage in, garbage out (GIGO)_en.vtt |
5.00KB |
| 001 Garbage in, garbage out (GIGO).mp4 |
11.55MB |
| 001 Introduction to GLM _ regression_en.srt |
29.73KB |
| 001 Introduction to GLM _ regression_en.vtt |
25.51KB |
| 001 Introduction to GLM _ regression.mp4 |
61.97MB |
| 001 Is _data_ singular or plural________en.srt |
2.33KB |
| 001 Is _data_ singular or plural________en.vtt |
2.02KB |
| 001 Is _data_ singular or plural_______.mp4 |
10.92MB |
| 001 IVs, DVs, models, and other stats lingo_en.srt |
24.30KB |
| 001 IVs, DVs, models, and other stats lingo_en.vtt |
20.93KB |
| 001 IVs, DVs, models, and other stats lingo.mp4 |
91.14MB |
| 001 K-means clustering_en.srt |
21.01KB |
| 001 K-means clustering_en.vtt |
18.08KB |
| 001 K-means clustering.mp4 |
54.29MB |
| 001 Motivation and description of correlation_en.srt |
27.37KB |
| 001 Motivation and description of correlation_en.vtt |
23.58KB |
| 001 Motivation and description of correlation.mp4 |
118.43MB |
| 001 Note about the code for this section.html |
135B |
| 001 Purpose and interpretation of the t-test_en.srt |
18.92KB |
| 001 Purpose and interpretation of the t-test_en.vtt |
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| 001 Purpose and interpretation of the t-test.mp4 |
32.16MB |
| 001 Should you memorize statistical formulas__en.srt |
4.17KB |
| 001 Should you memorize statistical formulas__en.vtt |
3.69KB |
| 001 Should you memorize statistical formulas_.mp4 |
28.00MB |
| 001 The two perspectives of the world_en.srt |
8.71KB |
| 001 The two perspectives of the world_en.vtt |
7.52KB |
| 001 The two perspectives of the world.mp4 |
13.91MB |
| 001 What are confidence intervals and why do we need them__en.srt |
13.12KB |
| 001 What are confidence intervals and why do we need them__en.vtt |
11.33KB |
| 001 What are confidence intervals and why do we need them_.mp4 |
29.83MB |
| 001 What is probability__en.srt |
17.94KB |
| 001 What is probability__en.vtt |
15.49KB |
| 001 What is probability_.mp4 |
41.11MB |
| 001 What is statistical power and why is it important__en.srt |
14.33KB |
| 001 What is statistical power and why is it important__en.vtt |
12.50KB |
| 001 What is statistical power and why is it important_.mp4 |
39.53MB |
| 002 About using MATLAB or Python_en.srt |
5.93KB |
| 002 About using MATLAB or Python_en.vtt |
5.17KB |
| 002 About using MATLAB or Python.mp4 |
27.11MB |
| 002 Accuracy, precision, resolution_en.srt |
11.39KB |
| 002 Accuracy, precision, resolution_en.vtt |
9.81KB |
| 002 Accuracy, precision, resolution.mp4 |
25.42MB |
| 002 ANOVA intro, part 2_en.srt |
28.45KB |
| 002 ANOVA intro, part 2_en.vtt |
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| 002 ANOVA intro, part 2.mp4 |
84.25MB |
| 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 |
7.55MB |
| 002 Bonus content.html |
3.64KB |
| 002 Code_ bar plots_en.srt |
25.39KB |
| 002 Code_ bar plots_en.vtt |
22.66KB |
| 002 Code_ bar plots.mp4 |
100.03MB |
| 002 Code_ k-means clustering_en.srt |
34.34KB |
| 002 Code_ k-means clustering_en.vtt |
29.39KB |
| 002 Code_ k-means clustering.mp4 |
230.34MB |
| 002 Computing confidence intervals via formula_en.srt |
9.42KB |
| 002 Computing confidence intervals via formula_en.vtt |
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| 002 Computing confidence intervals via formula.mp4 |
17.33MB |
| 002 Covariance and correlation_ formulas_en.srt |
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| 002 Covariance and correlation_ formulas_en.vtt |
17.86KB |
| 002 Covariance and correlation_ formulas.mp4 |
41.85MB |
| 002 d-prime_en.srt |
19.21KB |
| 002 d-prime_en.vtt |
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| 002 d-prime.mp4 |
34.14MB |
| 002 Estimating statistical power and sample size_en.srt |
16.57KB |
| 002 Estimating statistical power and sample size_en.vtt |
14.35KB |
| 002 Estimating statistical power and sample size.mp4 |
36.16MB |
| 002 Introduction_en.srt |
6.23KB |
| 002 Introduction_en.vtt |
5.44KB |
| 002 Introduction.mp4 |
53.02MB |
| 002 Least-squares solution to the GLM_en.srt |
14.33KB |
| 002 Least-squares solution to the GLM_en.vtt |
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| 002 Least-squares solution to the GLM.mp4 |
41.41MB |
| 002 One-sample t-test_en.srt |
11.59KB |
| 002 One-sample t-test_en.vtt |
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| 002 One-sample t-test.mp4 |
53.95MB |
| 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 |
37.52MB |
| 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 |
49.12MB |
| 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 |
35.54MB |
| 002 Z-score standardization_en.srt |
14.29KB |
| 002 Z-score standardization_en.vtt |
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| 002 Z-score standardization.mp4 |
36.23MB |
| 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 |
12.91MB |
| 003 Box-and-whisker plots_en.srt |
7.82KB |
| 003 Box-and-whisker plots_en.vtt |
6.79KB |
| 003 Box-and-whisker plots.mp4 |
11.12MB |
| 003 Code_ compute confidence intervals by formula_en.srt |
25.68KB |
| 003 Code_ compute confidence intervals by formula_en.vtt |
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| 003 Code_ compute confidence intervals by formula.mp4 |
94.29MB |
| 003 Code_ correlation coefficient_en.srt |
40.44KB |
| 003 Code_ correlation coefficient_en.vtt |
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| 003 Code_ correlation coefficient.mp4 |
214.14MB |
| 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 |
69.50MB |
| 003 Code_ One-sample t-test_en.srt |
31.24KB |
| 003 Code_ One-sample t-test_en.vtt |
26.62KB |
| 003 Code_ One-sample t-test.mp4 |
157.96MB |
| 003 Code_ z-score_en.srt |
19.26KB |
| 003 Code_ z-score_en.vtt |
16.61KB |
| 003 Code_ z-score.mp4 |
66.77MB |
| 003 Compute power and sample size using G_Power_en.srt |
6.82KB |
| 003 Compute power and sample size using G_Power_en.vtt |
5.80KB |
| 003 Compute power and sample size using G_Power.mp4 |
31.20MB |
| 003 Computing probabilities_en.srt |
15.15KB |
| 003 Computing probabilities_en.vtt |
13.08KB |
| 003 Computing probabilities.mp4 |
37.52MB |
| 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 |
20.49KB |
| 003 Evaluating regression models_ R2 and F.mp4 |
38.06MB |
| 003 MATLAB_ Import and clean the marriage data_en.srt |
23.57KB |
| 003 MATLAB_ Import and clean the marriage data_en.vtt |
20.52KB |
| 003 MATLAB_ Import and clean the marriage data.mp4 |
201.29MB |
| 003 Sample distributions under null and alternative hypotheses_en.srt |
14.68KB |
| 003 Sample distributions under null and alternative hypotheses_en.vtt |
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| 003 Sample distributions under null and alternative hypotheses.mp4 |
43.75MB |
| 003 Scientific notation_en.srt |
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| 003 Scientific notation_en.vtt |
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| 003 Scientific notation.mp4 |
12.87MB |
| 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 |
45.88MB |
| 003 Types of data_ categorical, numerical, etc_en.srt |
20.93KB |
| 003 Types of data_ categorical, numerical, etc_en.vtt |
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| 003 Types of data_ categorical, numerical, etc.mp4 |
59.37MB |
| 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 |
7.94MB |
| 004 _Unsupervised learning__ The role of variance_en.srt |
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| 004 _Unsupervised learning__ The role of variance_en.vtt |
3.58KB |
| 004 _Unsupervised learning__ The role of variance.mp4 |
28.65MB |
| 004 Code_ box plots_en.srt |
12.75KB |
| 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 |
148.40MB |
| 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 |
303.11MB |
| 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 |
70.12MB |
| 004 Confidence intervals via bootstrapping (resampling)_en.srt |
12.83KB |
| 004 Confidence intervals via bootstrapping (resampling)_en.vtt |
11.18KB |
| 004 Confidence intervals via bootstrapping (resampling).mp4 |
54.27MB |
| 004 MATLAB_ Import the divorce data_en.srt |
12.31KB |
| 004 MATLAB_ Import the divorce data_en.vtt |
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| 004 MATLAB_ Import the divorce data.mp4 |
96.29MB |
| 004 Min-max scaling_en.srt |
7.23KB |
| 004 Min-max scaling_en.vtt |
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| 004 Min-max scaling.mp4 |
11.73MB |
| 004 P-values_ definition, tails, and misinterpretations_en.srt |
25.43KB |
| 004 P-values_ definition, tails, and misinterpretations_en.vtt |
22.25KB |
| 004 P-values_ definition, tails, and misinterpretations.mp4 |
106.47MB |
| 004 Response bias_en.srt |
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| 004 Response bias_en.vtt |
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| 004 Response bias.mp4 |
21.82MB |
| 004 Simple regression_en.srt |
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| 004 Simple regression_en.vtt |
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| 004 Simple regression.mp4 |
36.77MB |
| 004 Summation notation_en.srt |
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| 004 Summation notation_en.vtt |
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| 004 Summation notation.mp4 |
7.73MB |
| 004 The F-test and the ANOVA table_en.srt |
10.47KB |
| 004 The F-test and the ANOVA table_en.vtt |
9.18KB |
| 004 The F-test and the ANOVA table.mp4 |
19.90MB |
| 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 |
24.36MB |
| 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 |
10.18MB |
| 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 |
7.06MB |
| 005 Absolute value_en.srt |
4.18KB |
| 005 Absolute value_en.vtt |
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| 005 Absolute value.mp4 |
6.92MB |
| 005 Clustering via dbscan_en.srt |
21.67KB |
| 005 Clustering via dbscan_en.vtt |
18.65KB |
| 005 Clustering via dbscan.mp4 |
100.30MB |
| 005 Code_ bootstrapping confidence intervals_en.srt |
21.66KB |
| 005 Code_ bootstrapping confidence intervals_en.vtt |
18.41KB |
| 005 Code_ bootstrapping confidence intervals.mp4 |
136.71MB |
| 005 Code_ min-max scaling_en.srt |
12.56KB |
| 005 Code_ min-max scaling_en.vtt |
10.75KB |
| 005 Code_ min-max scaling.mp4 |
40.43MB |
| 005 Code_ Response bias_en.srt |
6.35KB |
| 005 Code_ Response bias_en.vtt |
5.49KB |
| 005 Code_ Response bias.mp4 |
22.81MB |
| 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 |
52.29MB |
| 005 Correlation matrix_en.srt |
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| 005 Correlation matrix_en.vtt |
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| 005 Correlation matrix.mp4 |
30.96MB |
| 005 MATLAB_ More data visualizations_en.srt |
9.26KB |
| 005 MATLAB_ More data visualizations_en.vtt |
8.16KB |
| 005 MATLAB_ More data visualizations.mp4 |
34.32MB |
| 005 Probability and odds_en.srt |
6.94KB |
| 005 Probability and odds_en.vtt |
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| 005 Probability and odds.mp4 |
12.01MB |
| 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 |
17.32MB |
| 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 |
37.06MB |
| 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 |
93.81MB |
| 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 |
1.89KB |
| 006 _Unsupervised learning__ Confidence intervals for variance_en.vtt |
1.68KB |
| 006 _Unsupervised learning__ Confidence intervals for variance.mp4 |
8.54MB |
| 006 _Unsupervised learning__ Invert the min-max scaling_en.srt |
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| 006 _Unsupervised learning__ Invert the min-max scaling_en.vtt |
3.17KB |
| 006 _Unsupervised learning__ Invert the min-max scaling.mp4 |
6.79MB |
| 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 |
5.92MB |
| 006 Code_ correlation matrix_en.srt |
31.87KB |
| 006 Code_ correlation matrix_en.vtt |
27.17KB |
| 006 Code_ correlation matrix.mp4 |
282.48MB |
| 006 Code_ dbscan_en.srt |
49.38KB |
| 006 Code_ dbscan_en.vtt |
42.20KB |
| 006 Code_ dbscan.mp4 |
288.12MB |
| 006 Code_ Two-samples t-test_en.srt |
32.17KB |
| 006 Code_ Two-samples t-test_en.vtt |
27.56KB |
| 006 Code_ Two-samples t-test.mp4 |
211.35MB |
| 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 |
32.90MB |
| 006 F-score_en.srt |
33.08KB |
| 006 F-score_en.vtt |
28.75KB |
| 006 F-score.mp4 |
107.25MB |
| 006 Histograms_en.srt |
15.81KB |
| 006 Histograms_en.vtt |
13.70KB |
| 006 Histograms.mp4 |
43.73MB |
| 006 MATLAB_ Inferential statistics_en.srt |
15.30KB |
| 006 MATLAB_ Inferential statistics_en.vtt |
13.35KB |
| 006 MATLAB_ Inferential statistics.mp4 |
113.52MB |
| 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 |
12.18MB |
| 006 Samples, case reports, and anecdotes_en.srt |
7.68KB |
| 006 Samples, case reports, and anecdotes_en.vtt |
6.73KB |
| 006 Samples, case reports, and anecdotes.mp4 |
17.79MB |
| 006 The beauty and simplicity of Normal_en.srt |
7.64KB |
| 006 The beauty and simplicity of Normal_en.vtt |
6.72KB |
| 006 The beauty and simplicity of Normal.mp4 |
10.23MB |
| 006 The two-way ANOVA_en.srt |
29.37KB |
| 006 The two-way ANOVA_en.vtt |
25.18KB |
| 006 The two-way ANOVA.mp4 |
104.39MB |
| 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 |
19.94MB |
| 007 _Unsupervised learning__ Importance of N for t-test_en.srt |
6.85KB |
| 007 _Unsupervised learning__ Importance of N for t-test_en.vtt |
5.94KB |
| 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 |
133.49MB |
| 007 Measures of central tendency (mean)_en.srt |
18.99KB |
| 007 Measures of central tendency (mean)_en.vtt |
16.34KB |
| 007 Measures of central tendency (mean).mp4 |
38.70MB |
| 007 Misconceptions about confidence intervals_en.srt |
9.08KB |
| 007 Misconceptions about confidence intervals_en.vtt |
7.91KB |
| 007 Misconceptions about confidence intervals.mp4 |
18.60MB |
| 007 Multiple regression_en.srt |
19.12KB |
| 007 Multiple regression_en.vtt |
16.47KB |
| 007 Multiple regression.mp4 |
45.14MB |
| 007 One-way ANOVA example_en.srt |
20.60KB |
| 007 One-way ANOVA example_en.vtt |
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| 007 One-way ANOVA example.mp4 |
44.32MB |
| 007 Probability mass vs. density_en.srt |
18.42KB |
| 007 Probability mass vs. density_en.vtt |
15.95KB |
| 007 Probability mass vs. density.mp4 |
134.14MB |
| 007 Python_ Import and clean the marriage data_en.srt |
29.28KB |
| 007 Python_ Import and clean the marriage data_en.vtt |
25.48KB |
| 007 Python_ Import and clean the marriage data.mp4 |
249.82MB |
| 007 Receiver operating characteristics (ROC)_en.srt |
10.95KB |
| 007 Receiver operating characteristics (ROC)_en.vtt |
9.58KB |
| 007 Receiver operating characteristics (ROC).mp4 |
64.37MB |
| 007 The ethics of making up data_en.srt |
10.29KB |
| 007 The ethics of making up data_en.vtt |
8.90KB |
| 007 The ethics of making up data.mp4 |
19.65MB |
| 007 The logistic function_en.srt |
13.12KB |
| 007 The logistic function_en.vtt |
11.28KB |
| 007 The logistic function.mp4 |
17.90MB |
| 007 Type 1 and Type 2 errors_en.srt |
22.20KB |
| 007 Type 1 and Type 2 errors_en.vtt |
19.01KB |
| 007 Type 1 and Type 2 errors.mp4 |
45.90MB |
| 007 What are outliers and why are they dangerous__en.srt |
21.54KB |
| 007 What are outliers and why are they dangerous__en.vtt |
18.52KB |
| 007 What are outliers and why are they dangerous_.mp4 |
43.00MB |
| 008 _Unsupervised learning__ correlation to covariance matrix_en.srt |
5.81KB |
| 008 _Unsupervised learning__ correlation to covariance matrix_en.vtt |
5.12KB |
| 008 _Unsupervised learning__ correlation to covariance matrix.mp4 |
10.13MB |
| 008 _Unsupervised learning__ Histogram proportion_en.srt |
3.40KB |
| 008 _Unsupervised learning__ Histogram proportion_en.vtt |
2.98KB |
| 008 _Unsupervised learning__ Histogram proportion.mp4 |
11.79MB |
| 008 Code_ compute probability mass functions_en.srt |
16.01KB |
| 008 Code_ compute probability mass functions_en.vtt |
14.03KB |
| 008 Code_ compute probability mass functions.mp4 |
66.17MB |
| 008 Code_ One-way ANOVA (independent samples)_en.srt |
25.72KB |
| 008 Code_ One-way ANOVA (independent samples)_en.vtt |
21.93KB |
| 008 Code_ One-way ANOVA (independent samples).mp4 |
172.72MB |
| 008 Code_ ROC curves_en.srt |
11.67KB |
| 008 Code_ ROC curves_en.vtt |
10.14KB |
| 008 Code_ ROC curves.mp4 |
54.62MB |
| 008 K-nearest neighbor classification_en.srt |
8.99KB |
| 008 K-nearest neighbor classification_en.vtt |
7.84KB |
| 008 K-nearest neighbor classification.mp4 |
12.47MB |
| 008 Measures of central tendency (median, mode)_en.srt |
18.21KB |
| 008 Measures of central tendency (median, mode)_en.vtt |
15.71KB |
| 008 Measures of central tendency (median, mode).mp4 |
34.26MB |
| 008 Parametric vs. non-parametric tests_en.srt |
12.87KB |
| 008 Parametric vs. non-parametric tests_en.vtt |
11.30KB |
| 008 Parametric vs. non-parametric tests.mp4 |
87.45MB |
| 008 Python_ Import the divorce data_en.srt |
18.53KB |
| 008 Python_ Import the divorce data_en.vtt |
16.08KB |
| 008 Python_ Import the divorce data.mp4 |
137.14MB |
| 008 Rank and tied-rank_en.srt |
9.55KB |
| 008 Rank and tied-rank_en.vtt |
8.22KB |
| 008 Rank and tied-rank.mp4 |
12.92MB |
| 008 Removing outliers_ z-score method_en.srt |
14.14KB |
| 008 Removing outliers_ z-score method_en.vtt |
12.21KB |
| 008 Removing outliers_ z-score method.mp4 |
33.51MB |
| 008 Standardizing regression coefficients_en.srt |
18.34KB |
| 008 Standardizing regression coefficients_en.vtt |
15.73KB |
| 008 Standardizing regression coefficients.mp4 |
75.19MB |
| 008 Wilcoxon signed-rank (nonparametric t-test)_en.srt |
10.43KB |
| 008 Wilcoxon signed-rank (nonparametric t-test)_en.vtt |
9.09KB |
| 008 Wilcoxon signed-rank (nonparametric t-test).mp4 |
25.98MB |
| 009 _Unsupervised learning__ Make this plot look nicer__en.srt |
2.35KB |
| 009 _Unsupervised learning__ Make this plot look nicer__en.vtt |
2.06KB |
| 009 _Unsupervised learning__ Make this plot look nicer_.mp4 |
11.50MB |
| 009 Code_ computing central tendency_en.srt |
20.14KB |
| 009 Code_ computing central tendency_en.vtt |
17.41KB |
| 009 Code_ computing central tendency.mp4 |
66.60MB |
| 009 Code_ KNN_en.srt |
18.21KB |
| 009 Code_ KNN_en.vtt |
15.49KB |
| 009 Code_ KNN.mp4 |
108.34MB |
| 009 Code_ Multiple regression_en.srt |
27.91KB |
| 009 Code_ Multiple regression_en.vtt |
23.90KB |
| 009 Code_ Multiple regression.mp4 |
170.95MB |
| 009 Code_ One-way repeated-measures ANOVA_en.srt |
18.38KB |
| 009 Code_ One-way repeated-measures ANOVA_en.vtt |
15.87KB |
| 009 Code_ One-way repeated-measures ANOVA.mp4 |
73.10MB |
| 009 Code_ Signed-rank test_en.srt |
26.90KB |
| 009 Code_ Signed-rank test_en.vtt |
23.02KB |
| 009 Code_ Signed-rank test.mp4 |
161.85MB |
| 009 Cumulative distribution functions_en.srt |
20.37KB |
| 009 Cumulative distribution functions_en.vtt |
17.73KB |
| 009 Cumulative distribution functions.mp4 |
45.41MB |
| 009 Multiple comparisons and Bonferroni correction_en.srt |
12.50KB |
| 009 Multiple comparisons and Bonferroni correction_en.vtt |
10.79KB |
| 009 Multiple comparisons and Bonferroni correction.mp4 |
29.56MB |
| 009 Partial correlation_en.srt |
15.42KB |
| 009 Partial correlation_en.vtt |
13.36KB |
| 009 Partial correlation.mp4 |
59.34MB |
| 009 Pie charts_en.srt |
8.47KB |
| 009 Pie charts_en.vtt |
7.33KB |
| 009 Pie charts.mp4 |
16.53MB |
| 009 Python_ Inferential statistics_en.srt |
16.08KB |
| 009 Python_ Inferential statistics_en.vtt |
14.05KB |
| 009 Python_ Inferential statistics.mp4 |
115.54MB |
| 009 The modified z-score method_en.srt |
5.90KB |
| 009 The modified z-score method_en.vtt |
5.12KB |
| 009 The modified z-score method.mp4 |
9.62MB |
| 010 _Unsupervised learning__ central tendencies with outliers_en.srt |
4.31KB |
| 010 _Unsupervised learning__ central tendencies with outliers_en.vtt |
3.78KB |
| 010 _Unsupervised learning__ central tendencies with outliers.mp4 |
16.74MB |
| 010 Code_ cdfs and pdfs_en.srt |
14.49KB |
| 010 Code_ cdfs and pdfs_en.vtt |
12.56KB |
| 010 Code_ cdfs and pdfs.mp4 |
95.94MB |
| 010 Code_ partial correlation_en.srt |
29.41KB |
| 010 Code_ partial correlation_en.vtt |
25.23KB |
| 010 Code_ partial correlation.mp4 |
108.27MB |
| 010 Code_ pie charts_en.srt |
19.38KB |
| 010 Code_ pie charts_en.vtt |
16.66KB |
| 010 Code_ pie charts.mp4 |
78.92MB |
| 010 Code_ z-score for outlier removal_en.srt |
33.64KB |
| 010 Code_ z-score for outlier removal_en.vtt |
28.78KB |
| 010 Code_ z-score for outlier removal.mp4 |
136.89MB |
| 010 Mann-Whitney U test (nonparametric t-test)_en.srt |
8.84KB |
| 010 Mann-Whitney U test (nonparametric t-test)_en.vtt |
7.66KB |
| 010 Mann-Whitney U test (nonparametric t-test).mp4 |
20.32MB |
| 010 Polynomial regression models_en.srt |
12.22KB |
| 010 Polynomial regression models_en.vtt |
10.65KB |
| 010 Polynomial regression models.mp4 |
48.15MB |
| 010 Principal components analysis (PCA)_en.srt |
23.28KB |
| 010 Principal components analysis (PCA)_en.vtt |
20.26KB |
| 010 Principal components analysis (PCA).mp4 |
42.56MB |
| 010 Statistical vs. theoretical vs. clinical significance_en.srt |
9.98KB |
| 010 Statistical vs. theoretical vs. clinical significance_en.vtt |
8.64KB |
| 010 Statistical vs. theoretical vs. clinical significance.mp4 |
19.08MB |
| 010 Take-home messages_en.srt |
8.72KB |
| 010 Take-home messages_en.vtt |
7.60KB |
| 010 Take-home messages.mp4 |
43.80MB |
| 010 Two-way ANOVA example_en.srt |
16.08KB |
| 010 Two-way ANOVA example_en.vtt |
14.00KB |
| 010 Two-way ANOVA example.mp4 |
35.95MB |
| 011 _Unsupervised learning__ cdf's for various distributions_en.srt |
3.32KB |
| 011 _Unsupervised learning__ cdf's for various distributions_en.vtt |
2.94KB |
| 011 _Unsupervised learning__ cdf's for various distributions.mp4 |
9.31MB |
| 011 _Unsupervised learning__ z vs. modified-z_en.srt |
3.84KB |
| 011 _Unsupervised learning__ z vs. modified-z_en.vtt |
3.36KB |
| 011 _Unsupervised learning__ z vs. modified-z.mp4 |
9.02MB |
| 011 Code_ Mann-Whitney U test_en.srt |
7.75KB |
| 011 Code_ Mann-Whitney U test_en.vtt |
6.70KB |
| 011 Code_ Mann-Whitney U test.mp4 |
52.05MB |
| 011 Code_ PCA_en.srt |
26.52KB |
| 011 Code_ PCA_en.vtt |
22.60KB |
| 011 Code_ PCA.mp4 |
175.10MB |
| 011 Code_ polynomial modeling_en.srt |
22.44KB |
| 011 Code_ polynomial modeling_en.vtt |
19.30KB |
| 011 Code_ polynomial modeling.mp4 |
129.08MB |
| 011 Code_ Two-way mixed ANOVA_en.srt |
21.44KB |
| 011 Code_ Two-way mixed ANOVA_en.vtt |
18.37KB |
| 011 Code_ Two-way mixed ANOVA.mp4 |
114.16MB |
| 011 Cross-validation_en.srt |
16.44KB |
| 011 Cross-validation_en.vtt |
14.32KB |
| 011 Cross-validation.mp4 |
28.25MB |
| 011 Measures of dispersion (variance, standard deviation)_en.srt |
26.26KB |
| 011 Measures of dispersion (variance, standard deviation)_en.vtt |
22.56KB |
| 011 Measures of dispersion (variance, standard deviation).mp4 |
54.12MB |
| 011 The problem with Pearson_en.srt |
9.89KB |
| 011 The problem with Pearson_en.vtt |
8.65KB |
| 011 The problem with Pearson.mp4 |
16.57MB |
| 011 When to use lines instead of bars_en.srt |
8.62KB |
| 011 When to use lines instead of bars_en.vtt |
7.47KB |
| 011 When to use lines instead of bars.mp4 |
17.98MB |
| 012 _Unsupervised learning__ K-means on PC data_en.srt |
2.21KB |
| 012 _Unsupervised learning__ K-means on PC data_en.vtt |
1.95KB |
| 012 _Unsupervised learning__ K-means on PC data.mp4 |
11.52MB |
| 012 _Unsupervised learning__ Polynomial design matrix_en.srt |
1.11KB |
| 012 _Unsupervised learning__ Polynomial design matrix_en.vtt |
1010B |
| 012 _Unsupervised learning__ Polynomial design matrix.mp4 |
4.74MB |
| 012 Code_ Computing dispersion_en.srt |
37.23KB |
| 012 Code_ Computing dispersion_en.vtt |
32.31KB |
| 012 Code_ Computing dispersion.mp4 |
266.09MB |
| 012 Creating sample estimate distributions_en.srt |
27.73KB |
| 012 Creating sample estimate distributions_en.vtt |
23.83KB |
| 012 Creating sample estimate distributions.mp4 |
124.85MB |
| 012 Linear vs. logarithmic axis scaling_en.srt |
12.48KB |
| 012 Linear vs. logarithmic axis scaling_en.vtt |
10.79KB |
| 012 Linear vs. logarithmic axis scaling.mp4 |
25.64MB |
| 012 Multivariate outlier detection_en.srt |
14.35KB |
| 012 Multivariate outlier detection_en.vtt |
12.28KB |
| 012 Multivariate outlier detection.mp4 |
25.05MB |
| 012 Nonparametric correlation_ Spearman rank_en.srt |
10.73KB |
| 012 Nonparametric correlation_ Spearman rank_en.vtt |
9.30KB |
| 012 Nonparametric correlation_ Spearman rank.mp4 |
23.72MB |
| 012 Permutation testing for t-test significance_en.srt |
16.36KB |
| 012 Permutation testing for t-test significance_en.vtt |
14.20KB |
| 012 Permutation testing for t-test significance.mp4 |
63.48MB |
| 012 Statistical significance vs. classification accuracy_en.srt |
17.02KB |
| 012 Statistical significance vs. classification accuracy_en.vtt |
14.68KB |
| 012 Statistical significance vs. classification accuracy.mp4 |
42.50MB |
| 013 Code_ Euclidean distance for outlier removal_en.srt |
12.77KB |
| 013 Code_ Euclidean distance for outlier removal_en.vtt |
11.04KB |
| 013 Code_ Euclidean distance for outlier removal.mp4 |
43.72MB |
| 013 Code_ line plots_en.srt |
10.87KB |
| 013 Code_ line plots_en.vtt |
9.41KB |
| 013 Code_ line plots.mp4 |
37.29MB |
| 013 Code_ permutation testing_en.srt |
37.10KB |
| 013 Code_ permutation testing_en.vtt |
31.77KB |
| 013 Code_ permutation testing.mp4 |
240.90MB |
| 013 Fisher-Z transformation for correlations_en.srt |
9.87KB |
| 013 Fisher-Z transformation for correlations_en.vtt |
8.63KB |
| 013 Fisher-Z transformation for correlations.mp4 |
28.48MB |
| 013 Independent components analysis (ICA)_en.srt |
17.25KB |
| 013 Independent components analysis (ICA)_en.vtt |
15.07KB |
| 013 Independent components analysis (ICA).mp4 |
45.52MB |
| 013 Interquartile range (IQR)_en.srt |
7.01KB |
| 013 Interquartile range (IQR)_en.vtt |
6.10KB |
| 013 Interquartile range (IQR).mp4 |
9.84MB |
| 013 Logistic regression_en.srt |
25.46KB |
| 013 Logistic regression_en.vtt |
21.81KB |
| 013 Logistic regression.mp4 |
52.70MB |
| 013 Monte Carlo sampling_en.srt |
3.82KB |
| 013 Monte Carlo sampling_en.vtt |
3.35KB |
| 013 Monte Carlo sampling.mp4 |
8.83MB |
| 014 _Unsupervised learning__ How many permutations__en.srt |
7.74KB |
| 014 _Unsupervised learning__ How many permutations__en.vtt |
6.73KB |
| 014 _Unsupervised learning__ How many permutations_.mp4 |
32.50MB |
| 014 _Unsupervised learning__ log-scaled plots_en.srt |
2.47KB |
| 014 _Unsupervised learning__ log-scaled plots_en.vtt |
2.14KB |
| 014 _Unsupervised learning__ log-scaled plots.mp4 |
3.73MB |
| 014 Code_ ICA_en.srt |
18.44KB |
| 014 Code_ ICA_en.vtt |
15.90KB |
| 014 Code_ ICA.mp4 |
73.36MB |
| 014 Code_ IQR_en.srt |
23.44KB |
| 014 Code_ IQR_en.vtt |
20.15KB |
| 014 Code_ IQR.mp4 |
83.39MB |
| 014 Code_ Logistic regression_en.srt |
14.18KB |
| 014 Code_ Logistic regression_en.vtt |
12.14KB |
| 014 Code_ Logistic regression.mp4 |
81.23MB |
| 014 Code_ Spearman correlation and Fisher-Z_en.srt |
11.10KB |
| 014 Code_ Spearman correlation and Fisher-Z_en.vtt |
9.61KB |
| 014 Code_ Spearman correlation and Fisher-Z.mp4 |
42.71MB |
| 014 Removing outliers by data trimming_en.srt |
8.53KB |
| 014 Removing outliers by data trimming_en.vtt |
7.40KB |
| 014 Removing outliers by data trimming.mp4 |
16.90MB |
| 014 Sampling variability, noise, and other annoyances_en.srt |
13.06KB |
| 014 Sampling variability, noise, and other annoyances_en.vtt |
11.37KB |
| 014 Sampling variability, noise, and other annoyances.mp4 |
106.08MB |
| 015 _Unsupervised learning__ Spearman correlation_en.srt |
1.86KB |
| 015 _Unsupervised learning__ Spearman correlation_en.vtt |
1.64KB |
| 015 _Unsupervised learning__ Spearman correlation.mp4 |
15.95MB |
| 015 Code_ Data trimming to remove outliers_en.srt |
16.30KB |
| 015 Code_ Data trimming to remove outliers_en.vtt |
14.05KB |
| 015 Code_ Data trimming to remove outliers.mp4 |
65.29MB |
| 015 Code_ sampling variability_en.srt |
38.25KB |
| 015 Code_ sampling variability_en.vtt |
32.90KB |
| 015 Code_ sampling variability.mp4 |
154.75MB |
| 015 QQ plots_en.srt |
10.18KB |
| 015 QQ plots_en.vtt |
8.88KB |
| 015 QQ plots.mp4 |
16.22MB |
| 015 Under- and over-fitting_en.srt |
25.37KB |
| 015 Under- and over-fitting_en.vtt |
21.71KB |
| 015 Under- and over-fitting.mp4 |
120.86MB |
| 016 _Unsupervised learning__ confidence interval on correlation_en.srt |
3.32KB |
| 016 _Unsupervised learning__ confidence interval on correlation_en.vtt |
2.93KB |
| 016 _Unsupervised learning__ confidence interval on correlation.mp4 |
10.31MB |
| 016 _Unsupervised learning__ Overfit data_en.srt |
2.69KB |
| 016 _Unsupervised learning__ Overfit data_en.vtt |
2.36KB |
| 016 _Unsupervised learning__ Overfit data.mp4 |
4.82MB |
| 016 Code_ QQ plots_en.srt |
23.48KB |
| 016 Code_ QQ plots_en.vtt |
20.10KB |
| 016 Code_ QQ plots.mp4 |
90.30MB |
| 016 Expected value_en.srt |
15.37KB |
| 016 Expected value_en.vtt |
13.21KB |
| 016 Expected value.mp4 |
59.63MB |
| 016 Non-parametric solutions to outliers_en.srt |
6.34KB |
| 016 Non-parametric solutions to outliers_en.vtt |
5.57KB |
| 016 Non-parametric solutions to outliers.mp4 |
22.96MB |
| 017 Comparing _nested_ models_en.srt |
17.28KB |
| 017 Comparing _nested_ models_en.vtt |
15.11KB |
| 017 Comparing _nested_ models.mp4 |
39.07MB |
| 017 Conditional probability_en.srt |
18.81KB |
| 017 Conditional probability_en.vtt |
16.12KB |
| 017 Conditional probability.mp4 |
85.68MB |
| 017 Kendall's correlation for ordinal data_en.srt |
15.22KB |
| 017 Kendall's correlation for ordinal data_en.vtt |
13.13KB |
| 017 Kendall's correlation for ordinal data.mp4 |
30.15MB |
| 017 Nonlinear data transformations_en.srt |
19.80KB |
| 017 Nonlinear data transformations_en.vtt |
17.38KB |
| 017 Nonlinear data transformations.mp4 |
33.69MB |
| 017 Statistical _moments__en.srt |
13.06KB |
| 017 Statistical _moments__en.vtt |
11.16KB |
| 017 Statistical _moments_.mp4 |
21.68MB |
| 018 An outlier lecture on personal accountability_en.srt |
4.12KB |
| 018 An outlier lecture on personal accountability_en.vtt |
3.65KB |
| 018 An outlier lecture on personal accountability.mp4 |
17.70MB |
| 018 Code_ conditional probabilities_en.srt |
29.61KB |
| 018 Code_ conditional probabilities_en.vtt |
25.37KB |
| 018 Code_ conditional probabilities.mp4 |
115.08MB |
| 018 Code_ Kendall correlation_en.srt |
17.59KB |
| 018 Code_ Kendall correlation_en.vtt |
22.96KB |
| 018 Code_ Kendall correlation.mp4 |
184.22MB |
| 018 Histograms part 2_ Number of bins_en.srt |
14.32KB |
| 018 Histograms part 2_ Number of bins_en.vtt |
12.41KB |
| 018 Histograms part 2_ Number of bins.mp4 |
23.50MB |
| 018 What to do about missing data_en.srt |
9.59KB |
| 018 What to do about missing data_en.vtt |
8.36KB |
| 018 What to do about missing data.mp4 |
16.05MB |
| 019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.srt |
3.31KB |
| 019 _Unsupervised learning__ Does Kendall vs. Pearson matter__en.vtt |
2.95KB |
| 019 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4 |
14.95MB |
| 019 Code_ Histogram bins_en.srt |
17.86KB |
| 019 Code_ Histogram bins_en.vtt |
15.40KB |
| 019 Code_ Histogram bins.mp4 |
118.12MB |
| 019 Tree diagrams for conditional probabilities_en.srt |
9.92KB |
| 019 Tree diagrams for conditional probabilities_en.vtt |
8.57KB |
| 019 Tree diagrams for conditional probabilities.mp4 |
13.50MB |
| 020 The Law of Large Numbers_en.srt |
14.41KB |
| 020 The Law of Large Numbers_en.vtt |
12.47KB |
| 020 The Law of Large Numbers.mp4 |
40.55MB |
| 020 The subgroups correlation paradox_en.srt |
6.98KB |
| 020 The subgroups correlation paradox_en.vtt |
6.13KB |
| 020 The subgroups correlation paradox.mp4 |
21.57MB |
| 020 Violin plots_en.srt |
4.98KB |
| 020 Violin plots_en.vtt |
4.30KB |
| 020 Violin plots.mp4 |
6.47MB |
| 021 Code_ Law of Large Numbers in action_en.srt |
27.83KB |
| 021 Code_ Law of Large Numbers in action_en.vtt |
23.84KB |
| 021 Code_ Law of Large Numbers in action.mp4 |
165.60MB |
| 021 Code_ violin plots_en.srt |
15.42KB |
| 021 Code_ violin plots_en.vtt |
13.19KB |
| 021 Code_ violin plots.mp4 |
104.96MB |
| 021 Cosine similarity_en.srt |
7.49KB |
| 021 Cosine similarity_en.vtt |
6.54KB |
| 021 Cosine similarity.mp4 |
14.20MB |
| 022 _Unsupervised learning__ asymmetric violin plots_en.srt |
3.84KB |
| 022 _Unsupervised learning__ asymmetric violin plots_en.vtt |
3.31KB |
| 022 _Unsupervised learning__ asymmetric violin plots.mp4 |
17.32MB |
| 022 Code_ Cosine similarity vs. Pearson correlation_en.srt |
31.27KB |
| 022 Code_ Cosine similarity vs. Pearson correlation_en.vtt |
26.93KB |
| 022 Code_ Cosine similarity vs. Pearson correlation.mp4 |
102.19MB |
| 022 The Central Limit Theorem_en.srt |
15.56KB |
| 022 The Central Limit Theorem_en.vtt |
13.52KB |
| 022 The Central Limit Theorem.mp4 |
26.67MB |
| 023 Code_ the CLT in action_en.srt |
23.58KB |
| 023 Code_ the CLT in action_en.vtt |
20.30KB |
| 023 Code_ the CLT in action.mp4 |
93.32MB |
| 023 Shannon entropy_en.srt |
15.53KB |
| 023 Shannon entropy_en.vtt |
13.46KB |
| 023 Shannon entropy.mp4 |
33.05MB |
| 024 _Unsupervised learning__ Averaging pairs of numbers_en.srt |
3.19KB |
| 024 _Unsupervised learning__ Averaging pairs of numbers_en.vtt |
2.76KB |
| 024 _Unsupervised learning__ Averaging pairs of numbers.mp4 |
9.48MB |
| 024 Code_ entropy_en.srt |
30.29KB |
| 024 Code_ entropy_en.vtt |
25.84KB |
| 024 Code_ entropy.mp4 |
96.76MB |
| 025 _Unsupervised learning__ entropy and number of bins_en.srt |
2.01KB |
| 025 _Unsupervised learning__ entropy and number of bins_en.vtt |
1.76KB |
| 025 _Unsupervised learning__ entropy and number of bins.mp4 |
8.25MB |
| 25299297-stats-intro-GuessTheTest.zip |
3.72KB |
| 32684220-statsML.zip |
1.36MB |
| 35855730-state-marriage-rates-90-95-99-19.xlsx |
23.64KB |
| 35855734-state-divorce-rates-90-95-99-19.xlsx |
22.47KB |