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| 001 [Important] Getting the most out of this course.en.srt |
6.30Кб |
| 001 [Important] Getting the most out of this course.mp4 |
38.04Мб |
| 002 About using MATLAB or Python.en.srt |
6.17Кб |
| 002 About using MATLAB or Python.mp4 |
38.91Мб |
| 003 Statistics guessing game!.en.srt |
13.88Кб |
| 003 Statistics guessing game!.mp4 |
80.31Мб |
| 003 stats-intro-GuessTheTest.zip |
3.72Кб |
| 004 Using the Q&A forum.en.srt |
8.48Кб |
| 004 Using the Q&A forum.mp4 |
24.47Мб |
| 005 (optional) Entering time-stamped notes in the Udemy video player.en.srt |
3.23Кб |
| 005 (optional) Entering time-stamped notes in the Udemy video player.mp4 |
8.46Мб |
| 006 Should you memorize statistical formulas_.en.srt |
4.32Кб |
| 006 Should you memorize statistical formulas_.mp4 |
28.04Мб |
| 007 Arithmetic and exponents.en.srt |
5.85Кб |
| 007 Arithmetic and exponents.mp4 |
7.62Мб |
| 008 Scientific notation.en.srt |
9.10Кб |
| 008 Scientific notation.mp4 |
12.96Мб |
| 009 Summation notation.en.srt |
6.25Кб |
| 009 Summation notation.mp4 |
7.80Мб |
| 010 Absolute value.en.srt |
4.34Кб |
| 010 Absolute value.mp4 |
6.97Мб |
| 011 Natural exponent and logarithm.en.srt |
8.38Кб |
| 011 Natural exponent and logarithm.mp4 |
12.28Мб |
| 012 The logistic function.en.srt |
13.67Кб |
| 012 The logistic function.mp4 |
18.03Мб |
| 013 Rank and tied-rank.en.srt |
9.96Кб |
| 013 Rank and tied-rank.mp4 |
12.94Мб |
| 014 Download materials for the entire course!.en.srt |
5.62Кб |
| 014 Download materials for the entire course!.mp4 |
14.52Мб |
| 014 statsML.zip |
1.42Мб |
| 015 Is _data_ singular or plural_!_!!_!.en.srt |
2.42Кб |
| 015 Is _data_ singular or plural_!_!!_!.mp4 |
10.89Мб |
| 016 Where do data come from and what do they mean_.en.srt |
8.74Кб |
| 016 Where do data come from and what do they mean_.mp4 |
35.62Мб |
| 017 Types of data_ categorical, numerical, etc.en.srt |
21.77Кб |
| 017 Types of data_ categorical, numerical, etc.mp4 |
59.62Мб |
| 018 Code_ representing types of data on computers.en.srt |
13.67Кб |
| 018 Code_ representing types of data on computers.mp4 |
47.94Мб |
| 019 Sample vs. population data.en.srt |
17.89Кб |
| 019 Sample vs. population data.mp4 |
37.27Мб |
| 020 Samples, case reports, and anecdotes.en.srt |
7.98Кб |
| 020 Samples, case reports, and anecdotes.mp4 |
17.88Мб |
| 021 The ethics of making up data.en.srt |
10.72Кб |
| 021 The ethics of making up data.mp4 |
19.76Мб |
| 022 Bar plots.en.srt |
17.75Кб |
| 022 Bar plots.mp4 |
37.01Мб |
| 023 Code_ bar plots.en.srt |
26.48Кб |
| 023 Code_ bar plots.mp4 |
100.24Мб |
| 024 Box-and-whisker plots.en.srt |
8.15Кб |
| 024 Box-and-whisker plots.mp4 |
11.21Мб |
| 025 Code_ box plots.en.srt |
13.30Кб |
| 025 Code_ box plots.mp4 |
83.68Мб |
| 026 _Unsupervised learning__ Boxplots of normal and uniform noise.en.srt |
3.89Кб |
| 026 _Unsupervised learning__ Boxplots of normal and uniform noise.mp4 |
8.27Мб |
| 027 Histograms.en.srt |
16.45Кб |
| 027 Histograms.mp4 |
43.91Мб |
| 028 Code_ histograms.en.srt |
25.25Кб |
| 028 Code_ histograms.mp4 |
133.75Мб |
| 029 _Unsupervised learning__ Histogram proportion.en.srt |
3.54Кб |
| 029 _Unsupervised learning__ Histogram proportion.mp4 |
11.83Мб |
| 030 Pie charts.en.srt |
8.83Кб |
| 030 Pie charts.mp4 |
16.63Мб |
| 031 Code_ pie charts.en.srt |
20.19Кб |
| 031 Code_ pie charts.mp4 |
69.24Мб |
| 032 When to use lines instead of bars.en.srt |
8.98Кб |
| 032 When to use lines instead of bars.mp4 |
18.08Мб |
| 033 Linear vs. logarithmic axis scaling.en.srt |
12.99Кб |
| 033 Linear vs. logarithmic axis scaling.mp4 |
25.66Мб |
| 034 Code_ line plots.en.srt |
11.32Кб |
| 034 Code_ line plots.mp4 |
37.42Мб |
| 035 _Unsupervised learning__ log-scaled plots.en.srt |
2.57Кб |
| 035 _Unsupervised learning__ log-scaled plots.mp4 |
3.75Мб |
| 036 Descriptive vs. inferential statistics.en.srt |
6.63Кб |
| 036 Descriptive vs. inferential statistics.mp4 |
21.56Мб |
| 037 Accuracy, precision, resolution.en.srt |
11.88Кб |
| 037 Accuracy, precision, resolution.mp4 |
25.54Мб |
| 038 Data distributions.en.srt |
17.43Кб |
| 038 Data distributions.mp4 |
32.14Мб |
| 039 Code_ data from different distributions.en.srt |
47.83Кб |
| 039 Code_ data from different distributions.mp4 |
303.53Мб |
| 040 _Unsupervised learning__ histograms of distributions.en.srt |
3.19Кб |
| 040 _Unsupervised learning__ histograms of distributions.mp4 |
10.21Мб |
| 041 The beauty and simplicity of Normal.en.srt |
7.93Кб |
| 041 The beauty and simplicity of Normal.mp4 |
10.31Мб |
| 042 Measures of central tendency (mean).en.srt |
19.78Кб |
| 042 Measures of central tendency (mean).mp4 |
38.91Мб |
| 043 Measures of central tendency (median, mode).en.srt |
18.95Кб |
| 043 Measures of central tendency (median, mode).mp4 |
34.45Мб |
| 044 Code_ computing central tendency.en.srt |
20.95Кб |
| 044 Code_ computing central tendency.mp4 |
76.27Мб |
| 045 _Unsupervised learning__ central tendencies with outliers.en.srt |
4.48Кб |
| 045 _Unsupervised learning__ central tendencies with outliers.mp4 |
16.79Мб |
| 046 Measures of dispersion (variance, standard deviation).en.srt |
27.35Кб |
| 046 Measures of dispersion (variance, standard deviation).mp4 |
54.41Мб |
| 047 Code_ Computing dispersion.en.srt |
38.67Кб |
| 047 Code_ Computing dispersion.mp4 |
266.53Мб |
| 048 Interquartile range (IQR).en.srt |
7.29Кб |
| 048 Interquartile range (IQR).mp4 |
9.91Мб |
| 049 Code_ IQR.en.srt |
24.41Кб |
| 049 Code_ IQR.mp4 |
83.65Мб |
| 050 QQ plots.en.srt |
10.58Кб |
| 050 QQ plots.mp4 |
16.34Мб |
| 051 Code_ QQ plots.en.srt |
24.47Кб |
| 051 Code_ QQ plots.mp4 |
90.55Мб |
| 052 Statistical _moments_.en.srt |
13.63Кб |
| 052 Statistical _moments_.mp4 |
21.81Мб |
| 053 Histograms part 2_ Number of bins.en.srt |
14.90Кб |
| 053 Histograms part 2_ Number of bins.mp4 |
23.53Мб |
| 054 Code_ Histogram bins.en.srt |
18.59Кб |
| 054 Code_ Histogram bins.mp4 |
118.27Мб |
| 055 Violin plots.en.srt |
5.19Кб |
| 055 Violin plots.mp4 |
6.53Мб |
| 056 Code_ violin plots.en.srt |
16.08Кб |
| 056 Code_ violin plots.mp4 |
105.08Мб |
| 057 _Unsupervised learning__ asymmetric violin plots.en.srt |
4.01Кб |
| 057 _Unsupervised learning__ asymmetric violin plots.mp4 |
17.37Мб |
| 058 Shannon entropy.en.srt |
16.14Кб |
| 058 Shannon entropy.mp4 |
33.23Мб |
| 059 Code_ entropy.en.srt |
31.58Кб |
| 059 Code_ entropy.mp4 |
110.34Мб |
| 060 _Unsupervised learning__ entropy and number of bins.en.srt |
2.09Кб |
| 060 _Unsupervised learning__ entropy and number of bins.mp4 |
8.27Мб |
| 061 Garbage in, garbage out (GIGO).en.srt |
5.90Кб |
| 061 Garbage in, garbage out (GIGO).mp4 |
11.61Мб |
| 062 Z-score standardization.en.srt |
14.89Кб |
| 062 Z-score standardization.mp4 |
36.38Мб |
| 063 Code_ z-score.en.srt |
20.05Кб |
| 063 Code_ z-score.mp4 |
66.96Мб |
| 064 Min-max scaling.en.srt |
7.52Кб |
| 064 Min-max scaling.mp4 |
11.74Мб |
| 065 Code_ min-max scaling.en.srt |
13.11Кб |
| 065 Code_ min-max scaling.mp4 |
40.53Мб |
| 066 _Unsupervised learning__ Invert the min-max scaling.en.srt |
3.77Кб |
| 066 _Unsupervised learning__ Invert the min-max scaling.mp4 |
6.82Мб |
| 067 What are outliers and why are they dangerous_.en.srt |
22.43Кб |
| 067 What are outliers and why are they dangerous_.mp4 |
43.23Мб |
| 068 Removing outliers_ z-score method.en.srt |
14.72Кб |
| 068 Removing outliers_ z-score method.mp4 |
33.66Мб |
| 069 The modified z-score method.en.srt |
6.13Кб |
| 069 The modified z-score method.mp4 |
9.68Мб |
| 070 Code_ z-score for outlier removal.en.srt |
35.06Кб |
| 070 Code_ z-score for outlier removal.mp4 |
137.22Мб |
| 071 _Unsupervised learning__ z vs. modified-z.en.srt |
3.99Кб |
| 071 _Unsupervised learning__ z vs. modified-z.mp4 |
9.07Мб |
| 072 Multivariate outlier detection.en.srt |
14.97Кб |
| 072 Multivariate outlier detection.mp4 |
25.19Мб |
| 073 Code_ Euclidean distance for outlier removal.en.srt |
13.29Кб |
| 073 Code_ Euclidean distance for outlier removal.mp4 |
43.84Мб |
| 074 Removing outliers by data trimming.en.srt |
8.87Кб |
| 074 Removing outliers by data trimming.mp4 |
16.99Мб |
| 075 Code_ Data trimming to remove outliers.en.srt |
16.97Кб |
| 075 Code_ Data trimming to remove outliers.mp4 |
65.43Мб |
| 076 Non-parametric solutions to outliers.en.srt |
6.58Кб |
| 076 Non-parametric solutions to outliers.mp4 |
23.05Мб |
| 077 An outlier lecture on personal accountability.en.srt |
4.28Кб |
| 077 An outlier lecture on personal accountability.mp4 |
17.83Мб |
| 078 What is probability_.en.srt |
18.66Кб |
| 078 What is probability_.mp4 |
41.32Мб |
| 079 Probability vs. proportion.en.srt |
14.74Кб |
| 079 Probability vs. proportion.mp4 |
37.66Мб |
| 080 Computing probabilities.en.srt |
15.77Кб |
| 080 Computing probabilities.mp4 |
37.69Мб |
| 081 Code_ compute probabilities.en.srt |
22.97Кб |
| 081 Code_ compute probabilities.mp4 |
137.11Мб |
| 082 Probability and odds.en.srt |
7.22Кб |
| 082 Probability and odds.mp4 |
12.01Мб |
| 083 _Unsupervised learning__ probabilities of odds-space.en.srt |
3.26Кб |
| 083 _Unsupervised learning__ probabilities of odds-space.mp4 |
5.96Мб |
| 084 Probability mass vs. density.en.srt |
19.16Кб |
| 084 Probability mass vs. density.mp4 |
134.39Мб |
| 085 Code_ compute probability mass functions.en.srt |
16.60Кб |
| 085 Code_ compute probability mass functions.mp4 |
66.29Мб |
| 086 Cumulative probability distributions.en.srt |
16.39Кб |
| 086 Cumulative probability distributions.mp4 |
36.73Мб |
| 087 Code_ cdfs and pdfs.en.srt |
14.71Кб |
| 087 Code_ cdfs and pdfs.mp4 |
42.28Мб |
| 088 _Unsupervised learning__ cdf's for various distributions.en.srt |
3.44Кб |
| 088 _Unsupervised learning__ cdf's for various distributions.mp4 |
9.35Мб |
| 089 Creating sample estimate distributions.en.srt |
28.87Кб |
| 089 Creating sample estimate distributions.mp4 |
125.23Мб |
| 090 Monte Carlo sampling.en.srt |
3.96Кб |
| 090 Monte Carlo sampling.mp4 |
16.35Мб |
| 091 Sampling variability, noise, and other annoyances.en.srt |
13.57Кб |
| 091 Sampling variability, noise, and other annoyances.mp4 |
106.24Мб |
| 092 Code_ sampling variability.en.srt |
39.81Кб |
| 092 Code_ sampling variability.mp4 |
155.12Мб |
| 093 Expected value.en.srt |
16.02Кб |
| 093 Expected value.mp4 |
59.79Мб |
| 094 Conditional probability.en.srt |
19.61Кб |
| 094 Conditional probability.mp4 |
85.95Мб |
| 095 Code_ conditional probabilities.en.srt |
30.85Кб |
| 095 Code_ conditional probabilities.mp4 |
115.37Мб |
| 096 Tree diagrams for conditional probabilities.en.srt |
10.34Кб |
| 096 Tree diagrams for conditional probabilities.mp4 |
13.61Мб |
| 097 The Law of Large Numbers.en.srt |
14.99Кб |
| 097 The Law of Large Numbers.mp4 |
40.72Мб |
| 098 Code_ Law of Large Numbers in action.en.srt |
29.00Кб |
| 098 Code_ Law of Large Numbers in action.mp4 |
165.91Мб |
| 099 The Central Limit Theorem.en.srt |
16.18Кб |
| 099 The Central Limit Theorem.mp4 |
26.84Мб |
| 100 Code_ the CLT in action.en.srt |
24.54Кб |
| 100 Code_ the CLT in action.mp4 |
93.57Мб |
| 101 _Unsupervised learning__ Averaging pairs of numbers.en.srt |
3.32Кб |
| 101 _Unsupervised learning__ Averaging pairs of numbers.mp4 |
9.51Мб |
| 102 IVs, DVs, models, and other stats lingo.en.srt |
25.29Кб |
| 102 IVs, DVs, models, and other stats lingo.mp4 |
91.48Мб |
| 103 What is an hypothesis and how do you specify one_.en.srt |
24.33Кб |
| 103 What is an hypothesis and how do you specify one_.mp4 |
49.37Мб |
| 104 Sample distributions under null and alternative hypotheses.en.srt |
15.25Кб |
| 104 Sample distributions under null and alternative hypotheses.mp4 |
43.92Мб |
| 105 P-values_ definition, tails, and misinterpretations.en.srt |
27.94Кб |
| 105 P-values_ definition, tails, and misinterpretations.mp4 |
131.88Мб |
| 106 P-z combinations that you should memorize.en.srt |
9.39Кб |
| 106 P-z combinations that you should memorize.mp4 |
17.33Мб |
| 107 Degrees of freedom.en.srt |
19.38Кб |
| 107 Degrees of freedom.mp4 |
33.10Мб |
| 108 Type 1 and Type 2 errors.en.srt |
23.14Кб |
| 108 Type 1 and Type 2 errors.mp4 |
46.14Мб |
| 109 Parametric vs. non-parametric tests.en.srt |
13.35Кб |
| 109 Parametric vs. non-parametric tests.mp4 |
87.66Мб |
| 110 Multiple comparisons and Bonferroni correction.en.srt |
13.01Кб |
| 110 Multiple comparisons and Bonferroni correction.mp4 |
29.70Мб |
| 111 Statistical vs. theoretical vs. clinical significance.en.srt |
10.39Кб |
| 111 Statistical vs. theoretical vs. clinical significance.mp4 |
19.19Мб |
| 112 Cross-validation.en.srt |
17.07Кб |
| 112 Cross-validation.mp4 |
28.44Мб |
| 113 Statistical significance vs. classification accuracy.en.srt |
17.72Кб |
| 113 Statistical significance vs. classification accuracy.mp4 |
42.69Мб |
| 114 Purpose and interpretation of the t-test.en.srt |
19.67Кб |
| 114 Purpose and interpretation of the t-test.mp4 |
32.21Мб |
| 115 One-sample t-test.en.srt |
12.06Кб |
| 115 One-sample t-test.mp4 |
54.10Мб |
| 116 Code_ One-sample t-test.en.srt |
32.59Кб |
| 116 Code_ One-sample t-test.mp4 |
158.23Мб |
| 117 _Unsupervised learning__ The role of variance.en.srt |
4.28Кб |
| 117 _Unsupervised learning__ The role of variance.mp4 |
28.68Мб |
| 118 Two-samples t-test.en.srt |
19.73Кб |
| 118 Two-samples t-test.mp4 |
93.81Мб |
| 119 Code_ Two-samples t-test.en.srt |
33.52Кб |
| 119 Code_ Two-samples t-test.mp4 |
211.61Мб |
| 120 _Unsupervised learning__ Importance of N for t-test.en.srt |
7.14Кб |
| 120 _Unsupervised learning__ Importance of N for t-test.mp4 |
20.09Мб |
| 121 Wilcoxon signed-rank (nonparametric t-test).en.srt |
10.84Кб |
| 121 Wilcoxon signed-rank (nonparametric t-test).mp4 |
30.44Мб |
| 122 Code_ Signed-rank test.en.srt |
28.04Кб |
| 122 Code_ Signed-rank test.mp4 |
162.12Мб |
| 123 Mann-Whitney U test (nonparametric t-test).en.srt |
9.20Кб |
| 123 Mann-Whitney U test (nonparametric t-test).mp4 |
20.41Мб |
| 124 Code_ Mann-Whitney U test.en.srt |
8.07Кб |
| 124 Code_ Mann-Whitney U test.mp4 |
52.05Мб |
| 125 Permutation testing for t-test significance.en.srt |
17.00Кб |
| 125 Permutation testing for t-test significance.mp4 |
63.66Мб |
| 126 Code_ permutation testing.en.srt |
38.65Кб |
| 126 Code_ permutation testing.mp4 |
241.29Мб |
| 127 _Unsupervised learning__ How many permutations_.en.srt |
8.05Кб |
| 127 _Unsupervised learning__ How many permutations_.mp4 |
55.40Мб |
| 128 What are confidence intervals and why do we need them_.en.srt |
13.66Кб |
| 128 What are confidence intervals and why do we need them_.mp4 |
29.97Мб |
| 129 Computing confidence intervals via formula.en.srt |
10.30Кб |
| 129 Computing confidence intervals via formula.mp4 |
17.44Мб |
| 130 Code_ compute confidence intervals by formula.en.srt |
26.75Кб |
| 130 Code_ compute confidence intervals by formula.mp4 |
149.63Мб |
| 131 Confidence intervals via bootstrapping (resampling).en.srt |
13.33Кб |
| 131 Confidence intervals via bootstrapping (resampling).mp4 |
54.41Мб |
| 132 Code_ bootstrapping confidence intervals.en.srt |
22.62Кб |
| 132 Code_ bootstrapping confidence intervals.mp4 |
136.76Мб |
| 133 _Unsupervised learning__ Confidence intervals for variance.en.srt |
1.96Кб |
| 133 _Unsupervised learning__ Confidence intervals for variance.mp4 |
8.57Мб |
| 134 Misconceptions about confidence intervals.en.srt |
9.45Кб |
| 134 Misconceptions about confidence intervals.mp4 |
18.70Мб |
| 135 Motivation and description of correlation.en.srt |
28.49Кб |
| 135 Motivation and description of correlation.mp4 |
96.65Мб |
| 136 Covariance and correlation_ formulas.en.srt |
21.67Кб |
| 136 Covariance and correlation_ formulas.mp4 |
42.08Мб |
| 137 Code_ correlation coefficient.en.srt |
42.12Кб |
| 137 Code_ correlation coefficient.mp4 |
214.65Мб |
| 138 Code_ Simulate data with specified correlation.en.srt |
20.84Кб |
| 138 Code_ Simulate data with specified correlation.mp4 |
136.21Мб |
| 139 Correlation matrix.en.srt |
14.17Кб |
| 139 Correlation matrix.mp4 |
31.12Мб |
| 140 Code_ correlation matrix.en.srt |
33.24Кб |
| 140 Code_ correlation matrix.mp4 |
282.79Мб |
| 141 _Unsupervised learning__ average correlation matrices.en.srt |
4.23Кб |
| 141 _Unsupervised learning__ average correlation matrices.mp4 |
18.53Мб |
| 142 _Unsupervised learning__ correlation to covariance matrix.en.srt |
6.03Кб |
| 142 _Unsupervised learning__ correlation to covariance matrix.mp4 |
10.20Мб |
| 143 Partial correlation.en.srt |
16.04Кб |
| 143 Partial correlation.mp4 |
59.54Мб |
| 144 Code_ partial correlation.en.srt |
30.63Кб |
| 144 Code_ partial correlation.mp4 |
108.26Мб |
| 145 The problem with Pearson.en.srt |
10.28Кб |
| 145 The problem with Pearson.mp4 |
16.69Мб |
| 146 Nonparametric correlation_ Spearman rank.en.srt |
11.17Кб |
| 146 Nonparametric correlation_ Spearman rank.mp4 |
23.84Мб |
| 147 Fisher-Z transformation for correlations.en.srt |
10.25Кб |
| 147 Fisher-Z transformation for correlations.mp4 |
28.60Мб |
| 148 Code_ Spearman correlation and Fisher-Z.en.srt |
11.55Кб |
| 148 Code_ Spearman correlation and Fisher-Z.mp4 |
42.81Мб |
| 149 _Unsupervised learning__ Spearman correlation.en.srt |
1.92Кб |
| 149 _Unsupervised learning__ Spearman correlation.mp4 |
15.96Мб |
| 150 _Unsupervised learning__ confidence interval on correlation.en.srt |
3.44Кб |
| 150 _Unsupervised learning__ confidence interval on correlation.mp4 |
8.90Мб |
| 151 Kendall's correlation for ordinal data.en.srt |
15.85Кб |
| 151 Kendall's correlation for ordinal data.mp4 |
30.32Мб |
| 152 Code_ Kendall correlation.en.srt |
27.92Кб |
| 152 Code_ Kendall correlation.mp4 |
184.47Мб |
| 153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.en.srt |
3.50Кб |
| 153 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp4 |
14.95Мб |
| 154 Cosine similarity.en.srt |
7.78Кб |
| 154 Cosine similarity.mp4 |
14.28Мб |
| 155 Code_ Cosine similarity vs. Pearson correlation.en.srt |
32.54Кб |
| 155 Code_ Cosine similarity vs. Pearson correlation.mp4 |
102.53Мб |
| 156 ANOVA intro, part1.en.srt |
27.24Кб |
| 156 ANOVA intro, part1.mp4 |
137.94Мб |
| 157 ANOVA intro, part 2.en.srt |
29.58Кб |
| 157 ANOVA intro, part 2.mp4 |
84.60Мб |
| 158 Sum of squares.en.srt |
26.56Кб |
| 158 Sum of squares.mp4 |
46.02Мб |
| 159 The F-test and the ANOVA table.en.srt |
10.87Кб |
| 159 The F-test and the ANOVA table.mp4 |
20.02Мб |
| 160 The omnibus F-test and post-hoc comparisons.en.srt |
19.62Кб |
| 160 The omnibus F-test and post-hoc comparisons.mp4 |
63.61Мб |
| 161 The two-way ANOVA.en.srt |
30.60Кб |
| 161 The two-way ANOVA.mp4 |
104.77Мб |
| 162 One-way ANOVA example.en.srt |
21.47Кб |
| 162 One-way ANOVA example.mp4 |
44.53Мб |
| 163 Code_ One-way ANOVA (independent samples).en.srt |
26.84Кб |
| 163 Code_ One-way ANOVA (independent samples).mp4 |
172.94Мб |
| 164 Code_ One-way repeated-measures ANOVA.en.srt |
19.13Кб |
| 164 Code_ One-way repeated-measures ANOVA.mp4 |
73.30Мб |
| 165 Two-way ANOVA example.en.srt |
17.38Кб |
| 165 Two-way ANOVA example.mp4 |
35.83Мб |
| 166 Code_ Two-way mixed ANOVA.en.srt |
22.35Кб |
| 166 Code_ Two-way mixed ANOVA.mp4 |
114.36Мб |
| 167 Introduction to GLM _ regression.en.srt |
30.97Кб |
| 167 Introduction to GLM _ regression.mp4 |
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| 168 Least-squares solution to the GLM.en.srt |
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| 168 Least-squares solution to the GLM.mp4 |
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| 169 Evaluating regression models_ R2 and F.en.srt |
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| 169 Evaluating regression models_ R2 and F.mp4 |
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| 170 Simple regression.en.srt |
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| 170 Simple regression.mp4 |
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| 171 Code_ simple regression.en.srt |
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| 171 Code_ simple regression.mp4 |
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| 172 _Unsupervised learning__ Compute R2 and F.en.srt |
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| 172 _Unsupervised learning__ Compute R2 and F.mp4 |
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| 173 Multiple regression.en.srt |
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| 173 Multiple regression.mp4 |
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| 174 Standardizing regression coefficients.en.srt |
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| 174 Standardizing regression coefficients.mp4 |
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| 175 Code_ Multiple regression.en.srt |
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| 175 Code_ Multiple regression.mp4 |
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| 176 Polynomial regression models.en.srt |
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| 176 Polynomial regression models.mp4 |
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| 177 Code_ polynomial modeling.en.srt |
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| 177 Code_ polynomial modeling.mp4 |
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| 178 _Unsupervised learning__ Polynomial design matrix.en.srt |
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| 178 _Unsupervised learning__ Polynomial design matrix.mp4 |
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| 179 Logistic regression.en.srt |
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| 179 Logistic regression.mp4 |
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| 180 Code_ Logistic regression.en.srt |
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| 180 Code_ Logistic regression.mp4 |
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| 181 Under- and over-fitting.en.srt |
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| 181 Under- and over-fitting.mp4 |
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| 182 _Unsupervised learning__ Overfit data.en.srt |
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| 182 _Unsupervised learning__ Overfit data.mp4 |
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| 183 Comparing _nested_ models.en.srt |
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| 183 Comparing _nested_ models.mp4 |
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| 184 What to do about missing data.en.srt |
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| 184 What to do about missing data.mp4 |
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| 185 What is statistical power and why is it important_.en.srt |
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| 185 What is statistical power and why is it important_.mp4 |
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| 186 Estimating statistical power and sample size.en.srt |
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| 186 Estimating statistical power and sample size.mp4 |
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| 187 Compute power and sample size using G_Power.en.srt |
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| 187 Compute power and sample size using G_Power.mp4 |
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| 188 K-means clustering.en.srt |
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| 188 K-means clustering.mp4 |
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| 189 Code_ k-means clustering.en.srt |
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| 189 Code_ k-means clustering.mp4 |
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| 190 _Unsupervised learning__ K-means and normalization.en.srt |
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| 190 _Unsupervised learning__ K-means and normalization.mp4 |
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| 191 _Unsupervised learning__ K-means on a Gauss blur.en.srt |
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| 191 _Unsupervised learning__ K-means on a Gauss blur.mp4 |
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| 192 Clustering via dbscan.en.srt |
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| 192 Clustering via dbscan.mp4 |
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| 193 Code_ dbscan.en.srt |
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| 193 Code_ dbscan.mp4 |
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| 194 _Unsupervised learning__ dbscan vs. k-means.en.srt |
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| 194 _Unsupervised learning__ dbscan vs. k-means.mp4 |
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| 195 K-nearest neighbor classification.en.srt |
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| 195 K-nearest neighbor classification.mp4 |
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| 196 Code_ KNN.en.srt |
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| 196 Code_ KNN.mp4 |
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| 197 Principal components analysis (PCA).en.srt |
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| 197 Principal components analysis (PCA).mp4 |
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| 198 Code_ PCA.en.srt |
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| 198 Code_ PCA.mp4 |
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| 199 _Unsupervised learning__ K-means on PC data.en.srt |
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| 199 _Unsupervised learning__ K-means on PC data.mp4 |
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| 200 Independent components analysis (ICA).en.srt |
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| 200 Independent components analysis (ICA).mp4 |
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| 201 Code_ ICA.en.srt |
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| 201 Code_ ICA.mp4 |
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| 202 The two perspectives of the world.en.srt |
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| 202 The two perspectives of the world.mp4 |
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| 203 d-prime.en.srt |
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| 203 d-prime.mp4 |
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| 204 Code_ d-prime.en.srt |
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| 204 Code_ d-prime.mp4 |
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| 205 Response bias.en.srt |
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| 205 Response bias.mp4 |
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| 206 Code_ Response bias.en.srt |
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| 206 Code_ Response bias.mp4 |
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| 207 Receiver operating characteristics (ROC).en.srt |
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| 207 Receiver operating characteristics (ROC).mp4 |
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| 208 Code_ ROC curves.en.srt |
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| 208 Code_ ROC curves.mp4 |
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| 209 _Unsupervised learning__ Make this plot look nicer!.en.srt |
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| 209 _Unsupervised learning__ Make this plot look nicer!.mp4 |
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| 210 About deep learning.html |
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| 211 Bonus content.html |
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