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Урок 1.
00:04:29
[Important] Getting the most out of this course
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Урок 2.
00:04:10
About using MATLAB or Python
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Урок 3.
00:08:48
Statistics guessing game!
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Урок 4.
00:05:17
Using the Q&A forum
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Урок 5.
00:01:53
(optional) Entering time-stamped notes in the Udemy video player
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Урок 6.
00:03:13
Should you memorize statistical formulas?
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Урок 7.
00:04:03
Arithmetic and exponents
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Урок 8.
00:05:54
Scientific notation
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Урок 9.
00:04:22
Summation notation
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Урок 10.
00:03:05
Absolute value
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Урок 11.
00:05:54
Natural exponent and logarithm
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Урок 12.
00:08:59
The logistic function
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Урок 13.
00:06:31
Rank and tied-rank
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Урок 14.
00:03:49
Download materials for the entire course!
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Урок 15.
00:01:54
Is "data" singular or plural?!?!!?!
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Урок 16.
00:06:10
Where do data come from and what do they mean?
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Урок 17.
00:14:57
Types of data: categorical, numerical, etc
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Урок 18.
00:08:59
Code: representing types of data on computers
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Урок 19.
00:12:03
Sample vs. population data
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Урок 20.
00:05:32
Samples, case reports, and anecdotes
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Урок 21.
00:06:58
The ethics of making up data
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Урок 22.
00:11:39
Bar plots
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Урок 23.
00:17:00
Code: bar plots
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Урок 24.
00:05:42
Box-and-whisker plots
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Урок 25.
00:08:42
Code: box plots
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Урок 26.
00:02:32
"Unsupervised learning": Boxplots of normal and uniform noise
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Урок 27.
00:11:17
Histograms
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Урок 28.
00:16:41
Code: histograms
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Урок 29.
00:02:23
"Unsupervised learning": Histogram proportion
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Урок 30.
00:06:00
Pie charts
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Урок 31.
00:13:23
Code: pie charts
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Урок 32.
00:06:12
When to use lines instead of bars
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Урок 33.
00:09:05
Linear vs. logarithmic axis scaling
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Урок 34.
00:07:25
Code: line plots
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Урок 35.
00:01:45
"Unsupervised learning": log-scaled plots
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Урок 36.
00:04:32
Descriptive vs. inferential statistics
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Урок 37.
00:07:30
Accuracy, precision, resolution
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Урок 38.
00:11:27
Data distributions
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Урок 39.
00:32:09
Code: data from different distributions
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Урок 40.
00:01:58
"Unsupervised learning": histograms of distributions
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Урок 41.
00:05:30
The beauty and simplicity of Normal
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Урок 42.
00:12:48
Measures of central tendency (mean)
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Урок 43.
00:12:18
Measures of central tendency (median, mode)
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Урок 44.
00:13:59
Code: computing central tendency
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Урок 45.
00:03:09
"Unsupervised learning": central tendencies with outliers
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Урок 46.
00:17:49
Measures of dispersion (variance, standard deviation)
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Урок 47.
00:26:34
Code: Computing dispersion
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Урок 48.
00:04:54
Interquartile range (IQR)
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Урок 49.
00:15:59
Code: IQR
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Урок 50.
00:07:23
QQ plots
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Урок 51.
00:15:35
Code: QQ plots
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Урок 52.
00:08:24
Statistical "moments"
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Урок 53.
00:10:01
Histograms part 2: Number of bins
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Урок 54.
00:12:25
Code: Histogram bins
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Урок 55.
00:03:20
Violin plots
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Урок 56.
00:10:10
Code: violin plots
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Урок 57.
00:02:32
"Unsupervised learning": asymmetric violin plots
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Урок 58.
00:11:03
Shannon entropy
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Урок 59.
00:20:16
Code: entropy
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Урок 60.
00:01:27
"Unsupervised learning": entropy and number of bins
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Урок 61.
00:04:11
Garbage in, garbage out (GIGO)
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Урок 62.
00:09:26
Z-score standardization
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Урок 63.
00:12:51
Code: z-score
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Урок 64.
00:05:07
Min-max scaling
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Урок 65.
00:08:17
Code: min-max scaling
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Урок 66.
00:02:36
"Unsupervised learning": Invert the min-max scaling
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Урок 67.
00:14:27
What are outliers and why are they dangerous?
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Урок 68.
00:09:27
Removing outliers: z-score method
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Урок 69.
00:04:05
The modified z-score method
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Урок 70.
00:22:31
Code: z-score for outlier removal
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Урок 71.
00:02:39
"Unsupervised learning": z vs. modified-z
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Урок 72.
00:09:27
Multivariate outlier detection
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Урок 73.
00:09:02
Code: Euclidean distance for outlier removal
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Урок 74.
00:05:48
Removing outliers by data trimming
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Урок 75.
00:11:04
Code: Data trimming to remove outliers
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Урок 76.
00:04:41
Non-parametric solutions to outliers
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Урок 77.
00:03:05
An outlier lecture on personal accountability
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Урок 78.
00:12:18
What is probability?
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Урок 79.
00:09:26
Probability vs. proportion
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Урок 80.
00:10:29
Computing probabilities
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Урок 81.
00:14:35
Code: compute probabilities
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Урок 82.
00:04:59
Probability and odds
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Урок 83.
00:02:31
"Unsupervised learning": probabilities of odds-space
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Урок 84.
00:13:07
Probability mass vs. density
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Урок 85.
00:11:38
Code: compute probability mass functions
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Урок 86.
00:10:45
Cumulative probability distributions
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Урок 87.
00:09:42
Code: cdfs and pdfs
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Урок 88.
00:02:26
"Unsupervised learning": cdf's for various distributions
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Урок 89.
00:18:32
Creating sample estimate distributions
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Урок 90.
00:02:54
Monte Carlo sampling
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Урок 91.
00:08:42
Sampling variability, noise, and other annoyances
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Урок 92.
00:26:16
Code: sampling variability
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Урок 93.
00:10:10
Expected value
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Урок 94.
00:12:46
Conditional probability
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Урок 95.
00:20:13
Code: conditional probabilities
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Урок 96.
00:06:25
Tree diagrams for conditional probabilities
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Урок 97.
00:09:51
The Law of Large Numbers
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Урок 98.
00:19:24
Code: Law of Large Numbers in action
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Урок 99.
00:10:35
The Central Limit Theorem
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Урок 100.
00:16:22
Code: the CLT in action
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Урок 101.
00:02:10
"Unsupervised learning": Averaging pairs of numbers
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Урок 102.
00:16:46
IVs, DVs, models, and other stats lingo
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Урок 103.
00:15:09
What is an hypothesis and how do you specify one?
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Урок 104.
00:10:39
Sample distributions under null and alternative hypotheses
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Урок 105.
00:18:57
P-values: definition, tails, and misinterpretations
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Урок 106.
00:06:52
P-z combinations that you should memorize
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Урок 107.
00:12:22
Degrees of freedom
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Урок 108.
00:14:19
Type 1 and Type 2 errors
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Урок 109.
00:09:13
Parametric vs. non-parametric tests
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Урок 110.
00:08:34
Multiple comparisons and Bonferroni correction
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Урок 111.
00:06:52
Statistical vs. theoretical vs. clinical significance
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Урок 112.
00:11:31
Cross-validation
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Урок 113.
00:11:13
Statistical significance vs. classification accuracy
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Урок 114.
00:13:14
Purpose and interpretation of the t-test
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Урок 115.
00:08:10
One-sample t-test
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Урок 116.
00:20:47
Code: One-sample t-test
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Урок 117.
00:02:51
"Unsupervised learning": The role of variance
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Урок 118.
00:13:07
Two-samples t-test
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Урок 119.
00:22:10
Code: Two-samples t-test
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Урок 120.
00:04:46
"Unsupervised learning": Importance of N for t-test
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Урок 121.
00:07:37
Wilcoxon signed-rank (nonparametric t-test)
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Урок 122.
00:18:35
Code: Signed-rank test
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Урок 123.
00:06:04
Mann-Whitney U test (nonparametric t-test)
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Урок 124.
00:05:22
Code: Mann-Whitney U test
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Урок 125.
00:11:27
Permutation testing for t-test significance
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Урок 126.
00:25:27
Code: permutation testing
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Урок 127.
00:05:22
"Unsupervised learning": How many permutations?
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Урок 128.
00:08:46
What are confidence intervals and why do we need them?
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Урок 129.
00:06:44
Computing confidence intervals via formula
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Урок 130.
00:17:12
Code: compute confidence intervals by formula
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Урок 131.
00:08:59
Confidence intervals via bootstrapping (resampling)
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Урок 132.
00:14:34
Code: bootstrapping confidence intervals
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Урок 133.
00:01:26
"Unsupervised learning:" Confidence intervals for variance
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Урок 134.
00:06:23
Misconceptions about confidence intervals
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Урок 135.
00:18:21
Motivation and description of correlation
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Урок 136.
00:14:10
Covariance and correlation: formulas
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Урок 137.
00:27:50
Code: correlation coefficient
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Урок 138.
00:13:51
Code: Simulate data with specified correlation
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Урок 139.
00:09:35
Correlation matrix
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Урок 140.
00:20:26
Code: correlation matrix
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Урок 141.
00:02:52
"Unsupervised learning": average correlation matrices
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Урок 142.
00:04:17
"Unsupervised learning": correlation to covariance matrix
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Урок 143.
00:10:24
Partial correlation
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Урок 144.
00:19:56
Code: partial correlation
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Урок 145.
00:06:44
The problem with Pearson
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Урок 146.
00:07:18
Nonparametric correlation: Spearman rank
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Урок 147.
00:06:55
Fisher-Z transformation for correlations
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Урок 148.
00:07:41
Code: Spearman correlation and Fisher-Z
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Урок 149.
00:01:29
"Unsupervised learning": Spearman correlation
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Урок 150.
00:02:26
"Unsupervised learning": confidence interval on correlation
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Урок 151.
00:10:38
Kendall's correlation for ordinal data
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Урок 152.
00:18:10
Code: Kendall correlation
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Урок 153.
00:02:40
"Unsupervised learning": Does Kendall vs. Pearson matter?
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Урок 154.
00:04:42
The subgroups correlation paradox
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Урок 155.
00:05:27
Cosine similarity
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Урок 156.
00:21:20
Code: Cosine similarity vs. Pearson correlation
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Урок 157.
00:17:52
ANOVA intro, part1
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Урок 158.
00:19:57
ANOVA intro, part 2
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Урок 159.
00:18:14
Sum of squares
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Урок 160.
00:07:29
The F-test and the ANOVA table
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Урок 161.
00:12:39
The omnibus F-test and post-hoc comparisons
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Урок 162.
00:19:53
The two-way ANOVA
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Урок 163.
00:13:25
One-way ANOVA example
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Урок 164.
00:16:35
Code: One-way ANOVA (independent samples)
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Урок 165.
00:12:18
Code: One-way repeated-measures ANOVA
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Урок 166.
00:11:18
Two-way ANOVA example
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Урок 167.
00:14:29
Code: Two-way mixed ANOVA
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Урок 168.
00:19:54
Introduction to GLM / regression
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Урок 169.
00:09:47
Least-squares solution to the GLM
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Урок 170.
00:16:18
Evaluating regression models: R2 and F
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Урок 171.
00:13:18
Simple regression
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Урок 172.
00:09:13
Code: simple regression
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Урок 173.
00:01:06
"Unsupervised learning": Compute R2 and F
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Урок 174.
00:13:02
Multiple regression
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Урок 175.
00:12:19
Standardizing regression coefficients
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Урок 176.
00:18:43
Code: Multiple regression
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Урок 177.
00:08:57
Polynomial regression models
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Урок 178.
00:15:47
Code: polynomial modeling
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Урок 179.
00:00:53
"Unsupervised learning": Polynomial design matrix
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Урок 180.
00:16:56
Logistic regression
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Урок 181.
00:09:28
Code: Logistic regression
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Урок 182.
00:16:20
Under- and over-fitting
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Урок 183.
00:01:57
"Unsupervised learning": Overfit data
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Урок 184.
00:12:26
Comparing "nested" models
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Урок 185.
00:06:37
What to do about missing data
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Урок 186.
00:09:48
What is statistical power and why is it important?
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Урок 187.
00:11:23
Estimating statistical power and sample size
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Урок 188.
00:04:11
Compute power and sample size using G*Power
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Урок 189.
00:13:47
K-means clustering
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Урок 190.
00:22:12
Code: k-means clustering
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Урок 191.
00:01:54
"Unsupervised learning:" K-means and normalization
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Урок 192.
00:01:27
"Unsupervised learning:" K-means on a Gauss blur
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Урок 193.
00:14:19
Clustering via dbscan
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Урок 194.
00:33:04
Code: dbscan
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Урок 195.
00:03:05
"Unsupervised learning": dbscan vs. k-means
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Урок 196.
00:06:21
K-nearest neighbor classification
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Урок 197.
00:11:49
Code: KNN
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Урок 198.
00:16:35
Principal components analysis (PCA)
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Урок 199.
00:17:33
Code: PCA
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Урок 200.
00:01:36
"Unsupervised learning:" K-means on PC data
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Урок 201.
00:12:46
Independent components analysis (ICA)
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Урок 202.
00:12:41
Code: ICA
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Урок 203.
00:05:30
The two perspectives of the world
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Урок 204.
00:12:31
d-prime
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Урок 205.
00:15:03
Code: d-prime
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Урок 206.
00:08:03
Response bias
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Урок 207.
00:04:16
Code: Response bias
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Урок 208.
00:07:35
Receiver operating characteristics (ROC)
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Урок 209.
00:08:11
Code: ROC curves
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Урок 210.
00:01:34
"Unsupervised learning": Make this plot look nicer!