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Course content
Sections:
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Activities:
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Resources:
221
- Announcements
- 001 Important Getting the most out of this course gitir
- 002 About using MATLAB or Python gitir
- 003 Statistics guessing game gitir
- 004 Using the Q A forum gitir
- 005 optional Entering time stamped notes in the Udemy video player gitir
- 006 Should you memorize statistical formulas gitir
- 007 Arithmetic and exponents gitir
- 008 Scientific notation gitir
- 009 Summation notation gitir
- 010 Absolute value gitir
- 011 Natural exponent and logarithm gitir
- 012 The logistic function gitir
- 013 Rank and tied rank gitir
- 014 Download materials for the entire course gitir
- 015 Is data singular or plural gitir
- 016 Where do data come from and what do they mean gitir
- 017 Types of data categorical numerical etc gitir
- 018 Code representing types of data on computers gitir
- 019 Sample vs population data gitir
- 020 Samples case reports and anecdotes gitir
- 021 The ethics of making up data gitir
- 022 Bar plots gitir
- 023 Code bar plots gitir
- 024 Box and whisker plots gitir
- 025 Code box plots gitir
- 026 Unsupervised learning Boxplots of normal and uniform noise gitir
- 027 Histograms gitir
- 028 Code histograms gitir
- 029 Unsupervised learning Histogram proportion gitir
- 030 Pie charts gitir
- 031 Code pie charts gitir
- 032 When to use lines instead of bars gitir
- 033 Linear vs logarithmic axis scaling gitir
- 034 Code line plots gitir
- 035 Unsupervised learning log scaled plots gitir
- 036 Descriptive vs inferential statistics gitir
- 037 Accuracy precision resolution gitir
- 038 Data distributions gitir
- 039 Code data from different distributions gitir
- 040 Unsupervised learning histograms of distributions gitir
- 041 The beauty and simplicity of Normal gitir
- 042 Measures of central tendency mean gitir
- 043 Measures of central tendency median mode gitir
- 044 Code computing central tendency gitir
- 045 Unsupervised learning central tendencies with outliers gitir
- 046 Measures of dispersion variance standard deviation gitir
- 047 Code Computing dispersion gitir
- 048 Interquartile range IQR gitir
- 049 Code IQR gitir
- 050 QQ plots gitir
- 051 Code QQ plots gitir
- 052 Statistical moments gitir
- 053 Histograms part 2 Number of bins gitir
- 054 Code Histogram bins gitir
- 055 Violin plots gitir
- 056 Code violin plots gitir
- 057 Unsupervised learning asymmetric violin plots gitir
- 058 Shannon entropy gitir
- 059 Code entropy gitir
- 060 Unsupervised learning entropy and number of bins gitir
- 061 Garbage in garbage out GIGO gitir
- 062 Z score standardization gitir
- 063 Code z score gitir
- 064 Min max scaling gitir
- 065 Code min max scaling gitir
- 066 Unsupervised learning Invert the min max scaling gitir
- 067 What are outliers and why are they dangerous gitir
- 068 Removing outliers z score method gitir
- 069 The modified z score method gitir
- 070 Code z score for outlier removal gitir
- 071 Unsupervised learning z vs modified z gitir
- 072 Multivariate outlier detection gitir
- 073 Code Euclidean distance for outlier removal gitir
- 074 Removing outliers by data trimming gitir
- 075 Code Data trimming to remove outliers gitir
- 076 Non parametric solutions to outliers gitir
- 077 Nonlinear data transformations gitir
- 078 An outlier lecture on personal accountability gitir
- 079 What is probability gitir
- 080 Probability vs proportion gitir
- 081 Computing probabilities gitir
- 082 Code compute probabilities gitir
- 083 Probability and odds gitir
- 084 Unsupervised learning probabilities of odds space gitir
- 085 Probability mass vs density gitir
- 086 Code compute probability mass functions gitir
- 087 Cumulative distribution functions gitir
- 088 Code cdfs and pdfs gitir
- 089 Unsupervised learning cdf s for various distributions gitir
- 090 Creating sample estimate distributions gitir
- 091 Monte Carlo sampling gitir
- 092 Sampling variability noise and other annoyances gitir
- 093 Code sampling variability gitir
- 094 Expected value gitir
- 095 Conditional probability gitir
- 096 Code conditional probabilities gitir
- 097 Tree diagrams for conditional probabilities gitir
- 098 The Law of Large Numbers gitir
- 099 Code Law of Large Numbers in action gitir
- 100 The Central Limit Theorem gitir
- 101 Code the CLT in action gitir
- 102 Unsupervised learning Averaging pairs of numbers gitir
- 103 IVs DVs models and other stats lingo gitir
- 104 What is an hypothesis and how do you specify one gitir
- 105 Sample distributions under null and alternative hypotheses gitir
- 106 P values definition tails and misinterpretations gitir
- 107 P z combinations that you should memorize gitir
- 108 Degrees of freedom gitir
- 109 Type 1 and Type 2 errors gitir
- 110 Parametric vs non parametric tests gitir
- 111 Multiple comparisons and Bonferroni correction gitir
- 112 Statistical vs theoretical vs clinical significance gitir
- 113 Cross validation gitir
- 114 Statistical significance vs classification accuracy gitir
- 115 Purpose and interpretation of the t test gitir
- 116 One sample t test gitir
- 117 Code One sample t test gitir
- 118 Unsupervised learning The role of variance gitir
- 119 Two samples t test gitir
- 120 Code Two samples t test gitir
- 121 Unsupervised learning Importance of N for t test gitir
- 122 Wilcoxon signed rank nonparametric t test gitir
- 123 Code Signed rank test gitir
- 124 Mann Whitney U test nonparametric t test gitir
- 125 Code Mann Whitney U test gitir
- 126 Permutation testing for t test significance gitir
- 127 Code permutation testing gitir
- 128 Unsupervised learning How many permutations gitir
- 129 What are confidence intervals and why do we need them gitir
- 130 Computing confidence intervals via formula gitir
- 131 Code compute confidence intervals by formula gitir
- 132 Confidence intervals via bootstrapping resampling gitir
- 133 Code bootstrapping confidence intervals gitir
- 134 Unsupervised learning Confidence intervals for variance gitir
- 135 Misconceptions about confidence intervals gitir
- 136 Motivation and description of correlation gitir
- 137 Covariance and correlation formulas gitir
- 138 Code correlation coefficient gitir
- 139 Code Simulate data with specified correlation gitir
- 140 Correlation matrix gitir
- 141 Code correlation matrix gitir
- 142 Unsupervised learning average correlation matrices gitir
- 143 Unsupervised learning correlation to covariance matrix gitir
- 144 Partial correlation gitir
- 145 Code partial correlation gitir
- 146 The problem with Pearson gitir
- 147 Nonparametric correlation Spearman rank gitir
- 148 Fisher Z transformation for correlations gitir
- 149 Code Spearman correlation and Fisher Z gitir
- 150 Unsupervised learning Spearman correlation gitir
- 151 Unsupervised learning confidence interval on correlation gitir
- 152 Kendall s correlation for ordinal data gitir
- 153 Code Kendall correlation gitir
- 154 Unsupervised learning Does Kendall vs Pearson matter gitir
- 155 The subgroups correlation paradox gitir
- 156 Cosine similarity gitir
- 157 Code Cosine similarity vs Pearson correlation gitir
- 158 ANOVA intro part1 gitir
- 159 ANOVA intro part 2 gitir
- 160 Sum of squares gitir
- 161 The F test and the ANOVA table gitir
- 162 The omnibus F test and post hoc comparisons gitir
- 163 The two way ANOVA gitir
- 164 One way ANOVA example gitir
- 165 Code One way ANOVA independent samples gitir
- 166 Code One way repeated measures ANOVA gitir
- 167 Two way ANOVA example gitir
- 168 Code Two way mixed ANOVA gitir
- 169 Introduction to GLM regression gitir
- 170 Least squares solution to the GLM gitir
- 171 Evaluating regression models R2 and F gitir
- 172 Simple regression gitir
- 173 Code simple regression gitir
- 174 Unsupervised learning Compute R2 and F gitir
- 175 Multiple regression gitir
- 176 Standardizing regression coefficients gitir
- 177 Code Multiple regression gitir
- 178 Polynomial regression models gitir
- 179 Code polynomial modeling gitir
- 180 Unsupervised learning Polynomial design matrix gitir
- 181 Logistic regression gitir
- 182 Code Logistic regression gitir
- 183 Under and over fitting gitir
- 184 Unsupervised learning Overfit data gitir
- 185 Comparing nested models gitir
- 186 What to do about missing data gitir
- 187 What is statistical power and why is it important gitir
- 188 Estimating statistical power and sample size gitir
- 189 Compute power and sample size using G Power gitir
- 190 K means clustering gitir
- 191 Code k means clustering gitir
- 192 Unsupervised learning K means and normalization gitir
- 193 Unsupervised learning K means on a Gauss blur gitir
- 194 Clustering via dbscan gitir
- 195 Code dbscan gitir
- 196 Unsupervised learning dbscan vs k means gitir
- 197 K nearest neighbor classification gitir
- 198 Code KNN gitir
- 199 Principal components analysis PCA gitir
- 200 Code PCA gitir
- 201 Unsupervised learning K means on PC data gitir
- 202 Independent components analysis ICA gitir
- 203 Code ICA gitir
- 204 The two perspectives of the world gitir
- 205 d prime gitir
- 206 Code d prime gitir
- 207 Response bias gitir
- 208 Code Response bias gitir
- 209 F score gitir
- 210 Receiver operating characteristics ROC gitir
- 211 Code ROC curves gitir
- 212 Unsupervised learning Make this plot look nicer gitir
- 214 Introduction gitir
- 215 MATLAB Import and clean the marriage data gitir
- 216 MATLAB Import the divorce data gitir
- 217 MATLAB More data visualizations gitir
- 218 MATLAB Inferential statistics gitir
- 219 Python Import and clean the marriage data gitir
- 220 Python Import the divorce data gitir
- 221 Python Inferential statistics gitir
- 222 Take home messages gitir