Section 1 : Introduction

 		lecture 1 What does the course cover 3:54
lecture 2 About Certification Pdf

Section 2 : Sample or population data?

 		lecture 3 Understanding the difference between a population 4:2
lecture 4 The various types of data we can work with 4:33
lecture 5 Levels of measuremen 3:44
lecture 6 Categorical variables. Visualization techniques fo 4:53

Section 3 : The fundamentals of descriptive statistics

 		lecture 7 Categorical variables.VisualizationtechniquesExerc Pdf
lecture 8 Numerical variables. Using a frequency distributio 3:10
lecture 9 Numerical variables. Using a frequencydistribution
lecture 10 Histogram charts 2:14
lecture 11 Histogram charts. Exercise Pdf
lecture 12 Cross tables and scatter plots 4:44
lecture 13 Cross tables and scatter plots. Exercise

Section 4 : Measures of central tendency, asymmetry, and variability

 		lecture 14 The main measures of central tendency- mean, media 4:20
lecture 15 Mean, median and mode. Exercise
lecture 16 Measuring skewness 2:38
lecture 17 Skewness. Exercise
lecture 18 Measuring how data is spread out- calculating var
lecture 19 Variance. Exercise
lecture 20 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
lecture 21 Standard deviation and coefficientofvariation.Exer
lecture 22 Calculating and understanding covariance 3:24
lecture 23 Covariance. Exercise
lecture 24 The correlation coefficient 3:18
lecture 25 Correlation coefficient

Section 5 : Practical example descriptive statistics

 		lecture 26 Practical example 16:16
lecture 27 Practical exampledescriptivestatistics

Section 6 : Distributions

 		lecture 28 Introduction to inferential statistics 1:1
lecture 29 What is a distribution 4:33
lecture 30 The Normal distribution 3:54
lecture 31 The standard normal distribution 3:31
lecture 32 Standard NormalDistribution
lecture 33 Understanding the central limit theorem 4:20
lecture 34 Standard error 1:27

Section 7 : Estimators and estimates

 		lecture 35 Working with estimators and estimates 3:7
lecture 36 Confidence intervals - an invaluable tool for deci 2:42
lecture 37 Calculating confidence intervals within a populati 8:1
lecture 38 Confidence intervals.Populationvarianceknown
lecture 39 Confidence interval clarifications 4:39
lecture 40 Student's T distribution
lecture 41 Calculating confidence intervals within a populati 4:37
lecture 42 Population variance unknown. Tscore.Exercise
lecture 43 What is a margin of error and why is it important 4:53

Section 8 : Confidence intervals advanced topics

 		lecture 44 Calculating confidence intervals for two means wit 6:4
lecture 45 Confidence intervals.Twomeans.Dependentsamples.Exe
lecture 46 Calculating confidence intervals for two means wit 4:31
lecture 47 Confidence intervals. Two means.Independentsamples
lecture 48 Calculating confidence intervals for two means wit 3:57
lecture 49 Confidence intervals. Two means.Independentsamples
lecture 50 Calculating confidence intervals for two means wit 1:27

Section 9 : Practical example inferential statistics

 		lecture 51 Practical example- inferential statistics 10:6
lecture 52 Practical example inferential statistics

Section 10 : Hypothesis testingIntroduction

 		lecture 53 The null and the alternative hypothesis 5:52
lecture 54 About Proctor Testing Pdf
lecture 55 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS Pdf
lecture 56 Type I error vs Type II error 4:14

Section 11 : Hypothesis testing Let's start testing!

 		lecture 57 Test for the mean. Population variance known 6:34
lecture 58 Test for the mean.Populationvarianceknown.Exercise
lecture 59 What is the p-value and why is it one of the most 4:13
lecture 60 Test for the mean. Population variance unknown 4:49
lecture 61 Test for the mean.Populationvarianceunknown.Exerci
lecture 62 Test for the mean. Dependent sample 5:18
lecture 63 Test for the mean.Dependentsamples
lecture 64 Test for the mean. Independent samples (Part 1) 4:22
lecture 65 Test for the mean.Independentsamples(Part1
lecture 66 Test for the mean. Independent samples (Part 2) 4:27
lecture 67 Test for the mean.Independentsamples (Par2). Exerc

Section 12 : Practical example hypothesis testing

 		lecture 68 Practical example- hypothesis testing 7:16
lecture 69 Practical examplehypothesis testin

Section 13 : The fundamentals of regression analysis

 		lecture 70 Introduction to regression analysis 1:2
lecture 71 Correlation and causation
lecture 72 The linear regression model made easy 5:50
lecture 73 What is the difference between correlation and reg 1:44
lecture 74 A geometrical representation of the linear regress 1:26
lecture 75 A practical example - Reinforced learning 5:46

Section 14 : Subtleties of regression analysis

 		lecture 76 Decomposing the linear regression model - understa 3:38
lecture 77 What is R-squared and how does it help us 5:24
lecture 78 The ordinary least squares setting and its practic 2:24
lecture 79 Studying regression tables 4:54
lecture 80 Regression tables.Exercise
lecture 81 The multiple linear regression model 2:56
lecture 82 The adjusted R-squared
lecture 83 What does the F-statistic show us and why do we ne 2:1

Section 15 : Assumptions for linear regression analysis

 		lecture 84 OLS assumptions 2:21
lecture 85 A1. Linearity
lecture 86 A2. No endogeneity 4:9
lecture 87 A3. Normality and homo scedasticity 5:48
lecture 88 A4. No autocorrelation 3:15
lecture 89 A5. No multicollinearity 5:3

Section 16 : Dealing with categorical date

 		lecture 90 Dummy variables 5:3

Section 17 : Practical example regression analysis

 		lecture 91 Practical example- regression analysis 14:10