#### 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