Section 1 : Introduction
Lecture 1 What does the course cover 00:03:54 Duration
Lecture 2 About Certification

Section 2 : Sample or population data?
Lecture 1 Understanding the difference between a population 00:04:02 Duration
Lecture 2 The various types of data we can work with 00:04:33 Duration
Lecture 3 Levels of measuremen 00:03:44 Duration
Lecture 4 Categorical variables. Visualization techniques fo 00:04:53 Duration

Section 3 : The fundamentals of descriptive statistics
Lecture 1 Categorical variables.VisualizationtechniquesExerc
Lecture 2 Numerical variables. Using a frequency distributio 00:03:10 Duration
Lecture 3 Numerical variables. Using a frequencydistribution
Lecture 4 Histogram charts 00:02:14 Duration
Lecture 5 Histogram charts. Exercise
Lecture 6 Cross tables and scatter plots 00:04:44 Duration
Lecture 7 Cross tables and scatter plots. Exercise

Section 4 : Measures of central tendency, asymmetry, and variability
Lecture 1 The main measures of central tendency- mean, media 00:04:20 Duration
Lecture 2 Mean, median and mode. Exercise
Lecture 3 Measuring skewness 00:02:38 Duration
Lecture 4 Skewness. Exercise
Lecture 5 Measuring how data is spread out- calculating var
Lecture 6 Variance. Exercise
Lecture 7 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 8 Standard deviation and coefficientofvariation.Exer
Lecture 9 Calculating and understanding covariance 00:03:24 Duration
Lecture 10 Covariance. Exercise
Lecture 11 The correlation coefficient 00:03:18 Duration
Lecture 12 Correlation coefficient

Section 5 : Practical example descriptive statistics
Lecture 1 Practical example 00:16:16 Duration
Lecture 2 Practical exampledescriptivestatistics

Section 6 : Distributions
Lecture 1 Introduction to inferential statistics 00:01:01 Duration
Lecture 2 What is a distribution 00:04:33 Duration
Lecture 3 The Normal distribution 00:03:54 Duration
Lecture 4 The standard normal distribution 00:03:31 Duration
Lecture 5 Standard NormalDistribution
Lecture 6 Understanding the central limit theorem 00:04:20 Duration
Lecture 7 Standard error 00:01:27 Duration

Section 7 : Estimators and estimates
Lecture 1 Working with estimators and estimates 00:03:07 Duration
Lecture 2 Confidence intervals - an invaluable tool for deci 00:02:42 Duration
Lecture 3 Calculating confidence intervals within a populati 00:08:01 Duration
Lecture 4 Confidence intervals.Populationvarianceknown
Lecture 5 Confidence interval clarifications 00:04:39 Duration
Lecture 6 Student's T distribution
Lecture 7 Calculating confidence intervals within a populati 00:04:37 Duration
Lecture 8 Population variance unknown. Tscore.Exercise
Lecture 9 What is a margin of error and why is it important 00:04:53 Duration

Section 8 : Confidence intervals advanced topics
Lecture 1 Calculating confidence intervals for two means wit 00:06:04 Duration
Lecture 2 Confidence intervals.Twomeans.Dependentsamples.Exe
Lecture 3 Calculating confidence intervals for two means wit 00:04:31 Duration
Lecture 4 Confidence intervals. Two means.Independentsamples
Lecture 5 Calculating confidence intervals for two means wit 00:03:57 Duration
Lecture 6 Confidence intervals. Two means.Independentsamples
Lecture 7 Calculating confidence intervals for two means wit 00:01:27 Duration

Section 9 : Practical example inferential statistics
Lecture 1 Practical example- inferential statistics 00:10:06 Duration
Lecture 2 Practical example inferential statistics

Section 10 : Hypothesis testingIntroduction
Lecture 1 The null and the alternative hypothesis 00:05:52 Duration
Lecture 2 About Proctor Testing
Lecture 3 Remove - INTRODUCTION TO BRAINMEASURES PROCTOR SYS
Lecture 4 Type I error vs Type II error 00:04:14 Duration

Section 11 : Hypothesis testing Let's start testing!
Lecture 1 Test for the mean. Population variance known 00:06:34 Duration
Lecture 2 Test for the mean.Populationvarianceknown.Exercise
Lecture 3 What is the p-value and why is it one of the most 00:04:13 Duration
Lecture 4 Test for the mean. Population variance unknown 00:04:49 Duration
Lecture 5 Test for the mean.Populationvarianceunknown.Exerci
Lecture 6 Test for the mean. Dependent sample 00:05:18 Duration
Lecture 7 Test for the mean.Dependentsamples
Lecture 8 Test for the mean. Independent samples (Part 1) 00:04:22 Duration
Lecture 9 Test for the mean.Independentsamples(Part1
Lecture 10 Test for the mean. Independent samples (Part 2) 00:04:27 Duration
Lecture 11 Test for the mean.Independentsamples (Par2). Exerc

Section 12 : Practical example hypothesis testing
Lecture 1 Practical example- hypothesis testing 00:07:16 Duration
Lecture 2 Practical examplehypothesis testin

Section 13 : The fundamentals of regression analysis
Lecture 1 Introduction to regression analysis 00:01:02 Duration
Lecture 2 Correlation and causation
Lecture 3 The linear regression model made easy 00:05:50 Duration
Lecture 4 What is the difference between correlation and reg 00:01:44 Duration
Lecture 5 A geometrical representation of the linear regress 00:01:26 Duration
Lecture 6 A practical example - Reinforced learning 00:05:46 Duration

Section 14 : Subtleties of regression analysis
Lecture 1 Decomposing the linear regression model - understa 00:03:38 Duration
Lecture 2 What is R-squared and how does it help us 00:05:24 Duration
Lecture 3 The ordinary least squares setting and its practic 00:02:24 Duration
Lecture 4 Studying regression tables 00:04:54 Duration
Lecture 5 Regression tables.Exercise
Lecture 6 The multiple linear regression model 00:02:56 Duration
Lecture 7 The adjusted R-squared
Lecture 8 What does the F-statistic show us and why do we ne 00:02:01 Duration

Section 15 : Assumptions for linear regression analysis
Lecture 1 OLS assumptions 00:02:21 Duration
Lecture 2 A1. Linearity
Lecture 3 A2. No endogeneity 00:04:09 Duration
Lecture 4 A3. Normality and homo scedasticity 00:05:48 Duration
Lecture 5 A4. No autocorrelation 00:03:15 Duration
Lecture 6 A5. No multicollinearity 00:05:03 Duration

Section 16 : Dealing with categorical date
Lecture 1 Dummy variables 00:05:03 Duration

Section 17 : Practical example regression analysis
Lecture 1 Practical example- regression analysis 00:14:10 Duration