Course Curriculum

Introduction
What does the course cover? 00:00:00
Sample or population data ?
Understanding the difference between a population and a sample 00:00:00
The fundamentals of regression analysis
The various types of data we can work with 00:00:00
Levels of measurement 00:00:00
Categorical variables. Visualization techniques for categorical variables 00:00:00
Numerical variables. Using a frequency distribution table 00:00:00
Histogram charts 00:00:00
Cross tables and scatter plots 00:00:00
Measures of central tendency, asymmetry, and variability
The main measures of central tendency: mean, median and mode 00:00:00
Measuring skewness 00:00:00
Measuring how data is spread out: calculating variance 00:00:00
Standard deviation and coefficient of variation 00:00:00
The various types of data we can work with 00:00:00
Calculating and understanding covariance 00:00:00
The correlation coefficient 00:00:00
Practical example: descriptive statistics
Practical example 00:00:00
Distributions
Introduction to inferential statistics 00:00:00
What is a distribution? 00:00:00
The Normal distribution 00:00:00
The standard normal distribution 00:00:00
Understanding the central limit theorem 00:00:00
Standard error 00:00:00
Estimators and estimates
Working with estimators and estimates 00:00:00
Confidence intervals – an invaluable tool for decision making 00:00:00
Calculating confidence intervals within a population with a known variance 00:00:00
Confidence interval clarifications 00:00:00
Student’s T distribution 00:00:00
Calculating confidence intervals within a population with an unknown variance 00:00:00
What is a margin of error and why is it important in Statistics? 00:00:00
Confidence intervals: advanced topics
Calculating confidence intervals for two means with dependent samples 00:00:00
Calculating confidence intervals for two means with independent samples (part 1) 00:00:00
Calculating confidence intervals for two means with independent samples (part 2) 00:00:00
Calculating confidence intervals for two means with independent samples (part 3) 00:00:00
Practical example: inferential statistics
Practical example: inferential statistics 00:00:00
Hypothesis testing: Introduction
The null and the alternative hypothesis 00:00:00
Establishing a rejection region and a significance level 00:00:00
Type I error vs Type II error 00:00:00
Hypothesis testing: Lets start testing!
Test for the mean. Population variance known 00:00:00
What is the p-value and why is it one of the most useful tools for statisticians 00:00:00
Test for the mean. Population variance unknown 00:00:00
Test for the mean. Dependent samples 00:00:00
Test for the mean. Independent samples (Part 1) 00:00:00
Test for the mean. Independent samples (Part 2) 00:00:00
Practical example:hypothesis testing
Practical example: hypothesis testing 00:00:00
The fundamentals of regression analysis
Introduction to regression analysis 00:00:00
Correlation and causation 00:00:00
The linear regression model made easy 00:00:00
What is the difference between correlation and regression? 00:00:00
A geometrical representation of the linear regression model 00:00:00
A practical example – Reinforced learning 00:00:00
Subtleties of regression analysis
Decomposing the linear regression model – understanding its nuts and bolts 00:00:00
What is R-squared and how does it help us? 00:00:00
The ordinary least squares setting and its practical applications 00:00:00
Studying regression tables 00:00:00
The multiple linear regression model 00:00:00
The adjusted R-squared 00:00:00
What does the F-statistic show us and why do we need to understand it? 00:00:00
Assumptions for linear regression analysis
OLS assumptions 00:00:00
A1. Linearity 00:00:00
A2. No endogeneity 00:00:00
A3. Normality and homoscedasticity 00:00:00
A4. No autocorrelation 00:00:00
A5. No multicollinearity 00:00:00
Dealing with categorical data
Dummy variables 00:00:00
Practical example: regression analysis
Practical example: regression analysis 00:00:00
Bonus lecture

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