### 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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