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