Introduction
  • What does the course cover?
  • Download all resources
Sample or population data?
  • Understanding the difference between a population and a sample
  • Population vs sample
The fundamentals of descriptive statistics
  • The various types of data we can work with
  • Types of data
  • Levels of measurement
  • Levels of measurement
  • Categorical variables. Visualization techniques for categorical variables
  • Categorical variables. Visualization Techniques
  • Categorical variables. Visualization techniques. Exercise
  • Numerical variables. Using a frequency distribution table
  • Numerical variables. Using a frequency distribution table
  • Numerical variables. Using a frequency distribution table. Exercise
  • Histogram charts
  • Histogram charts
  • Histogram charts. Exercise
  • Cross tables and scatter plots
  • Cross Tables and Scatter Plots
  • Cross tables and scatter plots. Exercise
Measures of central tendency, asymmetry, and variability
  • The main measures of central tendency: mean, median and mode
  • Mean, median and mode. Exercise
  • Measuring skewness
  • Skewness
  • Skewness. Exercise
  • Measuring how data is spread out: calculating variance
  • Variance. Exercise
  • Standard deviation and coefficient of variation
  • Standard deviation
  • Standard deviation and coefficient of variation. Exercise
  • Calculating and understanding covariance
  • Covariance. Exercise
  • The correlation coefficient
  • Correlation
  • Correlation coefficient
Practical example: descriptive statistics
  • Practical example
  • Practical example: descriptive statistics
Distributions
  • Introduction to inferential statistics
  • What is a distribution?
  • What is a distribution
  • The Normal distribution
  • The Normal distribution
  • The standard normal distribution
  • The standard normal distribution
  • Standard Normal Distribution. Exercise
  • Understanding the central limit theorem
  • The central limit theorem
  • Standard error
  • Standard error
Estimators and estimates
  • Working with estimators and estimates
  • Estimators and estimates
  • Confidence intervals - an invaluable tool for decision making
  • Confidence intervals
  • Calculating confidence intervals within a population with a known variance
  • Confidence intervals. Population variance known. Exercise
  • Confidence interval clarifications
  • Student's T distribution
  • Student's T distribution
  • Calculating confidence intervals within a population with an unknown variance
  • Population variance unknown. T-score. Exercise
  • What is a margin of error and why is it important in Statistics?
  • Margin of error
Confidence intervals: advanced topics
  • Calculating confidence intervals for two means with dependent samples
  • Confidence intervals. Two means. Dependent samples. Exercise
  • Calculating confidence intervals for two means with independent samples (part 1)
  • Confidence intervals. Two means. Independent samples (Part 1). Exercise
  • Calculating confidence intervals for two means with independent samples (part 2)
  • Confidence intervals. Two means. Independent samples (Part 2). Exercise
  • Calculating confidence intervals for two means with independent samples (part 3)
Practical example: inferential statistics
  • Practical example: inferential statistics
  • Practical example: inferential statistics
Hypothesis testing: Introduction
  • The null and the alternative hypothesis
  • Further reading on null and alternative hypotheses
  • Null vs alternative
  • Establishing a rejection region and a significance level
  • Rejection region and significance level
  • Type I error vs Type II error
  • Type I error vs type II error
Hypothesis testing: Let's start testing!
  • Test for the mean. Population variance known
  • Test for the mean. Population variance known. Exercise
  • What is the p-value and why is it one of the most useful tools for statisticians
  • p-value
  • Test for the mean. Population variance unknown
  • Test for the mean. Population variance unknown. Exercise
  • Test for the mean. Dependent samples
  • Test for the mean. Dependent samples. Exercise
  • Test for the mean. Independent samples (Part 1)
  • Test for the mean. Independent samples (Part 1)
  • Test for the mean. Independent samples (Part 2)