- What does the course cover?
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- Understanding the difference between a population and a sample
- Population vs sample
- 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
- 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
- Practical example: descriptive statistics
- 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
- 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
- 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
- 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
- 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)