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Course content
Sections:
17
•
Activities:
0
•
Resources:
90
- 1. What does the course cover
- Statistics Glossary
- 1. Understanding the difference between a population and a sample
- Glossary
- Course notes_descriptive_statistics
- Population vs sample
- 1. The various types of data we can work with
- 3. Levels of measurement
- 5. Categorical variables. Visualization techniques for categorical variables
- 8. Numerical variables. Using a frequency distribution table
- 11. Histogram charts
- 14. Cross tables and scatter plots
- HTML Files
- PDF Files
- XLS files
- 1. The main measures of central tendency mean, median and mode.
- 3. Measuring skewness
- 6. Measuring how data is spread out calculating variance
- 8. Standard deviation and coefficient of variation
- 11. Calculating and understanding covariance
- 13. The correlation coefficient
- HTML Files
- XLSX Files
- 1. Practical example
- Files
- 1. Introduction to inferential statistics
- 2. What is a distribution
- 4. The Normal distributio
- 6. The standard normal distribution
- 9. Understanding the central limit theorem
- 11. Standard error
- HTML Files
- PDF Files
- XLS Files
- 1. Working with estimators and estimates
- 3. Confidence intervals - an invaluable tool for decision making
- 5. Calculating confidence intervals within a population with a known variance
- 7. Confidence interval clarifications
- 8. Student's T distribution.
- 10. Calculating confidence intervals within a population with an unknown variance
- 12. What is a margin of error and why is it important in Statistics
- XLS Files
- 1. Calculating confidence intervals for two means with dependent samples
- 3. Calculating confidence intervals for two means with independent samples (part 1)
- 5. Calculating confidence intervals for two means with independent samples (part 2)
- 7. Calculating confidence intervals for two means with independent samples (part 3)
- HTML Files
- XLS Files
- 1. Practical example inferential statistics
- Filles
- 1. The null and the alternative hypothesis
- 4. Establishing a rejection region and a significance level
- 6. Type I error vs Type II error
- Files
- 1. Test for the mean. Population variance known
- 3. What is the p-value and why is it one of the most useful tools for statisticians
- 5. Test for the mean. Population variance unknown
- 7. Test for the mean. Dependent samples
- 9. Test for the mean. Independent samples (Part 1)
- 11. Test for the mean. Independent samples (Part 2)
- HTML and PDF FIles
- XLSX Files
- 1. Practical example hypothesis testing
- Files
- 1. Introduction to regression analysis
- 3. Correlation and causation
- 5. The linear regression model made easy
- 7. What is the difference between correlation and regression
- 9. A geometrical representation of the linear regression model
- 11. A practical example - Reinforced learning
- Files
- 1. Decomposing the linear regression model - understanding its nuts and bolts
- 3. What is R-squared and how does it help us
- 5. The ordinary least squares setting and its practical applications
- 7. Studying regression tables
- 10. The multiple linear regression model
- 12. The adjusted R-squared
- 14. What does the F-statistic show us and why do we need to understand it
- Files
- 1. OLS assumptions
- 3. A1. Linearity
- 5. A2. No endogeneity
- 7. A3. Normality and homoscedasticity
- 9. A4. No autocorrelation
- 11. A5. No multicollinearit
- Files
- 1. Dummy variables
- 1.1 5.20. Dummy variables_lesson
- 1. Practical example regression analysis
- 1.1 5.21. Regression_Analysis_practical_example