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Welcome
1. Preparatory Work
1.2. Datacamp
1.3. The
for
loop
1.4. Rolling a virtual dice I
1.5. Rolling a virtual dice II
1.6. Simulation Exercises
2. Describing data
2.2. Learning Objectives
2.3. Concepts Review
2.4. Mean and Median
2.5. Standard deviation and IQR
2.6. Data cleaning
2.7. Grouping data
2.8. Python skills check
2.9. Tutorial exercises I
2.11. Tutorial Exercises II - optional
3. Plotting Data
3.2. Learning Objectives
3.3. Histogram
3.4. KDE plot
3.5. Boxplot
3.6. Scatterplot
3.7. Plotting with Pandas
3.8. Tweaking plots
3.9. More resources
3.10. Tutorial Exercises
4. Correlation and Covariance
4.2. Learning Objectives
4.3. Concepts Review
4.4. Covariance and Correlation
4.6. Climate example
4.7. Spearman’s Rank Correlation
4.8. Python skills check
4.9. Tutorial Exercises
4.10. Hand-in Assignment
5. Data generating distributions
5.1. The Binomial Distribution
5.2. Learning Objectives
5.3. Simulated coin toss
5.4. Repeat the simulation
5.5. Changing $n$ and $p$
5.6. Binomial PMF and CDF
5.7. Normal distribution
5.8. Python skills check
5.9. Tutorial Exercises I
5.10. Tutorial Exercises II
6. The Central Limit Theorem
6.2. Learning Objectives
6.3. Central Limit Theorem
6.5. Estimating the sampling distribution
from a sample
6.7. Tutorial exercises I: The Central Limit Theorem
6.10. Tutorial Exercises II: the $t$ distribution
7. The Bootstrap
7.2. Learning Objectives
7.3. Sampling with and without replacement
7.4. The bootstrap
7.6. Tutorial Exercises
8. Christmas vacation assignment
8.1. Movie data
9. Python Cheatsheet
9.1. Coding support sheet
10. Permutation Testing
10.2. Learning Objectives
10.3. Sort out
scipy.stats
version
10.4. Permutation test for paired data
10.5. Permutation test for unpaired or independent samples data
10.6. Permutation test for correlation
10.7. Permutation testing - more practice
10.8. Tutorial exercises
11. Classic tests
11.3. The t-test
11.4. Independent samples t-test
11.5. Paired Samples t-test
11.6. One sample t-test
11.7. Non-parametric equivalents of the t-test
11.8. The Mann Whitney U, or Wilcoxon Rank Sum Test
11.9. The Wilcoxon Sign-Rank Test
11.11. Tutorial Exercises: $t$-test and non-parametric equivalents
12. Power analysis
12.2. Learning Objectives
12.3. Concepts Review
12.4. Sensitivity, power, reproducibility
12.9. Tutorial work and Hand in assignment
12.10. Loading data from a .csv file
13. Regression
13.1. Learning Objectives
13.2. Regression Concepts
13.3. Interpreting the slope and intercept, prediction and residuals
13.4. Regression, Covariance and Correlation Q&A
13.5. Regression models in Python
13.7. Tutorial Exercises
14. Multivariate Analysis
14.2. Multivariate Analysis Concepts
14.3. Different types of multivariate relationship
14.4. Multiple Regression Q & A
14.5. Tutorial Exercises
15. Regression - Statistical Tests
15.2. Learning Objectives
15.3. Model fit: $R^2$
15.4. Conditional Distributions
15.5. Statistical Significance in Regression
15.6. Assumptions in Regression
15.7. Assessing Regression models in Python.
15.9. Tutorial Exercises
16. ANOVA
16.2. Learning Objectives
16.3. ANOVA Concepts
16.4. Longhand calculation example
16.5. ANOVA Equations
16.6. Kruskall-Wallis test
16.7. ANOVA versus Regression
16.8. ANOVA and Kruskal-Wallis in Python
16.9. Tutorial exercises
17. Logistic Regression
17.2. Learning Objectives
17.3. Odds, logs and log odds
17.4. Logistic Regression Equation
17.5. Predicted probabilities (worked example)
17.6. Logistic Regression in Python
17.7. Tutorial exercises
18. Research Design
18.2. Learning Objectives
18.3. Research Design Concepts
18.4. Causality
18.5. Tutorial exercises
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