Density plots

Now you'll be making a third type of graph: a density plot. This type of plot allows us to visualize a distribution in a somewhat detailed way. You'll make two density plots using the People dataset; one for Height and one for Weight.

plotnine has been imported as p9.

This exercise is part of the course

Performing Experiments in Python

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create density plot of Height
____(p9.ggplot(____)+ p9.____(x=____, fill=____)+ p9.____(alpha=0.5))

Edit and Run Code

This exercise is part of the course

Performing Experiments in Python

IntermediateSkill Level

4.9+

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In this chapter, you will learn how to explore your data and ask meaningful questions. Then, you will discover how to answer these question by using your first statistical hypothesis tests: the t-test, the Chi-Square test, the Fisher exact test, and the Pearson correlation test.

Exercise 1: Welcome to the course!Exercise 2: Getting started with plotnine Exercise 3: Boxplots Exercise 4: Density plots

Current Exercise

Exercise 5: Student's t-test Exercise 6: Your first t-test Exercise 7: One-sample t-test Exercise 8: Two-sample t-test Exercise 9: Testing proportion and correlation Exercise 10: Chi-square test Exercise 11: Fisher's exact test Exercise 12: Pearson correlation

In this chapter, you will learn how to examine and multiple factors at once, controlling for the effect of confounding variables and examining interactions between variables. You will learn how to use randomization and blocking to build robust tests and how to use the powerful ANOVA method.

Exercise 1: Confounding variables Exercise 2: Exploring confounding variables Exercise 3: Finding confounding variables Exercise 4: Blocking and randomization Exercise 5: Random sampling Exercise 6: Blocking Exercise 7: Paired t-test Exercise 8: ANOVA Exercise 9: One-way ANOVA Exercise 10: Two-way ANOVA Exercise 11: Interactive effects Exercise 12: Two-way ANOVA with interactive effects Exercise 13: Choosing an appropriate test

In this chapter, you will focus on ways to avoid drawing false conclusions, whether false positives (type I errors) or false negatives (type II errors). Central to avoiding false negatives is understanding the interplay between sample size, power analysis, and effect size.

Exercise 1: Type I errors Exercise 2: Bonferroni correction Exercise 3: Šídák correction Exercise 4: Sample size Exercise 5: Exploring sample size Exercise 6: Sample size for a t-test Exercise 7: Effect size Exercise 8: Effect size for a t-test Exercise 9: Computing Cohen's d Exercise 10: Effect size for a Fisher exact test Exercise 11: Effect sizes for Pearson correlation Exercise 12: Power Exercise 13: Power analysis for a t-test Exercise 14: Interpreting power analyses

In this final chapter, you will examine the assumptions underlying statistical tests and learn about how that influences your experimental design. This will include learning whether a variable follows a normal distribution and when you should use non-parametric statistical tests like the Wilcoxon rank-sum test and the Spearman correlation test.

Exercise 1: Assumptions and normal distributions Exercise 2: Exploring distributions with summary stats Exercise 3: Q-Q plot Exercise 4: Testing for normality Exercise 5: Shapiro-Wilk test Exercise 6: Choosing tests and normality Exercise 7: Non-parametric tests: Wilcoxon rank-sum test Exercise 8: Wilcoxon rank-sum test Exercise 9: Wilcoxon signed-rank test Exercise 10: Parametric vs non-parametric tests Exercise 11: More non-parametric tests: Spearman correlation Exercise 12: Spearman correlation Exercise 13: Choosing the correct correlation test Exercise 14: Summary