Building Statistical Summaries with R
This course covers inferential statistics techniques. Learn advanced techniques to compare means across categories, predictive models for regression and classification, and A/B testing to perform randomized experiments on two versions of a variable.
What you'll learn
The tools of machine learning - algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data. Today, more than ever, it is important that you really know your data well.
In this course, Building Statistical Summaries with R, you will gain the ability to harness the full power of inferential statistics, which are truly richly supported in R.
First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next, you will discover how the classic t-test can be used in a variety of common scenarios around estimating means. You will also learn about related tests such as the Z-test, the Pearson’s Chi-squared test, Levene’s test and Welch’s t-test for dealing with populations that have unequal variances.
Finally, you will round out your knowledge by using ANOVA, a powerful statistical technique used to measure statistical properties across different categories of data. Along the way, you will explore several variants of ANOVA, including one-way, two-way, Kruskal-Wallis, and Welch’s ANOVA.
You will build predictive models using linear regression and classification and finally, you will understand A/B testing, and implement both the frequentist and the Bayesian approaches to implement this incredibly powerful technique.
When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from hypothesis testing, including t-tests, ANOVA and Bayesian A/B testing in order to measure the strength of statistical relationships within your data.
Table of contents
- Version Check 0m
- Prerequisites and Course Outline 2m
- The Role of Statistics in Understanding Data 3m
- Hypothesis Testing 8m
- P-values, Power, and Alpha of Statistical Tests 3m
- Introducing the T-test 3m
- The T-test for Different Use Cases 5m
- The Z-test 3m
- One-way ANOVA: Assumptions and Alternatives 6m
- Two-way ANOVA and Assumptions 2m
- Pearsons Chi2 Test 4m
- Demo: Preprocessing Data 5m
- Demo: One Sample T-test and Z-test 6m
- Demo: Two Sample T-test 7m
- Demo: Type I and Type II Errors 7m
- Demo: Performing Chi2 Analysis 6m
- Demo: Interpreting the Results of Chi2 Analysis 6m
- Demo: One-way ANOVA 7m
- Demo: Assumptions of One-way ANOVA and Alternatives 3m
- Demo: Two-way ANOVA 8m
- Continuous and Categorical Data 2m
- Linear Regression 5m
- Demo: Exploring Data for Regression Analysis 4m
- Demo: Performing Linear Regression and Interpretings Results 7m
- Logistic Regression 5m
- Odds Ratio and the Forest Plot 2m
- Demo: Performing Logistic Regression 5m
- Demo: Accuracy, Sensitivity, and Specificity of the Logistic Regression Model 8m
- Introducing A/B Testing 5m
- Distributions and Statistical Tests 8m
- Bayes' Theorem Intuition 4m
- Frequentist Approach vs. Bayesian Approach 4m
- The Conjugate Prior 5m
- Understanding the Bayesian A/B Test 5m
- Demo: Modelling Outcomes and Priors for the Bayes A/B Test 8m
- Demo: Performing and Interpreting the Bayesian A/B Test for Click-through Rates 4m
- Demo: Performing and Interpreting the Bayesian A/B Test for Page Interactions 3m
- Summary and Further Study 2m