Building Statistical Summaries with R

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 d