#### In this step-**by**-step guide, we will walk you through linear regression in **R** using two sample datasets. The first dataset contains observations about income (in a range of $15k to $75k) and happiness (rated on a scale of 1 to 10) in an imaginary sample of 500 people. The income values are divided by 10,000 to make the income data match the scale. Multivariate ANOVA (MANOVA) -- Notes and **R** Code. This post covers my notes of multivariate ANOVA (MANOVA) methods using **R** from the book "Discovering Statistics using **R** (2012)" by Andy Field. Most code and text are directly copied from the book. All the credit goes to him. Note that we will refer to two types of categorical **variables**: **Group** **Variables** and Break **Variables**. The values of a **Group** **Variable** are used to define the rows, sub rows, and columns of the **summary** table. Up to two **Group** **Variables** may be used per table. **Group** **Variables** are not required. Break **Variables** are used to split a database into subgroups.

**multiple**t-tests on different

**variables**between the same two

**groups**;

**by**Kazuki Yoshida; Last updated almost 10 years ago Hide Comments (-) Share Hide Toolbars. We can also choose to calculate just one quantile

**by group**. For example, here’s how to calculate the 90th percentile of the number of wins for each team: For example, here’s how to calculate the 90th percentile of the number of wins for each team:. There are only 2 categorical

**variables**in our dataset, so let’s use the tabacco dataset which has 4 categorical

**variables**(i.e., gender, age

**group**, smoker, diseased). For this example, we would like to create a contingency table of the

**variables**smoker and diseased , and this for each gender :.