Part 13 (Regression): Simple linear regression

We often want to predict, or explain, one variable in terms of others. For example, how does the risk of heart disease vary with blood pressure? Or how does physical exercise decrease the level of cholesterol? Regression modeling can help with this kind of problem. In the simplest case, we determine if a linear relationship exist between a independent variable x and a dependent variable y. Learn more in Part 13 of your course.

Part 12 (Hypothesis testing): Contingency tables and Chi2 test

Chi-squared test is a statistical test applied to categorical data to evaluate how likely it is that any observed difference between the categories are significant. Contingency table shows how many subjects fall into each category. Learn in the new lesson how to construct a contingency table and perform a chi2 tests on the data.

Part 10 (Hypothesis testing): One-way ANOVA

One-way ANalysis Of VAriance (ANOVA) is used to compare several means. This method is often used in scientific or medical experiments when more than two treatments, processes, materials or products are being compared. So in ANOVA we are testing the null hypothesis, “Do all our data groups come from populations with the same mean?”. Read the new lesson.

Part 7 (Basic Statistics): Normality

In statistics, we use normality tests to determine whether a data set follows a normal distribution or not, or to compute how likely an underlying random variable is to be normally distributed. We have already learned that with the choice between parametric and non-parametric tests it is very important to know whether our data follows a normal distribution or not. In this lesson you learn which statistical tests you can apply to check normality of your data.