The relationship between two variables is often not linear, but almost any function that can be written in closed form can be incorporated in a nonlinear regression model. Learn here how MaxStat can help to model data in nonlinear function.
Part 15 (Regression): Logistic Regression
Logistic regression is used to model relationships between dichotomous categorical outcomes (e.g., dead vs. alive, present vs. absent or yes vs. no). Read more in our latest lesson.
Part 14 (Regression): Multiple linear regression
A multiple linear regression analysis is carried out to predict the values of a dependent variable, Y, given a set of multiple explanatory variables x. Click here to go the lesson.
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 11 (Hypothesis testing): Two-way ANOVA
Let us continue in our lessons with ANOVA, this time with two-way ANOVA. It examines the relationship between a quantitative dependent variable and two qualitative independent variables.
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.
