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Regression analysis can be more complex than what is covered in the "Dive deep to regression analysis" article. For example, sometimes the relationships are non-linear (e.g., square or reverse, etc.). Occasionally values of variables need to be converted in logarithms. When dealing with categorical independent variables (X), we will need to create dummy variables and examine effects of interactive variables. In addition, we will need to use logistic regression model when dealing with categorical dependent variable (Y).

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Other free tools that you can use to conduct surveys:

Survey research is a method of collecting data from a sample of individuals to gather information about their attitudes, beliefs, behaviors, and other characteristics. Surveys can be conducted in a variety of ways, including in person, by phone, by mail, or online. The information collected from surveys is used to make inferences about a larger population based on the responses from a sample of individuals.

Introduction: Multiple regression is a statistical technique used to model the relationship between two or more independent variables and a dependent variable. It is a type of regression analysis that allows us to study the effect of multiple variables on the response variable. The goal of multiple regression analysis is to find the best-fitting equation that predicts the response variable as a linear combination of the predictor variables.

Regression analysis is a statistical technique to examine if a dependent variable can be predicted with one or more independent variables. For example, statisticians often conduct regression analysis to see if college students’ GPA scores can be predicted with SAT scores and/or high school GPA scores.