a) Explain the requirements of the data for which multiple linear regression can be applied. (b) Take a use case from your work environment or elsewhere, identify one dependent variable and at least five independent variables (At least one of it needs to be a categorical variable) for which you would like to fit a multiple regression model (c) Explain some of the key interpretations you would be doing based on the output of the generated linear regression model.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
(a) Explain the requirements of the data for which multiple linear regression can be applied. (b) Take a use case from your work environment or elsewhere, identify one dependent variable and at least five independent variables (At least one of it needs to be a categorical variable) for which you would like to fit a multiple regression model (c) Explain some of the key interpretations you would be doing based on the output of the generated linear regression model.
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