Answer true or false to each of the following statements and explain your answers. a. The number of indicator variables required to represent the possible values of a qualitative predictor variable is one more than the number of possible values. b. If we take the regression equation relating the response variable y to a quantitative predictor variable x1 and indicator variables x2 and x3 to be y = β0 + β1x1 + β2x2 + β3x3, then we are assuming there is no interaction between x1 and the qualitative variable represented by x2 and x3. c. A cross-product term in a regression equation is often referred to as an interaction term.
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.
Answer true or false to each of the following statements and explain your answers.
a. The number of indicator variables required to represent the possible values of a qualitative predictor variable is one more than the number of possible values.
b. If we take the regression equation relating the response variable y to a quantitative predictor variable x1 and indicator variables x2 and x3 to be y = β0 + β1x1 + β2x2 + β3x3, then we are assuming there is no interaction between x1 and the qualitative variable represented by x2 and x3.
c. A cross-product term in a regression equation is often referred to as an interaction term.
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