Answer true or false to each of the following statements and explain your answers. a. In a regression equation involving a quantitative predictor variable x1 and a single indicator variable x2, the t-test of the utility of the interaction term is a test that the regression lines are parallel for the two values of the qualitative variable. b. If the regression lines relating the response variable y to a quantitative predictor variable x1 have the same y-intercept for each category of a qualitative predictor variable, then there is no interaction between the two predictor variables.
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. In a regression equation involving a quantitative predictor variable x1 and a single indicator variable x2, the t-test of the utility of the interaction term is a test that the regression lines are parallel for the two values of the qualitative variable.
b. If the regression lines relating the response variable y to a quantitative predictor variable x1 have the same y-intercept for each category of a qualitative predictor variable, then there is no interaction between the two predictor variables.
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