Which of the following statements is true with respect to a simple linear regression model? The percent of variation in the dependent variable that is explained by the regression model is equal to the square of the correlation coefficient between the x and y variables If the correlation coefficient between the x and y variables is negative, the sign on the regression slope coefficient will also be negative If the correlation between the dependent and the independent variable is determined to be significant, the regression model for y given x will also be significant All of the above are true
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.
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