When making predictions based on regression lines, which of the following is not listed as a consideration? A. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two variables. B. Use the regression line for predictions only if the data go far beyond the scope of the available sample data. C. If the regression equation does not appear to be useful for making predictions, the best predicted value of a variable is its point estimate. D. Use the regression equation for predictions only if the graph of the regression line on the scatterplot confirms that the regression line fits the points reasonably well.
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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