A random sample of records of electricity usage 2 = 0.71. Assume that a linear model is appropriate. Interpret r. homes gives the amount of electricity used and size (in square feet) of 135 homes. A simple linear regression to predict the amount of electricity used (in kilowatt-hours) based on size has an O A. The prediction error using the regression line to predict electricity use is 71% smaller than the prediction error using y to predict it. O B. The prediction error for predicting electricity use is about the same when using the regression line and y O C. The prediction error using the regression line to predict electricity use is 29% larger than the prediction error using y to predict it. O D. The prediction error using the regression line to predict electricity use is 71% larger than the prediction error using y to predict it. O E. The prediction error using the regression line to predict electricity use is 29% smaller than the prediction error using y to predict it.
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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