Predictor (x) Variables P-Value R Adjusted R 0.000 0.943 Regression Equation CITY = 6.86 – 0.00128 WT 0.933 WT/DISP/HWY 0.257 DISP + 0.652 HWY CITY = 38.0 – 0.00395 WT – 1.29 DISP CITY = 6.69 – 0.00159 WT + 0.670 HWY CITY - 1.87 - 0.625 DISP + 0.706 HWY CITY = 41.8 - 0.00607 WT CITY = 29.0 - 2.98 DISP = -3.15 + 0.819 HWY 0.000 0.748 0.000 0.942 0.000 0.935 0.000 0.712 0.720 WT/DISP 0.935 WT/HWY 0.928 DISP/HWY 0.697 WT DISP 0.641 0.000 0.659 0.924 0.000 0.920 HWY CITY
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.
City Fuel Consumption: Finding the Best Multiple Regression Equation. In Exercises 9–12, refer to the accompanying table, which was obtained using the data from 21 cars listed in Data Set 20 “Car Measurements” in Appendix B. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi /gal).
If exactly two predictor (x) variables are to be used to predict the city fuel consumption, which two variables should be chosen? Why?
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