An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. Weight (pounds), x Miles per Gallon, y 3710 18 3888 17 2701 25 3601 18 3332 22 3015 23 3675 17 2656 25 3513 20 3816 18 3402 17
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.
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year
Weight (pounds), x Miles per Gallon, y
3710 18
3888 17
2701 25
3601 18
3332 22
3015 23
3675 17
2656 25
3513 20
3816 18
3402 17
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