An engineer wants to determine how the weight of a car, x, affects gas mileage, y. The following data represent the weights of various cars and their miles per gallon. Car A B C D E Weight (pounds), x 2725 2875 3270 3715 4135 Miles per Gallon, y 27.4 23.3 22.9 20.5 18.7 (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. Write the equation for the least-squares regression line. y=__ x+__ (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.)
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.
|
Car
|
A
|
B
|
C
|
D
|
E
|
|
|---|---|---|---|---|---|---|
|
Weight (pounds), x
|
2725
|
2875
|
3270
|
3715
|
4135
|
|
|
Miles per Gallon, y
|
27.4
|
23.3
|
22.9
|
20.5
|
18.7
|
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