R3.4 Stats teachers' cars A random sample of AP Statistics teachers was asked to report the age (in years) and mile- age of their primary vehicles. A scatterplot of the data, a least-squares regression printout, and a residual plot are provided below. Rsquared - 82.0% Rsquared (adjusted) -81.1% -19280 with 21 -2-19 degrees of freedom Coefficient 7288.54 116306 se of Coeff 6591 1249 Variable ratio prob Constant LII 0.2826 Car age 931 0.0001 2000 25 se 75 e. 25 se 75 e Crge
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.
![(d) What's the correlation between car age and mil
age? Interpret this value in context.
(e)
How well does the regression line fit the data? Jus-
tify your answer using the residual plot and s.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fe90631a8-7e00-4a03-8189-cfd36c7841dd%2Fc9db9f38-ec41-4cb5-990b-1cdf0f92c99c%2Fo0xoxb8_processed.png&w=3840&q=75)
![R3.4 Stats teachers' cars A random sample of AP Statistics
teachers was asked to report the age (in years) and mile-
age of their primary vehicles. A scatterplot of the data, a
least-squares regression printout, and a residual plot are
provided below.
R squared - 82.0% R squared (adjusted) -81.1%
-19280 with 21 -2-19 degrees of freedom
Variable
Constant
Carage
Coefficient
7288.54
11630.6
se. of Coeff bratio prob
LII
931
0.2826
6591
1249
0.0001
200
25 se 75 e.0
25 se 75 se.e
Crge](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fe90631a8-7e00-4a03-8189-cfd36c7841dd%2Fc9db9f38-ec41-4cb5-990b-1cdf0f92c99c%2F05gew4k_processed.png&w=3840&q=75)
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