A statistics student is studying if there is a relationship between the price of a used car and the number of Using the computer output, what is the correlation? miles it had been driven. She collects data for 20 cars О -0.82 of the same model with different mileage and determines each car's price using a used car website. The analysis is given in the computer output. O -0.68 O 0.68 O 0.82 Predictor Coef SE Coef t-ratio Constant 24157.2 2164.1 2.965 0.046 Mileage -0.181 0.024 5.377 0.000 S = 3860.7 R-Sq = 68.0% R-Sq (Adj) 67.5% !3!
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.
![A statistics student is studying if there is a relationship
between the price of a used car and the number of
miles it had been driven. She collects data for 20 cars
of the same model with different mileage and
determines each car's price using a used car website.
The analysis is given in the computer output.
Using the computer output, what is the correlation?
O-0.82
O -0.68
O 0.68
O 0.82
Predictor
SE Coef t-ratio
Constant
24157.2
2164.1
2.965 0.046
Mileage
-0.181
0.024
5.377 0.000
S = 3860.7 R-Sq = 68.0% R-Sq (Adj) = 67.5%
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