The regression analysis below models the relationshipbetween the fuel efficiency (in miles per gallon) andhorsepower (in 100s) for a random sample of 15 cars.Dependent variable is: MPGR squared = 82.6% R squared (adjusted) = 81.3%s = 2.435 with 15 - 2 = 13 degrees of freedomVariable Coefficient s.e. of Coeff t-ratio probConstant 43.4518 2.057 21.1 ... 0.0001HP100 -7.0166 0.89 -7.86 ... 0.0001The highlighted value of 0.89 estimates the variability inA) horsepower for this sample of cars.B) fuel economy for this sample of cars.C) slopes among this sample of cars.D) slopes among all samples from this population of cars.E) errors for predictions made by this model.
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.
The
between the fuel efficiency (in miles per gallon) and
horsepower (in 100s) for a random sample of 15 cars.
Dependent variable is: MPG
R squared = 82.6% R squared (adjusted) = 81.3%
s = 2.435 with 15 - 2 = 13 degrees of freedom
Variable Coefficient s.e. of Coeff t-ratio prob
Constant 43.4518 2.057 21.1 ... 0.0001
HP100 -7.0166 0.89 -7.86 ... 0.0001The highlighted value of 0.89 estimates the variability in
A) horsepower for this sample of cars.
B) fuel economy for this sample of cars.
C) slopes among this sample of cars.
D) slopes among all samples from this population of cars.
E) errors for predictions made by this model.
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