The Minitab output shown below was obtained by using paired data consisting of weights (in ib) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight dF 3500 to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? E Click the icon to view the Minitab display. O Minitab output The linear correlation coefficient is- (Round to three decimal places as needed.) The regression equation is Highway = 50.4-0.00529 Weight: Coef SE Coef T Predictor Constant 50.419 2.767 17.47 0.000 Weight 0.0007964 -7.53 0.000 -0.0052867 S=2.29464 R-Sq= 65.7% R-Sqladj) = 63.3 % Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 31.916 0.525 (30.898, 32.934) (27.324, 36.508) Values of Predictors for New Observations New Weight 3500 Obs Print Done Enter your answer in the answer box and then click Check Anwer
The Minitab output shown below was obtained by using paired data consisting of weights (in ib) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight dF 3500 to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? E Click the icon to view the Minitab display. O Minitab output The linear correlation coefficient is- (Round to three decimal places as needed.) The regression equation is Highway = 50.4-0.00529 Weight: Coef SE Coef T Predictor Constant 50.419 2.767 17.47 0.000 Weight 0.0007964 -7.53 0.000 -0.0052867 S=2.29464 R-Sq= 65.7% R-Sqladj) = 63.3 % Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 31.916 0.525 (30.898, 32.934) (27.324, 36.508) Values of Predictors for New Observations New Weight 3500 Obs Print Done Enter your answer in the answer box and then click Check Anwer
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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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.
Question
Transcribed Image Text:The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 3500 lb
to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given
that there are 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts?
E Click the icon to view the Minitab display.
The linear correlation coefficient is
(Round to three decimal places as needed.)
Minitab output
The regression equation is
Highway = 50.4 -0.00529 Weight
Predictor
Constant
Weight
Coef
SE Coef
50.419
2.767
17.47
0.000
-0.0052867
0.0007964
-7.53
0.000
S=2.29464
R-Sq = 65.7%
R-Sq(adj) = 63.3 %
Predicted Values for New Observations
New
Obs
Fit
SE Fit
95% CI
95% PI
1
31.916
0.525
(30.898, 32.934)
(27.324, 36.508)
Values of Predictors for New Observations
New
Obs
Weight
1
3500
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