The linear correlation coefficient is __. (Round to three decimal places as needed.) Is there sufficient evidence to support a claim of linearcorrelation? -no -yes
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 Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 32 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 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
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The regression equation is
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Highway=50.5−0.00588
Weight |
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Predictor
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Coef
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SE Coef
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T
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P
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Constant
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50.474
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2.832
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17.67
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0.000
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Weight
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−0.0058772
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0.0007753
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−7.85
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0.000
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S=2.23963
R−Sq=65.9%
R−Sq(adj)=63.5%
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Predicted Values for New Observations
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New
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Obs
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Fit
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SE Fit
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95% CI
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95% PI
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1
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24.027
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0.523
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(23.009, 25.045)
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(19.430, 28.624)
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Values of Predictors for New Observations
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New
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Obs
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Weight
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1
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4500
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The linear correlation coefficient is __.
(Round to three decimal places as needed.)
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