Below is some of the regression output from a regression of the gas mileage (expressed in miles per gallon) (MPG) an automobile gets versus the size of the car's engine (expressed in cubic liters) and the weight of the car (expressed in tons) Regression Statistics Multiple R 0.982 R Square Adjusted R Square Standard Error Observations 10 ANOVA df MS F Significance F Regression 574.7 287.4 96.3 8.08847E-06 Residual 3.0 Total 9 595.6 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 53.3 2.48 21.86 0.000 48.44 60.19 Engine Size -4.8 1.77 0.054 0.09 Weight -18 3.36 0.003 -24.41 Based on the regression output, if we wanted to test the following hypothesis: H0: coefficient on Weight is GREATER THAN -13.7 H 1: coefficient on Weight is LESS THAN -13.7 What is the critical value for this hypothesis test if we use a 5% level of significance? (please express your answer using 2 decimal places)
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.
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