An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below. SUMMARY OUTPUT Regression Statistics Multiple R 0.991 R Square 0.982 Adjusted R Square 0.976 Standard Error 0.299 Observations 10 ANOVA df SS MS F Signif F Regression 2 33.4163 16.7082 186.325 0.001 Residual 7 0.6277 0.0897 Total 9 34.0440 Coeff StdError t Stat P-value Intcept – 0.0861 0.5674 – 0.152 0.8837 GDP 0.7654 0.0574 13.340 0.0001 Price – 0.0006 0.0028 – 0.219 0.8330 Referring to Table 14-3, the p-value for the regression model as a whole is
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.
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below.
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.991
R Square 0.982
Adjusted R Square 0.976
Standard Error 0.299
Observations 10
ANOVA
df SS MS F Signif F
Regression 2 33.4163 16.7082 186.325 0.001
Residual 7 0.6277 0.0897
Total 9 34.0440
Coeff StdError t Stat P-value
Intcept – 0.0861 0.5674 – 0.152 0.8837
GDP 0.7654 0.0574 13.340 0.0001
Price – 0.0006 0.0028 – 0.219 0.8330
Referring to Table 14-3, the p-value for the regression model as a whole is
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