Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.945724046 0.894393971 0.878563067 0.389665133 24 ANOVA of SS MS Significance F 56.46104232 Regression Residual 25.7044318 3.035064031 8.568143934 6.06992E-10 20 0. 151753202 Total 23 28.73949583 Upper 95% -3.42749564 -1.244934517 0.40246809 Coefficierts Standard Eror 0.623164211 Lower 95% t Stat 4.465633708 P-value -2.336215079 Intercept Economic Growth 0.000237013 0.27605404 0.060602236 4.565179101 0.000192394 0.149639991 Population Growth Meat Consumption 1.508025784 0.00860542 0.351713039 0.003093965 4.287659594 0.000359069 0.774365242 2.241686327 2.781356534 0.011522014 0.002151522 0.015059318
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.
Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation.
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