Dependent Variable: GFCF Method: Least Squares Date: 02/10/14 Time: 00:53 Sample: 1981 2012 Included observations: 32 Variable Coefficient Std. Error t-Statistic Prob. C 53826.76 75957.68 0.708641 0.4842 DOMDEBT 0.991086 0.046656 21.24254 0.0000 EXTDEBT 0.018121 0.040533 0.447074 0.6581 R-squared 0.939810 Mean dependent var 786278.2 S.D. dependent var Adjusted R-squared S.E. of regression Sum squared resid 0.935659 1210643. 307087.1 Akaike info criterion 28.19671 2.73E+12 Schwarz criterion 28.33412 Log likelihood Durbin-Watson stat -448.1474 F-statistic 226.4023 0.862668 Prob(F-statistic) 0.000000 Based on the regression output above, ß2 =?
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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