SUMMARY OUTPUT X Line Fit Plot Regression Statistics Multiple R 0.92966968 10 R Square 0.864285714 Adjusted R Square 0.819047619 > 5 Observation Standard Error 1.345185418 •Y 1 1 17 Observations 5 Predicted Y 2 3 13 10 15 20 5 8 ANOVA 4 7 10 df MS F Significance F 5 2 Regression 1 34.57142857 34.57142857 19.10526316 0.022151968 (Ctrl) - Residual Total 3 5.428571429 1.80952381 4 40 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 10.23809524 1.340908188 7.635194809 0.004664669 5.970726928 14.50546355 5.970726928 14.50546355 -0.523809524 0.119838642 -4.370956778 0.022151968 -0.905189567 -0.142429481 -0.905189567 -0.142429481 RESIDUAL OUTPUT Observation Predicted Y Residuals 1 1.333333333 -0.333333333 2 3.428571429 -0.428571429 3 6.047619048 -1.047619048 5 2 5 9.19047619 -0.19047619
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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