SUMMARY OUTPUT Regression Statistics Multiple R 0.999301299 R Square 0.998603086 Adjusted R 0.998253858 Standard E 208714.8321 Observatic ANOVA gnificance F 1 1.25E+14 1.25E+14 2859.455 7.32E-07 df SS MS Regressior Residual 4 1.74E+11 4.36E+10 Total 5 1.25E+14 Coefficients andard Erre t Stat P-value Lower 95% Upper 95% ower 95. Intercept -126083.5273 120268.8 -1.04835 0.353642 -460003 207836.1 -4600 X Variable 0.983702947 0.018396 53.47388 7.32E-07 0.932628 1.034778 0.9326:
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.
What is the regression equation for the data (see summary output image)?
Step by step
Solved in 2 steps with 2 images
