Regression Statistics Multiple R 0.852623 R Square 0.726966 Adjusted R Square 0.696628 Standard Error 6.154903 Observations 11 ANOVA df SS MS F Significance F Regression 1 907.7818 907.7818 23.96288396 0.000853291 Residual 9 340.9455 37.88283 Total 10 1248.727 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 95.56364 4.507657 21.20029 5.42706E-09 85.36660782 105.7607 85.36661 105.7607 X Variable 1 -2.87273 0.586847 -4.89519 0.000853291 -4.200267314 -1.54519 -4.20027 -1.54519 From the excel's output we can conclude that the regression equation 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.
| Regression Statistics | ||||||||
| Multiple R | 0.852623 | |||||||
| R Square | 0.726966 | |||||||
| Adjusted R Square | 0.696628 | |||||||
| Standard Error | 6.154903 | |||||||
| Observations | 11 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 1 | 907.7818 | 907.7818 | 23.96288396 | 0.000853291 | |||
| Residual | 9 | 340.9455 | 37.88283 | |||||
| Total | 10 | 1248.727 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 95.56364 | 4.507657 | 21.20029 | 5.42706E-09 | 85.36660782 | 105.7607 | 85.36661 | 105.7607 |
| X Variable 1 | -2.87273 | 0.586847 | -4.89519 | 0.000853291 | -4.200267314 | -1.54519 | -4.20027 | -1.54519 |
From the excel's output we can conclude that the regression equation is:
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 1 images
