A Simple Linear Regression (SLR) was performed where the monthly Revenue ("Rev", the y-variable) was regressed on the monthly Advertising Expenditures ("Expend", the x-variable). The Excel-generated Regression output is provided below: ANOVA df SS MS F Significance F Regression 1 492.528125 492.528125 10.65525634 0.046980871 Residual 3 138.671875 46.22395833 Total 4 631.2 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 23.1328125 5.324310936 4.344752359 0.022510469 6.188478833 40.07714617 Expend 3.1015625 0.950164031 3.264239014 0.046980871 0.077716489 6.125408511 a. From the Excel-generated Regression output above, give the value of b subscript o, the estimated y-intercept. Round off your answer to the fourth decimal place. b subscript 0 = Blank 1. Fill in the blank, read surrounding text. b. From the Excel-generated Regression output above, give the value of b subscript 1, the estimated slope. Round off your answer to the fourth decimal place. b subscript 1 =
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.
A Simple Linear Regression (SLR) was performed where the monthly Revenue ("Rev", the y-variable) was regressed on the monthly Advertising Expenditures ("Expend", the x-variable). The Excel-generated Regression output is provided below:
ANOVA
| df | SS | MS | F | Significance F | |
| Regression | 1 | 492.528125 | 492.528125 | 10.65525634 | 0.046980871 |
| Residual | 3 | 138.671875 | 46.22395833 | ||
| Total | 4 | 631.2 |
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 23.1328125 | 5.324310936 | 4.344752359 | 0.022510469 | 6.188478833 | 40.07714617 |
| Expend | 3.1015625 | 0.950164031 | 3.264239014 | 0.046980871 | 0.077716489 | 6.125408511 |
a. From the Excel-generated Regression output above, give the value of b subscript o, the estimated y-intercept. Round off your answer to the fourth decimal place. b subscript 0 = Blank 1. Fill in the blank, read surrounding text.
b. From the Excel-generated Regression output above, give the value of b subscript 1, the estimated slope. Round off your answer to the fourth decimal place. b subscript 1 =
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