A regression model relating x, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following regression output. Where ntotal = 40. ANOVA df MS F Significance F Regression 6,759.5 Residual Total 9,031.4 Coefficients Standard Error t Stat P-value Intercept 75.0 10.408 Number of 40.0 5.323 Salespersons a. Write the estimated regression equation (to whole number). + b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) F-value p-value is Select your answer - we Select your answer - v Ho c. Compute thet statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) t Stat p-value is Select your answer - , we Select your answer - v Ho: B,
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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