A regression model relating the number of salespersons at a branch office to annual sales at the office ANOVA df SS MS F 1 Regression Residual 2298.80 Total 29 9127.40 t Stat P-value Coefficients 80 Standard Error Intercept 11.333 50 5.482 Salespersons Write the estimated linear regression equation. (Be sur to define the variables first. See the PPT)
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 regression model relating the number of salespersons at a branch office to annual sales at the office (in thousands of dollars) provided the following regression output.
look at the TABLE in the picture sent
A)Write the estimated linear regression equation. (Be sure to define the variables first).
B) Interpret the intercept coefficient.
C)Interpret the slope coefficient.
D)Test for a significant relationship. Use α = 0.05. use (p -value hypothesis approach)
E)Did the estimated linear regression equation provide a good fit?
F)Predict the annual sales in the Memphis branch. The branch employs 12 salespersons
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