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)Test for a significant relationship. Use α = 0.05. use (p -value hypothesis approach) B)Did the estimated linear regression equation provide a good fit? C)Predict the annual sales in the Memphis branch. The branch employs 12 salespersons
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)Test for a significant relationship. Use α = 0.05. use (p -value hypothesis approach)
B)Did the estimated linear regression equation provide a good fit?
C)Predict the annual sales in the Memphis branch. The branch employs 12 salespersons
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QUESTION I
A regression model relating the number of salespersons at a branch office to annual sales at the office (in thou
ANOVA
df
SS
MS
F
Regression
1
Residual
2298.80
Total
29
9127.40
Coefficients
Standard Error
t Stat
P-value
Intercept
80
11.333
Salespersons
50
5.482
Write the estimated linear regression equation. (Be surto define the variables first. See the PPT)
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