Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 67.6 4.02 16.8 3.515E-16 59.4 75.9 Payroll 0.249 0.0673 0.0009 0.387 Based on the regression output, if you wanted to test whether the number of wins is related the team payroll, using the 5% level of significance, what would you conclude? A, B or C A. the two variables are statistically significantly related at that level of significance B. the two variables are NOT statistically significantly related at that level of significance C. there is not enough information to determine whether the two variables are statistically significantly related at that level of significance
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.
QUESTION 20
- Below is some of the regression output from a simple regression of the number of wins for a major league baseball team and the size amount of money the team is paying its players (expressed in millions of $'s)
|
SUMMARY OUTPUT |
Wins v. Team Payroll |
|
|
|
|
|
|
|
|
|
|
|
|
|
Regression Statistics |
|
|
|
|
|
|
|
Multiple R |
|
|
|
|
|
|
|
R Square |
|
|
|
|
|
|
|
Adjusted R Square |
|
|
|
|
|
|
|
Standard Error |
|
|
|
|
|
|
|
Observations |
30 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ANOVA |
|
|
|
|
|
|
|
|
df |
SS |
MS |
F |
|
|
|
Regression |
|
1032.6 |
1032.6 |
13.7 |
|
|
|
Residual |
|
2109.7 |
75.3 |
|
|
|
|
Total |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
|
Intercept |
67.6 |
4.02 |
16.8 |
3.515E-16 |
59.4 |
75.9 |
|
Payroll |
0.249 |
0.0673 |
|
0.0009 |
|
0.387 |
Based on the regression output, if you wanted to test whether the number of wins is related the team payroll, using the 5% level of significance, what would you conclude? A, B or C
|
A. the two variables are statistically significantly related at that level of significance |
||
|
B. the two variables are NOT statistically significantly related at that level of significance |
||
|
C. there is not enough information to determine whether the two variables are statistically significantly related at that level of significance |
Step by step
Solved in 2 steps
