When we estimate a linear multiple regression model (including a
linear simple regression model), it appears that the calculation of
the coefficient of determination, R2, for this model can be
accomplished by using the squared sample correlation coefficient
between the original values and the predicted values of the
dependent variable of this model.
Is this statement true? If yes, why? If not, why not? Please use
either matrix algebra or algebra to support your reasoning.
Define and discuss the difference between linear regression and
multiple regression. Are there any assumptions which must be met
before using multiple regression?
What is the difference between simple linear regression and
multiple linear regression?
What is the difference between multiple linear regression and
logistic regression?
Why should you use adjusted R-squared to choose between models
instead of R- squared?
Use SPSS to:
Height (Xi)
Diameter (Yi)
70
8.3
72
10.5
75
11.0
76
11.4
85
12.9
78
14.0
77
16.3
80
18.0
Create a scatterplot of the data above. Without conducting a
statistical test, does it look like there is a linear...
Regression
Make a distinction between simple linear and multiple linear
regression. Can you think of examples in your business world where
these techniques are or should be applied? Share the details, where
possible.
Estimate a simple linear regression model and present the
estimated linear equation. Display the regression summary table and
interpret the intercept and slope coefficient estimates of the
linear model.
Estimate
a simple linear regression model and present the estimated linear
equation. Display the regression summary table and interpret the
intercept and slope coefficient estimates of the linear model.
Estimate a simple linear regression model and present the
estimated linear equation. Display the regression summary table and
interpret the intercept and slope coefficient estimates of the
linear model.
The following is the estimation results for a multiple linear
regression model:
SUMMARY OUTPUT
Regression Statistics
R-Square
0.558
Regression Standard Error
(S)
863.100
Observations
35
Coeff
StdError
t-Stat
Intercept
1283.000
352.000
3.65
X1
25.228
8.631
X2
0.861
0.372
Questions:
Interpret each coefficient.