Model A: Y= β_0+β_1 X+ε Use the Excel Output from Model A to answer the following questions. Is X a significant predictor of Y? Interpret the coefficient of X. sFind the Total Sum of Square. Find Mean Squared Regression (MSR) and Mean Squared Error (MSE) What is the F-statistic? Is the F statistic significant? (assume critical value is 3.5) Calculate Y hat. Generate a new column of predictions based on the output from Model A (FYI: You are using your model to forecast outcomes for a set of points that were not used to build your model). Find the R square. Is this a good model? Explain why or why not.
Model A: Y= β_0+β_1 X+ε Use the Excel Output from Model A to answer the following questions. Is X a significant predictor of Y? Interpret the coefficient of X. sFind the Total Sum of Square. Find Mean Squared Regression (MSR) and Mean Squared Error (MSE) What is the F-statistic? Is the F statistic significant? (assume critical value is 3.5) Calculate Y hat. Generate a new column of predictions based on the output from Model A (FYI: You are using your model to forecast outcomes for a set of points that were not used to build your model). Find the R square. Is this a good model? Explain why or why not.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Model A: Y= β_0+β_1 X+ε
Use the Excel Output from Model A to answer the following questions.
Is X a significant predictor of Y? Interpret the coefficient of X.
sFind the Total Sum of Square. Find Mean Squared Regression (MSR) and Mean Squared Error (MSE)
What is the F-statistic? Is the F statistic significant? (assume critical value is 3.5)
Calculate Y hat. Generate a new column of predictions based on the output from Model A (FYI: You are using your model to forecast outcomes for a set of points that were not used to build your model).
Find the R square. Is this a good model? Explain why or why not.
Transcribed Image Text:Part I: Regression Basics
SUMMARY OUTPUT A
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Obs.
1
2
Regression Statistics
Intercept
X
3
4
X5560 00
8
0.97688814
df
0.939080583
3.277208147
5
1
3
4
Data
Y
23
16
25
28
SS
672.9797
32.22028
Y hat
Standard
Error
Coefficients
t Stat P-value
2.531468531 3.604616 0.702285 0.533087
3.43006993 0.433317 7.915839 0.004203
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