An organization develops a linear regression model to forecast sales. Below are the results of the regression analysis using 12 periods in the model. The regression equation is Y = .332167832(X) + 12.42424242 Is the model significant? What data demonstrates the significance of the model? What does the R Square signify? What is the forecast for period 13?
An organization develops a linear regression model to forecast sales. Below are the results of the regression analysis using 12 periods in the model. The regression equation is Y = .332167832(X) + 12.42424242 Is the model significant? What data demonstrates the significance of the model? What does the R Square signify? What is the forecast for period 13?
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section: Chapter Questions
Problem 6SGR
Related questions
Question
An organization develops a linear regression model to forecast sales. Below are the results of the
The regression equation is Y = .332167832(X) + 12.42424242
- Is the model significant? What data demonstrates the significance of the model?
- What does the R Square signify?
- What is the forecast for period 13?
![SUMMARY
OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R
Square
0.868519059
0.754325356
0.729757892
Standard
Error
0.71684689
Observations
12
ANOVA
Significance
df
MS
F
F
Regression
1 15.77797 15.77797203 30.70424132 0.000247139
Residual
10 5.138695 0.513869464
Total
11 20.91667
Standard
Error
Coefficients
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0% Upper 95.0%
Intercept
12.42424242 0.441189 28.16083155
7.408E-11
11.4412126 13.40727225
11.4412126 13.40727225
X Variable 1
0.332167832 0.059946 5.541140796 0.000247139 0.198600381 0.465735283 0.198600381 0.465735283](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F565a67ae-3bd6-4b85-8726-e4895610f83a%2F7400bdd2-c8f8-4a8a-958f-2f734abab22d%2Fy9ps3s_processed.png&w=3840&q=75)
Transcribed Image Text:SUMMARY
OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R
Square
0.868519059
0.754325356
0.729757892
Standard
Error
0.71684689
Observations
12
ANOVA
Significance
df
MS
F
F
Regression
1 15.77797 15.77797203 30.70424132 0.000247139
Residual
10 5.138695 0.513869464
Total
11 20.91667
Standard
Error
Coefficients
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0% Upper 95.0%
Intercept
12.42424242 0.441189 28.16083155
7.408E-11
11.4412126 13.40727225
11.4412126 13.40727225
X Variable 1
0.332167832 0.059946 5.541140796 0.000247139 0.198600381 0.465735283 0.198600381 0.465735283
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