MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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Answer the following questions.

 

Question-1: The objective of a study is to produce a multiple regression model to predict sales of cotton
fabric. The explanatory variables are
X,
Whole sale price index
Quantity of Imported Fabric
х,
Quantity of Exported Fabric
Time
Part of a computer output from the estimated regression based on 28 observations is shown below:
Predictor
Constant
Coeff StdDev
8876
2295
X,
-24
25
-6
2.5
X,
0.5
0.2
X,
63
70
Analysis of Variance
Source
Regression
Error
Total
21080
1426
22506
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Transcribed Image Text:Question-1: The objective of a study is to produce a multiple regression model to predict sales of cotton fabric. The explanatory variables are X, Whole sale price index Quantity of Imported Fabric х, Quantity of Exported Fabric Time Part of a computer output from the estimated regression based on 28 observations is shown below: Predictor Constant Coeff StdDev 8876 2295 X, -24 25 -6 2.5 X, 0.5 0.2 X, 63 70 Analysis of Variance Source Regression Error Total 21080 1426 22506
a) Write down the estimated regression equation and interpret the estimated regression coefficients for
X, and X,.
b) Test if the overall regression model is significant or not using a 0.05 level of significance.
c) Test the significance of each regression coefficient. Use significance level 0.05.
d) Suppose we drop the variable X, from the model. The reduced regression model has X,, X, and X,
as explanatory variables. The reduced model has R² =0.9. Test if the reduced regression model is
significant or not at significance level 0.05.
e) Suppose you believe that the quantity of cotton fabric sold may also be impacted by how cold the
weather is. Furthermore, suppose you have classified the weather in three categories, cold, normal, warm.
Design a regression model that allows for the new variables and show how you test your hypothesis.
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Transcribed Image Text:a) Write down the estimated regression equation and interpret the estimated regression coefficients for X, and X,. b) Test if the overall regression model is significant or not using a 0.05 level of significance. c) Test the significance of each regression coefficient. Use significance level 0.05. d) Suppose we drop the variable X, from the model. The reduced regression model has X,, X, and X, as explanatory variables. The reduced model has R² =0.9. Test if the reduced regression model is significant or not at significance level 0.05. e) Suppose you believe that the quantity of cotton fabric sold may also be impacted by how cold the weather is. Furthermore, suppose you have classified the weather in three categories, cold, normal, warm. Design a regression model that allows for the new variables and show how you test your hypothesis.
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