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2. Suppose you want to estimate the following model: Reorders¡ = B + R · Productsi + ÎR · Daysi where Reorders; is the number of reorders. Products; is the number of ordered products. Days; is the number of days since the last order. Using Excel, you have the following output: SUMMARY OUTPUT Regression Statistics Multiple R 0.8373 R Square 0.7010 Adjusted R Square 0.6998 Standard Error 3.1710 Observations 500 ANOVA df S MS F Significance F Regression 2 11717.33 5858.67 582.66 0.00 Residual 497 4997.38 10.06 Total 499 16714.71 一 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.9778 0.3416 2.8623 0.0044 0.3066 1.6489 products days_since_prior_orde 0.6333 0.0188 33.6482 0.0000 0.5963 0.6703 -0.0653 0.0133 -4.9207 0.0000 -0.0914 -0.0392 la) Ronort rearession results what are RR What are Standard Frrors of 8î 2R? What are

2. Suppose you want to estimate the following model: Reorders¡ = B + R · Productsi + ÎR · Daysi where Reorders; is the number of reorders. Products; is the number of ordered products. Days; is the number of days since the last order. Using Excel, you have the following output: SUMMARY OUTPUT Regression Statistics Multiple R 0.8373 R Square 0.7010 Adjusted R Square 0.6998 Standard Error 3.1710 Observations 500 ANOVA df S MS F Significance F Regression 2 11717.33 5858.67 582.66 0.00 Residual 497 4997.38 10.06 Total 499 16714.71 一 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.9778 0.3416 2.8623 0.0044 0.3066 1.6489 products days_since_prior_orde 0.6333 0.0188 33.6482 0.0000 0.5963 0.6703 -0.0653 0.0133 -4.9207 0.0000 -0.0914 -0.0392 la) Ronort rearession results what are RR What are Standard Frrors of 8î 2R? What are

MATLAB: An Introduction with Applications
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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I have already figured out part a) just havent figured out the rest of the parts B-E

(b) What is the null hypothesis of the t-test for the coefficient, B, and what does the t-test result suggest? (c) According to your model, what is the marginal effect of days since the last order on the number of reordered items? [Hint: your answer should include a description something like: if the number of days since the last order increases by then the number of reordered items is predicted to change by ....] ... (d) Please explain the goodness-of-fit of your model in term of the adjusted R-squared value. [Hint: your answer should include a description something like: . the percentage of the sample variation in ... ... that is explained by ... (e) Test the hypothesis, Ho: B1= 0.5; H1: B1 # 0.5. What is the t-stat? Assume a = 0.05. Whatis the critical t-value? Do we reject Ho?
Transcribed Image Text:(b) What is the null hypothesis of the t-test for the coefficient, B, and what does the t-test result suggest? (c) According to your model, what is the marginal effect of days since the last order on the number of reordered items? [Hint: your answer should include a description something like: if the number of days since the last order increases by then the number of reordered items is predicted to change by ....] ... (d) Please explain the goodness-of-fit of your model in term of the adjusted R-squared value. [Hint: your answer should include a description something like: . the percentage of the sample variation in ... ... that is explained by ... (e) Test the hypothesis, Ho: B1= 0.5; H1: B1 # 0.5. What is the t-stat? Assume a = 0.05. Whatis the critical t-value? Do we reject Ho?
2. Suppose you want to estimate the following model: Reorders; = ß + R · Productsi + B · Daysi where Reorders; is the number of reorders. Products; is the number of ordered products. Days; is the number of days since the last order. Using Excel, you have the following output: SUMMARY OUTPUT Regression Statistics Multiple R 0.8373 R Square 0.7010 Adjusted R Square Standard Error 0.6998 3.1710 Observations 500 ANOVA df MS Significance F Regression 2 11717.33 5858.67 582.66 0.00 Residual 497 4997.38 10.06 Total 499 16714.71 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.9778 0.3416 2.8623 0.0044 0.3066 1.6489 products 0.6333 0.0188 33.6482 0.0000 0.5963 0.6703 days_since_prior_orde -0.0653 0.0133 -4.9207 0.0000 -0.0914 -0.0392 (a) Report regression results, what are B? B? B? What are Standard Errors of B? R? B? What are their 95% Confidence Intervals?
Transcribed Image Text:2. Suppose you want to estimate the following model: Reorders; = ß + R · Productsi + B · Daysi where Reorders; is the number of reorders. Products; is the number of ordered products. Days; is the number of days since the last order. Using Excel, you have the following output: SUMMARY OUTPUT Regression Statistics Multiple R 0.8373 R Square 0.7010 Adjusted R Square Standard Error 0.6998 3.1710 Observations 500 ANOVA df MS Significance F Regression 2 11717.33 5858.67 582.66 0.00 Residual 497 4997.38 10.06 Total 499 16714.71 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.9778 0.3416 2.8623 0.0044 0.3066 1.6489 products 0.6333 0.0188 33.6482 0.0000 0.5963 0.6703 days_since_prior_orde -0.0653 0.0133 -4.9207 0.0000 -0.0914 -0.0392 (a) Report regression results, what are B? B? B? What are Standard Errors of B? R? B? What are their 95% Confidence Intervals?
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C)if the number of days since the last order increases by 1  then the number of reordered items is predicted to decrease  by 0.0653.

 

d)  the percentage of the sample variation in recorders  that is explained by regression model is 70 .1 %.

 

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