tile:llprivate/varytolders/91/ 4pcq6c11sv4tflnjk9til8p40000gn/T/WebKitPDFs-BAKmc
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tile:llprivate/varytolders/91/ 4pcq6c11sv4tflnjk9til8p40000gn/T/WebKitPDFs-BAKmcy/Assig Syllabus.0204.551.001 Fal 12017.pdt Assignment 4-Regression.pdt 140 CHAPTER4 REGRESSICN MODELS Problems 4-9 Jotn Smith has developed the following fosecasting 1sttesegrade 98778880%61669569 Fisal average 93 78 8 3 84 4 64 5 6 f-deend for K10air conditioners (a) Develop a pegression wodel that could be used to predict final average in thcoung based on (a) Foeecant the demand for K10 when the tempera (B) Predict t final averago ofa 8tudent who made (b) What is the demand for a temaperature of 80P? e) Wa is the demand for & temperture of SF? (c) Give the values of r and foe this model. Incer pret the value of r in the conmest of this problem. 4-14 Using the data in Problem 4.13, test to see if there is a statisticay significant reluicnship between tte grude 0a the firs"ess and the final vefage at the 0.05 level of significance. Use the formulas in this 4.10 The operations manager of a musical instriment distribstor foels that demand for a particular type of guitar nay be related to the aumber o YouTobe vies for a mesic vidoo by the popular rock group Marble Pumpkins during de preceding month The chapter and Appendis D 15Using computer software find the leaset-squancs re- gression line for the daa Problem 4.13. Based the F test, is there a statistically sipnificant relation- ship between the first nest grude and the final wverage 4-16 Steve Caples, a real estat"ppraiser Lake Charles, Lousiona has developed a ngression model to belp area. The model was developed wing recently sold homes in a perticular neighborhood The prce of the bouse is based on the square footage (X) of the house. The model is (a) Graph these data so see whether a linear equa- Gon miga describe the rehip betweenhe The coefficiest of correlation for he model i 0 63 b) Using the equations presented in this chapeer compate the SST, SSE and SSR. Find the least- (a) Use the model to predicl the selling price of a quares regression line for these data b) A house with 1,860 square feet recently sold for $165,000. Explain why this is not Wht the () sing the regression eqatioa, predict guitar 4-11 Using the data in Prublem 4-10, sest to see if there is a stecistically signifscost relationship betwees c) If yoa were poing to we multipk regresion to develop an pprisd model, what oher quantits sales and YouTube views at the 0.05 level of sig d What is the coefficieal of determination for this '4-17 Accountants at the firm walkeraad walker be- nusualily high travel voachers wben they return ificance. Use the fomulas in this chapter and 4 12 Using compuer software.nndthe leag..quares re· gression ine for the data in Problem 4-10, Baeed on deF s there a stacistieally significant selicn- ship betwecn the demand for guirars ard the mamber from business trips. The accountants took a ple of 200 vouchers submitted trom the past year thry then developed the following multiple regs- sion equation relating expected travel cost (Y) 00 Dumber of days on the road (%) and distance 4-1 3 ) soudem* ina mara gemeni science class have ju re. ceived their grades on the first cest. The isstractor ha% provoded information about tbe frst test grades s sume previoss classes, as well as the final aver- apes for the same staderts. Some of these grades have ben sampled and are follows: 9000S48x,040x DISCUSSION QUESTIONS AND PROBLEMS 4 30 70 60 80 30Explanation / Answer
from excel regression :
output on above data is as follows::
heere least square equation: Y =18.9892+0.7399 X
also analysis of variance is as follows:
for above F test as p vlaue is very less(significant). Therefore we can conclude that there is statistically significant relationship between the first grade and final average in the course.
SUMMARY OUTPUT Regression Statistics Multiple R 0.920518105 R Square 0.847353582 Adjusted R Square 0.825546951 Standard Error 4.665084976 Observations 9 ANOVA df SS MS F Significance F Regression 1 845.6589 845.6589 38.8576 0.0004 Residual 7 152.3411 21.7630 Total 8 998 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 18.9892 9.7518 1.9473 0.0925 -4.0701 42.0485 -4.0701 42.0485 X 0.7399 0.1187 6.2336 0.0004 0.4592 1.0205 0.4592 1.0205Related Questions
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