Develop a regression model using the manufacturing file. Use Number of e Value A

Question

Develop a regression model using the manufacturing file. Use Number of e Value Added by manufacturing, Value of industrial shipments, New capital expenditures, and End of year inventory to predict Cost of materials Set up and solve this problem in Excel. Use highlighting to emphasize important cells. Insert text boxes to provide answers to the following questions, based on your final output (run the regression a second time, if appropriate): 1. What proportion of the variance in the dependent variable is accounted for by the combined effect of the independent variables? 2. Does it appear that an appropriate number of variables are used? Explain. 3. Were any variables in the initial solution not significant? If yes, list them. 4. Are there any outliers? 5. What is the final regression equation?

Explanation / Answer

Given table,

Output of the excel after performing regression is given by

a) proportion of variance

is available in the above standard error column

varaince = n* SE^2

= 42*SE^2

variance for industrial shipments is large

this regression can be done by other ways as

if s=1, x1=1, else =0

s=2, x2=1, else =0

s=3, x3=1, else=0

s=4, x4=1, else=0

then running regression again

Now the output has standard deviation that is accountable

hence all the variables are valid

3) values added by manf (m) is insignificant since coeffcient is very less and approximating to 1

Removing m variable, we get

output as

we didnt get much significance increase in R

hence our assumption that m is insigificant is false

hence removing x4 variable we can

be removed which does not alter R

x4 is insignificant

4) There are no outliers from the line plot graphs that are obtained in the excel

5) Final regression equation

y =26.16 + 16.63* e - 3.0611 * c + 3.354 * i - 0.112 *m

x1,x2,x3 have large standard deviation we elimianted them

cost of materials(y) no of employees (e values added by manf (m) value of industrial shipment(s) new capital exp (c End of year inventory(i) 4219 52 2471 2 292 929 5357 74 4307 2 454 1427 1061 13 63 1 20 325 707 17 817 1 84 267 10421 169 8986 3 534 2083 4140 51 3145 2 220 697 7125 55 4076 2 176 1446 8994 84 3806 2 423 1014 5504 61 4276 2 464 1291 716 27 1239 1 22 356 8926 200 9423 3 200 2314 11121 294 11045 3 189 2727 2283 38 1916 1 29 682 364 17 599 1 21 197 1813 34 2063 1 20 450 71 1 34 1 2 17 1321 31 1445 1 16 526 12376 224 10603 3 465 2747 9661 83 5775 3 539 578 19285 172 10404 4 1071 3979 18632 257 13274 4 711 3329 2170 51 1909 1 88 355 7290 82 4606 2 182 580 8135 94 5518 2 715 1604 12980 273 12464 3 481 3535 4011 70 5447 2 358 829 5101 37 2290 2 128 447 3755 81 4182 2 177 956 2694 54 2818 2 109 718 3279 15 2201 2 698 725 20596 116 18848 4 3143 4257 10604 55 9655 3 2360 1502 24634 212 15668 4 1352 3976 28963 232 25918 4 1750 5427 8483 403 30692 4 1277 894 6940 121 17982 3 311 1216 8863 136 17957 3 618 3736 2823 69 9699 2 144 874 29572 604 38407 4 2959 4300 3811 41 3878 2 198 688 1047 21 3989 2 66 577 2055 65 4388 2 130 504

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