My main question is how do you enter the data into minitab. I can\'t seem to kno
ID: 3175132 • Letter: M
Question
My main question is how do you enter the data into minitab. I can't seem to know how to enter the X data and Y data correctly...Much explaination would be appreciated. I just need insight for part A and B, as well as how to input the data into minitab and how to use it because for some reason my numbers kept differing from the solution, or the regression equation.
A mail-order catalog business selling personal computer supplies, software, and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse. The business problem facing management relates to the factors that affect warehouse distribution costs. Currently, a handling fee is added to each order, regardless of the amount of the order. Data collected over the past 20 months indicate the warehouse distribution costs (in thousands of dollars), the sales (in thousands of dollars), and the number of orders received. Complete parts a through h below.
a. State the multiple regression equation.
b. Interpret the meaning of the slopes, b1 and b2 , in this problem.
c. Explain why the regression coefficient, b0 , has no practical meaning in the context of this problem.
d. Predict the mean monthly warehouse distribution cost when sales are $500,000 and the number of orders is 4 comma 100 .
e. Construct a 95% confidence interval estimate for the mean monthly warehouse distribution cost when sales are $500 ,000 and the number of orders is 4 comma 100 .
f. Construct a 95% prediction interval estimate for the monthly warehouse distribution cost when sales are $500 ,000 and the number of orders is 4 comma 100 .
g. Explain why the interval in (e) is narrower than the interval in (f). h. What conclusions can you reach concerning warehouse distribution costs?
Cost_($thousands) Sales_($thousands) Orders 54.93 388 4014 71.33 445 3809 85.89 514 5300 62.69 401 4268 72.91 454 4299 69.48 451 4093 52.17 308 3210 70.33 490 4800 82.49 516 5238 76.33 502 4734 71.88 537 4420 53.06 357 2925 61.92 371 3973 71.31 325 4427 59.98 402 3965 78.36 494 4588 94.29 531 5589 58.76 449 3453 90.25 630 5080 93.08 592 5740Explanation / Answer
(a)
Following is the output of multiple regression analysis:
The least square regression line:
y' = -1.224 +0.0474*X1+ 0.0116*X2
b:
Intercept: The intercept is -1.224
Slope b1: 0.0474
It shows the for each unit increase in sales cost is increased by 0.0474 units keep other things constant.
Slope b2: 0.0116
It shows the for each unit increase in orders cost is increased by 0.0116 units keep other things constant.
c.
When sales and orders is zero then cost is -1.224. It is meaningless in this case.
d.
The predicted value of cost for X1 = 500 and X2 = 4100 is
y' = -1.224 +0.0474*500+ 0.0116*4100 = 70.036
So requried predicted value is $70,036.
SUMMARY OUTPUT Regression Statistics Multiple R 0.944376058 R Square 0.89184614 Adjusted R Square 0.879122156 Standard Error 4.508558835 Observations 20 ANOVA df SS MS F Significance F Regression 2 2849.523973 1424.761986 70.09173922 6.15683E-09 Residual 17 345.5607471 20.32710277 Total 19 3195.08472 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -1.223969677 6.231819773 -0.196406463 0.846620817 -14.37195999 11.92402064 Sales_($thousands), X1 0.047394479 0.019055726 2.487151629 0.023558813 0.007190413 0.087598545 Orders, X2 0.011622726 0.002148093 5.410716934 4.68369E-05 0.007090645 0.016154807Related Questions
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