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A national chain of women's clothing stores with locations in large shopping malls thinks that it can do a better job of planning store renovations

A national chain of women's clothing stores with locations in large shopping malls thinks that it can do a better job of planning store renovations and expansions if it understands what variables impact sales. It plans a small pilot study using data on stores in 24 different mall locations (see data below). Use a regression model and discuss your findings. which of the independent variables are statistically significant (that is, you can reject the null of no significance)? which of the following correctly interprets the coefficient for Size? which of the following correctly interprets the coefficient for nearest competitor?

The variables for this model can be defined as follows:

Sales = monthly sales per store

Size = store size in square feet

Windows = number of linear feet of window display

Competitors = number of competitors located in the mall

Mall Size = size of mall in sq. feet

Nearest Competitor = distance to competitor in feet

Sales
Size
Windows
Competitors
Mall Size
Nearest Competitor
4453
3860
39
12
943700
227
4770
4150
41
15
532500
142
4821
3880
39
15
390500
263
4912
4000
39
13
545500
219
4774
4140
40
10
329600
232
4638
4370
48
14
802600
257
4076
3570
37
16
463300
241
3967
3870
39
16
855200
220
4000
4020
44
21
443000
188
4379
3990
38
16
613400
209
5761
4930
50
15
420300
220
3561
3540
34
15
626700
167
4145
3950
36
14
601500
187
4406
3770
36
12
593000
199
4972
3940
38
11
347100
204
4414
3590
35
10
355900
146
4363
4090
38
13
490100
206
4499
4580
45
16
649200
144
3573
3580
35
18
685900
178
5287
4380
42
15
106200
149
5339
4330
40
10
354900
231
4656
4060
37
11
598700
225
3943
3380
34
16
381800
163
5121
4760
44
17
597900
224
4557
3800
36
14
745300
195

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Sales b0 b1Size b2windows b3competitors b4mallsize b5nearestcompetitor So if we apply ols into the g... blur-text-image

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