TJ Maxx, a discount clothing retailer, would like to compare the sales per square foot in its different

locations, taking into account the substantial differences in income and population. The management is

planning to use this information to guide their location selections for new store openings.

The spreadsheet file TJ Maxx.xlsx, which accompanies this exam, includes Retail Sales data at 87

locations. This data set has been randomly split into two sections: The Training dataset contains 67

observations, representing the bulk of the locations, and the Holdout dataset includes 20 observations to

use to test and validate models that are constructed with the Training dataset.

For each of the locations in the dataset, we have values of the following variables:

Variable Coding & explanation

STORE ID :. Each store has a unique identifier number

SALES : Sales per square foot (in dollars/sq-ft)

INCOME : The median household income in the surrounding

Community : (in dollars)

POPULATION : The size of the surrounding population (in thousands)

MARKET : The market type which can take 3 possible values:

Help me Build the best model we can to predict the sales per square foot at any TJ Maxx location. Once we have settled on the model, print the regression output and provide a 1-2 page discussion of your regression model that

covers the following topics:

a) How did you choose the explanatory variables in your regression?

b) If you created new variables, explain what they are.

c) Explain how you validated your model

Store ID	Sales ($/SF)	Income	Population (000)	Market		Random
3	471.4	67000	831	Rural		0.325584
6	409.9	60000	774	Rural		0.507334
7	561.1	69000	684	Urban		0.625809
8	564.2	52000	592	Urban		0.39794
9	213.8	63000	222	Rural		0.668485
10	417.5	76000	1034	Suburban		0.624649
11	290.9	65000	395	Rural		0.429842
12	468.4	86000	713	Suburban		0.052057
13	319.8	66000	260	Rural		0.466951
18	441.5	78000	326	Urban		0.023217
20	572.6	64000	760	Urban		0.781223
21	355.8	73000	798	Suburban		0.581177
22	376	63000	612	Rural		0.366008
23	428	70000	926	Suburban		0.153008
24	310.5	65000	438	Rural		0.701262
25	555.2	73000	992	Suburban		0.156267
26	455.9	74000	786	Suburban		0.530419
27	489.1	68000	712	Urban		0.186439
28	454	92000	424	Suburban		0.072635
29	469.3	86000	1224	Suburban		0.394054
31	475.7	80000	953	Suburban		0.525134
33	458.2	69000	548	Rural		0.041619
34	495.8	84000	796	Suburban		0.75447
35	612.8	73000	872	Urban		0.73372
36	466.6	78000	923	Suburban		0.171836
37	401.7	71000	626	Suburban		0.269112
38	375.1	66000	323	Rural		0.525628
39	490.1	67000	559	Urban		0.608761
41	607.6	72000	499	Urban		0.078369
42	476.4	56000	947	Rural		0.411476
43	305.1	54000	705	Rural		0.081916
44	587.4	71000	1077	Urban		0.089739
45	491.1	59000	610	Urban		0.132568
46	483.5	59000	663	Urban		0.755113
47	379.3	86000	849	Suburban		0.282094
48	437.9	95000	884	Suburban		0.486075
49	508.9	67000	672	Urban		0.437197
50	573.8	76000	769	Urban		0.263417
51	376.8	85000	828	Suburban		0.25484
53	467.9	78000	421	Urban		0.508737
55	550.5	64000	452	Urban		0.476944
56	544.3	89000	503	Urban		0.290959
57	467.1	61000	581	Urban		0.705923
58	126.9	55000	321	Rural		0.64167
59	382	84000	740	Suburban		0.088533
60	403.3	61000	685	Urban		0.381972
61	407.5	63000	945	Rural		0.334219
62	512	71000	1075	Suburban		0.331482
63	691.7	76000	699	Urban		0.607442
64	569.4	73000	650	Urban		0.736546
65	425.6	70000	552	Rural		0.146243
68	442	74000	862	Suburban		0.606241
69	272.7	59000	434	Rural		0.051439
70	630.2	79000	531	Urban		0.148649
75	461.1	79000	793	Suburban		0.195759
76	352.7	75000	827	Suburban		0.215642
77	325.9	74000	270	Rural		0.212112
78	458.3	64000	452	Rural		0.50538
79	362.9	80000	578	Suburban		0.586547
80	442.9	66000	738	Rural		0.405788
81	641.9	88000	755	Urban		0.374868
82	287.2	56000	401	Rural		0.582873
83	313.1	62000	433	Rural		0.630649
84	468.4	72000	656	Rural		0.703657
85	616.5	69000	726	Urban		0.39427
86	276.6	65000	658	Rural		0.221737
87	580.2	71000	482	Urban		0.748047