OVERVIEW OF ASSIGNMENT:Your client is interested in knowing what variables explain the variation in Marketing%. In other words, what variables determine if dealers spend a higher or a lower % of their gross sales on marketing expenses. Thus, Marketing% is the dependent variable in your analysis.You will craft multiple models based on the information provided to you below and perform regression analysis using the data you provided earlier. You will communicate your findings to your audience. Results must be communicated through well-designed tables, as discussed in the lecture on presenting regression results. And these must be accompanied by thorough discussions that are accurate and precise. In short, you should produce a report that is well crafted–it is correct in conclusions/interpretations, visually appealing, professionally written, and meets the needs of your audience.
Marketing %
3.2
3.4
3.7
3.7
3.8
3.9
4
4
4
4
4
4
4.1
4.1
4.1
4.1
4.1
4.1
4.2
4.2
4.2
4.2
4.2
4.2
4.3
4.3
4.3
4.3
4.3
4.3
4.4
4.4
4.4
4.4
4.4
4.4
4.5
4.5
4.5
4.5
4.5
4.5
4.5
4.5
4.5
Competitors
2
4
1
6
4
3
3
6
6
5
7
5
3
4
1
2
2
6
5
5
6
5
6
3
7
5
4
7
11
5
6
6
6
7
7
5
6
5
7
5
8
6
10
7
7
Domestic
1
1
0
0
0
0
0
1
1
1
0
0
1
0
0
0
0
1
1
0
1
0
0
1
1
0
0
0
0
0
1
1
0
0
1
0
0
1
0
0
0
0
0
1
1
Road frontage
1
1
1
0
1
1
1
0
1
1
1
0
1
0
1
0
1
1
1
1
0
1
1
0
1
1
0
1
0
0
1
1
1
0
1
0
0
0
1
1
0
1
1
0
1
Lease
0
0
0
1
1
1
1
1
0
0
0
1
0
1
1
1
0
0
0
0
1
1
0
1
1
0
1
0
1
0
0
0
1
0
0
0
0
0
1
0
1
0
0
0
1
Locally owned
1
0
1
1
0
1
1
1
1
1
0
0
0
0
1
1
0
1
0
1
1
1
1
0
0
0
1
0
0
1
0
1
1
1
0
0
0
0
0
1
1
1
1
1
1
4.5
4.5
4.5
4.5
4.5
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.6
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.7
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.8
4.9
4.9
4.9
4.9
4.9
6
5
6
4
6
5
7
6
8
6
6
7
7
5
5
7
5
5
7
6
7
6
8
9
7
8
6
5
7
5
6
7
4
7
12
13
9
14
8
11
11
12
1
12
15
17
20
1
1
0
0
0
1
1
1
0
1
0
0
0
0
0
0
0
0
1
0
0
1
0
1
0
1
0
0
0
0
0
1
0
0
0
1
1
1
1
0
0
0
1
1
0
0
0
0
1
0
1
1
0
1
0
0
1
0
0
0
0
1
0
0
1
1
1
1
0
1
0
0
0
0
0
0
0
0
1
1
1
1
0
0
1
1
1
0
0
0
1
0
0
0
1
1
0
1
1
0
1
0
0
1
0
1
0
0
0
0
1
0
0
1
0
0
1
0
1
0
1
1
0
0
0
1
1
1
0
1
1
0
0
0
1
0
0
0
0
1
1
1
0
1
0
1
1
1
1
0
0
1
0
0
0
1
1
1
1
0
1
1
0
0
0
1
0
1
1
0
0
1
1
1
1
1
1
0
1
1
1
1
0
1
0
1
0
0
4.9
4.9
4.9
4.9
4.9
4.9
4.9
5
5
5
5
5
5
5.1
5.1
5.1
5.1
5.1
5.1
5.1
5.1
5.1
5.1
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.2
5.3
5.3
5.3
5.3
5.3
5.3
5.3
5.3
5.3
5.3
5.3
5.3
5.4
5.4
5.4
14
20
13
16
14
15
21
29
18
21
27
17
13
31
22
20
21
24
25
20
21
20
16
20
21
29
13
17
27
22
15
18
23
21
24
22
28
36
16
25
20
24
34
19
18
23
26
0
0
1
0
1
0
1
0
1
0
0
0
0
0
1
0
0
1
1
0
0
0
1
1
1
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
0
1
1
0
0
1
0
0
1
1
0
1
0
0
1
1
0
1
0
0
1
1
0
0
0
1
0
0
0
1
0
1
1
0
1
1
0
1
0
0
0
1
0
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
1
0
1
1
1
0
0
1
1
0
1
0
0
0
0
1
0
1
0
0
1
0
1
0
1
0
0
0
1
0
0
1
1
1
0
1
0
0
1
1
1
1
1
0
0
0
0
1
1
0
1
1
0
0
0
1
1
0
0
1
0
1
1
0
1
1
1
1
0
0
5.4
5.4
5.4
5.4
5.4
5.4
5.5
5.5
5.5
5.5
5.6
5.6
5.6
5.6
5.6
5.6
5.7
5.7
5.7
5.7
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.9
5.9
5.9
5.9
5.9
6
6
6
6.1
6.1
6.1
6.1
6.1
6.1
6.2
6.2
6.2
6.3
6.3
6.3
31
24
27
24
27
25
35
27
22
28
30
16
24
22
28
31
31
30
23
35
34
30
36
22
28
22
23
24
25
25
26
27
23
29
32
31
34
27
35
22
31
33
32
29
33
36
37
1
1
0
0
0
1
0
1
0
0
1
0
0
0
0
0
0
1
0
1
1
0
0
1
1
0
0
1
0
0
1
1
0
0
1
0
0
1
1
1
0
0
1
1
0
1
1
0
1
1
0
0
1
0
1
1
0
0
1
1
1
0
0
0
1
1
1
1
0
0
1
0
0
0
1
1
0
1
0
0
1
0
0
1
0
0
1
1
0
0
1
0
0
1
1
0
1
0
0
0
0
0
1
0
1
0
0
0
0
1
0
1
1
0
0
1
1
0
1
0
0
1
1
1
1
0
1
1
1
0
0
1
0
0
0
0
0
0
0
1
0
1
0
1
1
0
1
0
1
1
1
0
0
1
1
1
0
0
0
1
1
1
1
0
0
1
0
0
0
0
1
0
1
0
0
1
0
0
1
1
0
1
0
0
0
1
0
0
6.3
6.3
6.4
6.4
6.4
6.4
6.5
6.5
6.6
6.6
6.6
6.7
6.7
6.7
6.9
7
7.1
7.2
7.2
7.3
7.3
7.7
7.8
31
36
34
32
22
23
27
26
24
25
27
31
26
23
32
42
35
31
22
42
34
37
32
0
0
1
0
0
0
0
1
0
1
0
0
0
0
1
1
0
0
1
0
1
0
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
1
0
0
1
1
0
0
0
1
1
0
1
1
0
0
0
1
1
0
0
0
1
1
0
1
0
0
0
1
0
1
1
1
0
0
1
0
1
0
1
1
0
1
0
0
1
1
0
0
0
1
1
1
0
Full website
1
1
1
1
1
1
1
1
1
1
1
0
1
0
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
0
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
Car lines
2
2
3
1
2
3
2
2
2
3
2
2
3
3
3
2
2
2
3
1
2
2
4
3
1
2
1
3
3
3
2
2
2
1
3
2
4
3
3
3
2
2
3
3
1
Distance lower tax
77
56
55
36
45
79
61
44
38
74
56
85
44
61
58
39
71
68
86
44
54
37
59
44
92
29
56
58
55
89
74
79
39
59
50
41
59
27
55
81
55
56
28
30
76
Size (employees) Location R (Rural)
21
1
37
1
18
1
22
1
37
0
33
0
14
0
14
1
21
1
31
0
32
1
40
1
15
1
19
1
22
0
28
1
30
1
32
0
14
0
15
1
24
1
31
1
32
0
41
1
16
0
31
0
33
0
37
1
40
1
41
1
12
1
14
1
15
1
15
1
23
1
25
1
10
1
10
0
15
1
16
1
19
1
23
1
24
1
26
1
39
0
1
1
0
1
1
1
1
1
0
1
0
1
0
1
1
1
1
1
1
1
0
1
0
1
1
1
1
0
1
1
1
1
1
1
1
0
1
1
1
1
0
1
1
1
1
1
0
4
4
2
1
3
3
5
2
3
5
2
2
4
3
2
4
3
3
4
3
3
2
3
4
1
3
1
5
2
3
4
3
3
3
4
3
2
3
5
4
2
4
3
4
3
4
4
63
26
59
91
85
66
54
61
45
44
51
71
89
45
67
74
75
37
22
30
41
63
83
34
96
50
40
36
31
67
42
83
40
26
55
86
59
25
18
44
71
44
60
68
20
56
56
39
39
41
41
43
13
14
15
16
16
16
19
21
21
26
30
31
33
38
38
40
41
43
14
22
22
23
27
27
28
30
32
43
15
17
23
27
27
32
33
34
35
9
12
26
28
31
1
1
0
0
0
0
1
1
1
1
1
0
0
0
1
1
0
1
1
1
1
1
0
0
1
1
1
1
1
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
1
0
0
1
0
1
0
1
0
0
1
1
1
0
1
0
1
1
1
0
1
0
0
1
1
1
0
1
1
0
0
1
0
1
0
1
1
1
0
1
0
0
1
3
2
2
2
1
3
4
1
4
4
3
1
4
1
4
5
2
2
1
3
4
3
1
1
2
4
4
5
3
5
2
3
4
4
5
2
1
4
3
3
4
3
2
2
3
4
3
70
35
38
46
59
51
45
42
35
60
31
42
77
26
61
25
29
33
40
58
31
33
49
66
26
42
51
33
65
43
56
43
62
51
33
45
23
58
42
60
48
36
51
69
63
15
49
33
38
39
41
42
42
42
14
21
26
33
34
40
16
17
23
25
34
35
38
40
42
42
11
14
16
21
24
34
35
37
41
10
13
15
16
19
23
31
31
32
35
35
41
10
16
18
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
1
0
1
0
1
0
0
1
0
0
0
0
0
1
0
1
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0
0
1
4
3
2
1
4
5
2
4
5
5
1
5
5
5
2
4
4
2
5
2
3
3
4
5
5
4
5
5
4
3
4
5
3
4
4
5
4
3
3
4
4
5
5
4
3
4
4
41
37
45
39
72
18
20
51
44
20
34
22
13
14
21
21
9
15
31
32
21
33
16
10
51
36
8
40
24
13
33
45
22
43
37
33
25
33
9
44
34
34
19
45
33
23
29
20
22
26
27
38
38
19
31
33
37
9
12
15
18
25
31
23
24
41
44
16
17
26
26
29
42
43
10
13
14
30
40
31
39
41
15
16
16
24
25
37
20
33
35
17
24
26
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
5
3
5
5
4
5
5
3
4
3
4
4
4
4
4
4
4
5
4
5
5
33
33
31
28
12
41
31
23
20
24
32
27
20
15
17
45
11
13
20
16
13
22
17
32
37
20
25
37
39
33
41
38
40
40
18
29
37
35
26
41
22
41
25
36
17
33
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
Location S (Suburb)
0
0
0
0
1
1
1
0
0
0
0
0
0
0
1
0
0
1
1
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
Location C (City)
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
Domestic
YES
YES
NO
NO
NO
NO
NO
YES
YES
YES
NO
NO
YES
NO
NO
NO
NO
YES
YES
NO
YES
NO
NO
YES
YES
NO
NO
NO
NO
NO
YES
YES
NO
NO
YES
NO
NO
YES
NO
NO
NO
NO
NO
YES
YES
Road frontage
YES
YES
YES
NO
YES
YES
YES
NO
YES
YES
YES
NO
YES
NO
YES
NO
YES
YES
YES
YES
NO
YES
YES
NO
YES
YES
NO
YES
NO
NO
YES
YES
YES
NO
YES
NO
NO
NO
YES
YES
NO
YES
YES
NO
YES
Lease
NO
NO
NO
YES
YES
YES
YES
YES
NO
NO
NO
YES
NO
YES
YES
YES
NO
NO
NO
NO
YES
YES
NO
YES
YES
NO
YES
NO
YES
NO
NO
NO
YES
NO
NO
NO
NO
NO
YES
NO
YES
NO
NO
NO
YES
Locally owned
YES
NO
YES
YES
NO
YES
YES
YES
YES
YES
NO
NO
NO
NO
YES
YES
NO
YES
NO
YES
YES
YES
YES
NO
NO
NO
YES
NO
NO
YES
NO
YES
YES
YES
NO
NO
NO
NO
NO
YES
YES
YES
YES
YES
YES
0
0
1
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
YES
YES
NO
NO
NO
YES
YES
YES
NO
YES
NO
NO
NO
NO
NO
NO
NO
NO
YES
NO
NO
YES
NO
YES
NO
YES
NO
NO
NO
NO
NO
YES
NO
NO
NO
YES
YES
YES
YES
NO
NO
NO
YES
YES
NO
NO
NO
NO
YES
NO
YES
YES
NO
YES
NO
NO
YES
NO
NO
NO
NO
YES
NO
NO
YES
YES
YES
YES
NO
YES
NO
NO
NO
NO
NO
NO
NO
NO
YES
YES
YES
YES
NO
NO
YES
YES
YES
NO
NO
NO
YES
NO
NO
NO
YES
YES
NO
YES
YES
NO
YES
NO
NO
YES
NO
YES
NO
NO
NO
NO
YES
NO
NO
YES
NO
NO
YES
NO
YES
NO
YES
YES
NO
NO
NO
YES
YES
YES
NO
YES
YES
NO
NO
NO
YES
NO
NO
NO
NO
YES
YES
YES
NO
YES
NO
YES
YES
YES
YES
NO
NO
YES
NO
NO
NO
YES
YES
YES
YES
NO
YES
YES
NO
NO
NO
YES
NO
YES
YES
NO
NO
YES
YES
YES
YES
YES
YES
NO
YES
YES
YES
YES
NO
YES
NO
YES
NO
NO
1
1
1
1
0
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
0
0
0
1
1
1
1
1
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
1
1
0
NO
NO
YES
NO
YES
NO
YES
NO
YES
NO
NO
NO
NO
NO
YES
NO
NO
YES
YES
NO
NO
NO
YES
YES
YES
NO
NO
NO
NO
NO
NO
NO
YES
NO
NO
NO
NO
YES
NO
NO
NO
NO
NO
YES
NO
NO
NO
YES
NO
NO
NO
YES
NO
NO
YES
YES
NO
NO
YES
NO
NO
YES
YES
NO
YES
NO
NO
YES
YES
NO
YES
NO
NO
YES
YES
NO
NO
NO
YES
NO
NO
NO
YES
NO
YES
YES
NO
YES
YES
NO
YES
NO
NO
NO
YES
NO
YES
YES
YES
YES
YES
YES
YES
NO
NO
NO
NO
NO
NO
NO
NO
NO
YES
YES
YES
YES
NO
NO
YES
NO
YES
YES
YES
NO
NO
YES
YES
NO
YES
NO
NO
NO
NO
YES
NO
YES
NO
NO
YES
NO
YES
NO
YES
NO
NO
NO
YES
NO
NO
YES
YES
YES
NO
YES
NO
NO
YES
YES
YES
YES
YES
NO
NO
NO
NO
YES
YES
NO
YES
YES
NO
NO
NO
YES
YES
NO
NO
YES
NO
YES
YES
NO
YES
YES
YES
YES
NO
NO
1
1
1
1
1
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
YES
YES
NO
NO
NO
YES
NO
YES
NO
NO
YES
NO
NO
NO
NO
NO
NO
YES
NO
YES
YES
NO
NO
YES
YES
NO
NO
YES
NO
NO
YES
YES
NO
NO
YES
NO
NO
YES
YES
YES
NO
NO
YES
YES
NO
YES
YES
NO
YES
YES
NO
NO
YES
NO
YES
YES
NO
NO
YES
YES
YES
NO
NO
NO
YES
YES
YES
YES
NO
NO
YES
NO
NO
NO
YES
YES
NO
YES
NO
NO
YES
NO
NO
YES
NO
NO
YES
YES
NO
NO
YES
NO
NO
YES
YES
NO
YES
NO
NO
NO
NO
NO
YES
NO
YES
NO
NO
NO
NO
YES
NO
YES
YES
NO
NO
YES
YES
NO
YES
NO
NO
YES
YES
YES
YES
NO
YES
YES
YES
NO
NO
YES
NO
NO
NO
NO
NO
NO
NO
YES
NO
YES
NO
YES
YES
NO
YES
NO
YES
YES
YES
NO
NO
YES
YES
YES
NO
NO
NO
YES
YES
YES
YES
NO
NO
YES
NO
NO
NO
NO
YES
NO
YES
NO
NO
YES
NO
NO
YES
YES
NO
YES
NO
NO
NO
YES
NO
NO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
NO
NO
YES
NO
NO
NO
NO
YES
NO
YES
NO
NO
NO
NO
YES
YES
NO
NO
YES
NO
YES
NO
NO
NO
NO
NO
NO
NO
YES
NO
YES
YES
NO
NO
NO
NO
NO
NO
YES
NO
NO
YES
YES
NO
NO
NO
YES
YES
NO
YES
YES
NO
NO
NO
YES
YES
NO
NO
NO
YES
YES
NO
YES
NO
NO
NO
YES
NO
YES
YES
YES
NO
NO
YES
NO
YES
NO
YES
YES
NO
YES
NO
NO
YES
YES
NO
NO
NO
YES
YES
YES
NO
Full website
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
NO
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
YES
YES
YES
NO
YES
YES
YES
YES
YES
YES
NO
YES
YES
YES
YES
YES
YES
YES
YES
Location
R
R
R
R
S
S
S
R
R
C
R
R
R
R
S
R
R
S
S
R
R
R
C
R
S
S
S
R
R
R
R
R
R
R
R
R
R
C
R
R
R
R
R
R
S
YES
YES
NO
YES
YES
YES
YES
YES
NO
YES
NO
YES
NO
YES
YES
YES
YES
YES
YES
YES
NO
YES
NO
YES
YES
YES
YES
NO
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
YES
YES
NO
YES
YES
YES
YES
YES
NO
R
R
S
S
S
C
R
R
R
R
R
S
S
S
R
R
C
R
R
R
R
R
S
S
R
R
R
R
R
C
R
R
R
R
R
R
S
S
S
S
S
S
S
S
S
S
S
YES
NO
YES
NO
YES
NO
NO
YES
NO
YES
NO
YES
NO
NO
YES
YES
YES
NO
YES
NO
YES
YES
YES
NO
YES
NO
NO
YES
YES
YES
NO
YES
YES
NO
NO
YES
NO
YES
NO
YES
YES
YES
NO
YES
NO
NO
YES
S
S
S
S
R
S
S
S
S
S
S
R
S
S
S
S
S
S
S
S
S
S
S
S
S
S
C
C
R
S
S
S
S
S
C
C
C
S
S
S
S
S
R
S
C
C
S
NO
YES
NO
NO
YES
NO
YES
NO
YES
NO
NO
YES
NO
NO
NO
NO
NO
YES
NO
YES
NO
NO
NO
YES
NO
NO
NO
NO
YES
NO
NO
NO
NO
NO
NO
NO
NO
YES
NO
NO
NO
NO
NO
YES
NO
NO
YES
S
S
S
S
S
C
C
C
C
S
S
S
S
S
R
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
R
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
NO
NO
NO
YES
NO
NO
NO
NO
YES
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
C
C
C
C
C
C
C
R
C
C
C
C
C
C
C
C
C
C
R
C
C
C
C
Variable definitions
Marketing % = percent of gross sales that dealer puts towards marketing unrelated to website expenses
Competitiors = # of other dealers (any brand) in 2 hour radius
Domestic = YES if sells at least one domestic brand
Car lines = # of different car lines offered (ex: Ford, GM, Honda)
Road frontage = YES if dealer has road frontage on a major road
Lease = YES if dealer leases, as opposed to owns, the facility
Distance lower tax = distance from dealer to the closest dealer in a different state where sales tax rate on cars is lower than i
Locally owned = YES if dealership has local ownership (as opposed to being part of a chain of dealers and/or with non-local o
Full website = YES if dealerships has comprehensive website where complete inventory can be viewed
Size (employees) = # of employees at dealership related to sales and finance (is a proxy for size of dealership)
Location: A description of location of dealer based on 3 categorical options…R=Rural; S=Suburb; C=City
e on cars is lower than in own state
Name: Chanee Yarborough
1. Introduction
This report analyzes car dealerships’ advertising and marketing data to provide CAPITAL GROUP
with actionable insights.
The analysis begins with descriptive statistics to establish a base understanding for the subsequent
analyses. Next, a box plot is used to visualize the distribution of the Marketing % variable, and
scatterplots of relationships between the Marketing % variable and other variables are presented.
Finally, hypothesis tests are conducted to address questions concerning the Marketing % variable
across different subgroups of dealerships.
The findings are presented in a comprehensible manner, and key findings are highlighted to ensure
the report’s accessibility and to aid the CAPITAL GROUP in its strategic initiatives.
2. Dataset
The dataset used for analysis in this report includes 209 observations, each representing a car
dealership’s marketing and operational details.
The dataset includes various variables that capture car dealerships’ operational and strategic
aspects. “Marketing %” represents the percentage of gross sales allocated to marketing efforts,
excluding website expenses. “Competitors” quantifies the number of other dealerships within a 2hour radius, regardless of the brand. “Domestic” indicates whether a dealership sells at least one
domestic car brand. The “Car lines” variable counts the different car lines offered by a dealership,
such as Ford, GM, and Honda. “Road frontage” denotes whether a dealership is on a major road.
“Lease” specifies if the dealership facility is leased rather than owned. “Distance lower tax”
measures the proximity of a dealership to the nearest dealer in a different state where the sales tax
rate on cars is lower. “Locally owned” distinguishes dealerships that are locally owned from those
that are part of a chain or have non-local ownership. “Full website” identifies dealerships with a
comprehensive website showcasing their complete inventory. “Size (employees)” refers to the
number of sales and finance-related employees, serving as a proxy for the dealership’s size. Lastly,
“Location” categorizes the dealership’s location as Rural (R), Suburb (S), or City (C), providing
insight into the geographic context of each dealership’s operation.
3. Descriptive Statistics and Correlation Matrix
We begin our analysis with a discussion of the descriptive (summary) statistics of the primary
variable of interest, Marketing %
The statistical summary for the Marketing % allocation of car dealerships, with a count of 209
observations, had a range spanning from a minimum of 3.2% to a maximum of 7.8%, with the most
frequently occurring value (mode) being 4.6%. The average (mean) marketing spend was 5.17%
of gross sales, closely aligned with a median of 5.0%. The mean being higher than the median
suggests that Marketing % allocation among the dealerships is slightly right-skewed.
Marketing % exhibits moderate variability, as reflected by a standard deviation of 0.8475%. With
Name: Chanee Yarborough
a skewness of 0.6529, the distribution exhibits a slight tilt towards the higher values, indicating a
longer tail on the right side, aligning with our earlier observation of the distribution. However, the
distribution is relatively flat, with a kurtosis value of 0.2178, suggesting that the data does not have
heavy tails.
To provide insights into the strongly related variables, a correlation matrix of the quantitative
variables in the dataset is presented in Table 1.
Table 1: Correlation matrix of quantitative variables in the dataset.
Marketing %
Marketing %
1
Competitors
0.8565
Car lines
0.5086
Distance lower tax -0.5950
Size (employees)
0.0943
Competitors Car lines Distance lower tax Size (employees)
1
0.3837
-0.5669
0.0404
1
-0.3647
0.0698
1
-0.0046
1
From Table 1, the most robust relationship is between Marketing % and Competitors, with a
high positive correlation (0.8565), indicating that dealerships’ marketing spend tends to
increase with the number of competitors. There is also a moderate positive correlation (0.5086)
between Marketing % and Car lines, suggesting that dealerships with a wider range of car lines
typically allocate more to marketing. Conversely, Distance to lower-tax areas has a notable
negative correlation (-0.5950) with Marketing %, implying that dealerships further from lower-tax
regions spend less on marketing, potentially due to higher operational costs. This variable also
negatively correlates (-0.5669) with the number of Competitors, hinting at fewer competitors being
present as the distance from low-tax areas increases. Car lines show a moderate positive correlation
(0.3837) with Competitors, which may reflect a tendency for competitive markets to have a
broader range of products. Size (employees), however, shows weak correlations with all other
variables, indicating that the number of employees is not strongly linearly related to marketing
spend, competitive presence, product range, or tax considerations.
4. Data Visualization
In this section, we present a visualization of the data, first a Boxplot of Marketing % (Figure 1)
and then two scatter plots: Marketing % vs. Distance to lower tax (Figure 2a) and Marketing % vs.
Size (employees) (Figure 2b)
4.1 Box Plot of Market % variable
The box plot (Figure 1) displays the distribution of Marketing % for car dealerships. The middle
50% of the data (interquartile range) falls between 4.6% and 5.7%, with a median of 5%. One
notable outlier above the upper quartile indicates a dealership with an unusually high marketing
spend of approximately 7.8%. The plot suggests a moderately right-skewed distribution, which
aligns with the earlier mentioned positive skewness value in the descriptive statistics section.
Name: Chanee Yarborough
Figure 1: Boxplot of Marketing % Variable
4.2 Scatterplots
Figures 2(a) and 2(b) show the scatterplots of Marketing % vs. the Distance to lower tax and Size
(employees) variables.
Figure 2: (a) Scatter plot of Marketing % vs. Distance to lower tax, and (b) Scatterplot of
Marketing % vs. Size (employees)
The scatterplot in Figure 2(a) reveals a downward sloping pattern among the clusters of points,
signifying a negative linear trend between Marketing % and Distance to lower tax. This suggests
that dealerships located further from lower-tax regions tend to allocate a smaller percentage
of their gross sales to marketing, possibly due to increased operational costs.
On the other hand, the scatterplot in Figure 2(b) demonstrates a weak or non-existent correlation
between Marketing % and Size (employees), indicating that the number of employees at a
dealership does not have a strong linear relationship with the proportion of gross sales spent
on marketing.
5. Hypotheses Tests
To address some key questions, the results for various hypotheses questions are in this section.
Name: Chanee Yarborough
A. Null Hypothesis (H0): The mean Marketing % for dealerships that own their buildings is
equal to that of dealerships that lease.
Alternative Hypothesis (H1): The mean Marketing % for dealerships that own their
buildings is not equal to that of dealerships that lease.
Result: The result of the test that there is no statistically significant difference, at 0.05
significance level, t(207) = 0.12, p = .90. There is no difference the mean Marketing %
between car dealerships that own their buildings and those that lease.
B. Null Hypothesis (H0): The mean Marketing % for dealerships that have 32 or more
employees is equal to that of 31 or fewer employees.
Alternative Hypothesis (H1): The mean Marketing % for dealerships that have 32 or more
employees is not equal to that of 31 or fewer employees.
Result: The result of the t-test at 0.05 significance level is statistically significant, t(207) =
22.77, p < .0001 (two-tailed), leading us to reject the null hypothesis and accept the
alternative hypothesis.
C. Null Hypothesis (H0): The mean Marketing % for dealerships in suburbs is equal to the
mean Marketing % for dealerships in rural locations (ignoring city locations).
Alternative Hypothesis (H1): The mean Marketing % for dealerships in suburbs is less
than the mean Marketing % for dealerships in rural locations (ignoring city locations).
Result: The result of the t-test at 0.05 significance level is not statistically significant,
t(138) = 4.54, p > .999 (one-tailed). We cannot conclude that the mean Marketing %
for suburban dealerships is significantly lower than for rural dealerships.
D. Null Hypothesis (H0): The mean Marketing % for dealerships with 1-3 competitors equals
the mean Marketing % for dealerships with 4 or more competitors.
Alternative Hypothesis (H1): The mean Marketing % for dealerships that have 1-3
competitors is less than the mean Marketing % for dealerships with 4 or more competitors.
Result: The result of the t-test at 0.05 significance level is statistically significant, t(207) =
-4.54, p < .001 (one-tailed), leading us to reject the null hypothesis and accept the
alternative hypothesis. We can conclude that the mean Marketing % for dealerships
that have 1-3 competitors is lower than the mean Marketing % for dealerships with
4 or more competitors.
6. Conclusion
The key findings from the analysis indicate that marketing spending is moderately rightskewed. A negative correlation exists between marketing spend and distance to lower-tax areas,
while no substantial correlation is found with the number of employees. Hypothesis testing
confirms that market competition and having a large employee size (> 32) rather than
dealership premises ownership status more significantly influences marketing budgets.
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