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Table 5.1 Auto sales data example
Auto Sales Data—01/01/2005—01/31/2005
Record No.
Slsprn
Date
Make
Model
Amt Paid
Rebates
Sales Com
1
Bill
01/02/05
Ford
Wgn
$24,000
$2,500
$2,150
2
Henry
01/02/05
Toyota
Sdn
26,500
1,000
2,550
3
Harriet
01/03/05
Audi
Sdn
34,000
0
3,400
4
Ahmad
01/06/05
Audi
Cpe
37,000
0
5,550
5
Ahmad
01/06/05
Ford
Sdn
17,500
2,000
2,325
6
Henry
01/08/05
Toyota
Trk
24,500
1,500
2,300
7
Lupe
01/10/05
Ford
Wgn
23,000
2,500
2,050
8
Piego
01/12/05
Ford
Sdn
14,500
500
1,400
9
Kenji
01/13/05
Toyota
Trk
27,000
1,200
2,580
10
Ahmad
01/14/05
Audi
Cpe
38,000
0
5,700
11
Kenji
01/16/05
Toyota
Trk
28,500
1,500
2,700
12
Bill
01/16/05
Toyota
Sdn
23,000
2,000
2,100
13
Kenji
01/18/05
Ford
Wgn
21,500
1,500
2,000
14
Ahmad
01/19/05
Audi
Sdn
38,000
0
5,700
15
Bill
01/19/05
Ford
Wgn
23,000
1,000
2,200
16
Kenji
01/21/05
Toyota
Trk
26,500
1,500
2,500
17
Lupe
01/24/05
Ford
Sdn
13,500
500
1,300
18
Piego
01/25/05
Ford
Sdn
12,500
500
1,200
19
Bill
01/26/05
Toyota
Trk
22,000
1,000
2,100
20
Ahmad
01/29/05
Audi
Cpe
36,500
0
5,475
21
Bill
01/31/05
Ford
Sdn
12,500
500
1,200
22
Piego
01/31/05
Ford
Sdn
13,000
500
1,250
In this chapter, we examine some of the many useful Excel resources avail-
able to analyze qualitative data. This includes exploring the uses of PivotTable
and PivotChart reports: built-in Excel capability that permits quick and easy cross-
tabulation analysis, sometimes referred to as crosstab analysis. Crosstabs permit us
to determine how two or more variables in a set of data interact. Consider the auto
sales data we introduced in Chap. 4, which now appear in Table 5.1 in this chap-
ter. There are many questions a decision maker might consider in examining these
data. For example, is there a relationship between sales associates and the models of
automobiles they sell? More specifically, is there a propensity for some of the sales
associates to promote the sale of a particular automobile to a particular customer
demographic 1 ?
Although this type of analysis can be performed with sophisticated statistics,
in this chapter, we will use less rigorous numerical techniques to generate valu-
able insights. The simple, numerical information that results may be all that is
necessary for good decision making. Returning to our decision maker’s question,
1 In marketing, the term demographic implies the grouping or segmentation of customers into
groups with similar age, gender, family size, income, professions, education, religious affiliation,
race, ethnicity, national origin, etc. The choice of the characteristics to include in a demographic is
up to the decision maker.
 
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