The Data Matrix for the Synthetic Oil Use Survey

The data is from a study on automobile usage and tune-up costs that compares the use of synthetic versus regular oil. Synthetic automobile engine oil does not have the detrimental environmental impacts associated with regular oil.
Explanation of Variables
A = respondent ID number
B = User (=1) vs. nonuser (=0)
C = Premium (=1) vs. regular gasoline (=0)
D = $ paid for last tune-up
E = Miles driven last month
F = Gallons of gas in last fill-up
G = Pays with credit card (=1) or cash =(0)
H = Age of respondent
I = Recreational miles driven last week
J = Work miles driven last week
K = Last three digits of zip code
L = Gender of respondent: male (=1) or female (=2)
ID
USER
GASTYPE
PAID
MILES
GALLONS
PAYTYPE
AGE
RECMILES
WORKMILE
ZIPCODE
GENDER
1
0
1
20
1050
5
0
28
89
49
709
2
2
0
0
50
1175
14
0
28
30
62
711
1
3
1
1
105
1230
7
0
39
53
47
724
2
4
1
1
150
1680
9
0
35
48
42
728
2
5
0
1
40
1310
14
1
29
59
66
713
1
6
0
0
125
1500
8
1
32
78
49
723
1
7
0
1
110
1600
12
0
35
87
47
722
2
8
1
1
45
1720
10
0
35
59
76
703
2
9
0
0
250
1750
8
1
32
87
56
714
1
10
0
1
60
1770
11
1
32
41
100
710
1
11
1
1
20
2275
5
1
33
90
63
713
1
12
1
1
120
2500
10
1
38
43
64
705
1
13
0
1
70
1030
3
1
29
100
41
729
1
14
1
0
35
1100
7
0
32
31
26
736
1
15
0
0
70
1185
8
1
30
54
40
715
1
16
1
0
80
1225
12
1
28
41
48
723
2
17
0
0
100
1262
11
1
28
44
73
718
2
18
0
1
85
1295
7
0
32
60
77
724
1
19
1
0
220
1300
4
1
32
100
80
714
1
20
0
0
130
1550
6
1
31
54
54
725
1
21
1
0
155
1820
10
0
32
86
24
734
2
22
0
0
175
1890
8
1
31
51
36
725
1
23
0
1
50
1940
4
0
33
86
39
727
2
24
1
0
100
2200
10
0
36
100
120
734
1
25
0
0
80
2270
8
0
38
98
52
717
2
26
0
1
150
2440
8
1
35
35
44
720
2
27
1
1
20
2560
6
1
36
95
46
716
1
28
0
0
75
2730
7
1
37
46
52
714
1
29
1
1
55
1130
8
1
30
92
46
726
2
30
1
1
90
1575
12
1
31
52
37
731
2

Data Analysis

Frequencies

How many respondents paid with a credit card? How many paid cash? How many are males? How many are females? You can answer all these questions just counting the number of various answers occur. For example, to find out how people paid, all you have to do is count the number of people who selected response 0 (cash) and response 1 (credit card).

In Minitab, the Tables procedure will count the number of times each of the codes occurs.

STEPS
1. Click on “STAT”.
2. Select “TABLES”.
3. Select “TALLY INDIVIDUAL VARIABLES”.
4. Select the desired variables
a. For our example, we will select “GENDER” and “PAYTYPE” and move them to “VARIABLES” column.
5. Select “COUNTS” & “PERCENTAGES”.
6. Click “OK”.

Note: “Paytype” stands for pays with credit card (=1) or cash (=0). “Gender” – Gender of respondent: male = (=1) or female (=2).

Interpreting the frequency table: Minitab Output

Results for: MKTG 302- data in excel format Tally for Discrete Variables: PAYTYPE, GENDER

PAYTYPE Count Percent GENDER Count Percent 0 12 40.00 1 17 56.67 1 18 60.00 2 13 43.33 N= 30 N= 30

Each line of the frequency table describes a particular variable. 12 (40% of the total) paid cash. 18 (60% of the total) respondents used credit card. 17 out of 30 (56.7% of the total) respondents are males and 13 out of 30 (43.3% of the total) respondents are female.

We can also look these two variables in a graphical format.

STEPS
1. Click on “GRAPH”.
2. Select “BAR CHART”.
3. Select “SIMPLE”.
4. Select the desired variables
a. For our example, we will select “GENDER” and “PAYTYPE” and move them to “CATEGORICAL VARIABLES” column.
5. Click “OK”.

The bar chart shows the same information (looks nice, though!). From the frequency tables, we can’t tell whether males used credit card (or, cash) more than the females. We come back to this issue later in the note.

REMEMBER THAT WE ARE LIMITED IN OUR ANALYSIS OF NOMINAL VARIABLES (BOTH “PAYTYPE” AND “GENDER” ARE NOMINAL. We are limited frequency diagrams (counts) . Only one statistic is