Data Analysis of Keels Agency: Cycle World Customer Behavior Report

This report presents a comprehensive data analysis of the Keels Agency's Cycle World customer data, exploring the relationships between various factors. The analysis includes descriptive statistics, correlation analysis, and hypothesis testing to understand customer behavior, exercise habits, and fitness levels. The study investigates the association between product lines, income, age, sex, education, marital status, times/week of cycling, miles/week, and fitness levels. The report utilizes the PSPP software for calculations, presenting findings through tables, histograms, and bar plots. Key findings include the distribution of fitness levels, the relationship between product lines and income for excellent-shaped individuals, and the impact of cycling on maintaining excellent body shape. The report tests several hypotheses related to fitness, product line, income distribution, and cycling habits. The analysis reveals moderate links between product line and income, and provides insights into the characteristics of customers with excellent fitness levels.

Running head: DATA ANALYSIS OF KEELS AGENCY
Data Analysis of Keels Agency
Name of the Student:
Name of the University:
Author’s Note:

DATA ANALYSIS OF KEELS AGENCY 1
Executive Summary:
The Cycle World customers’ data of Keels agency deals with the interrelationship and significance of association
between several factors or variables are more or less high in different levels. We have calculated the descriptive summary
of different attributes of the dataset. We also found strong and moderate strong linear relationship among product level
and all factors, income and all factors. We carried out the simple linear relationships between age and income. Not only
that, correlation between several factors (taking two factors each time) are calculated but also their links were discussed.
Besides, the histograms of income level and Times/week of cycling also provided in the report. The one-to-one bar plots
between levels of Fitness and Miles/week and between Times/week and Miles/week are given in the report. It is crucial
to note that, the report analyzed the KEELS data and tested the hypotheses. It also helped us to draw necessary
interpretations and inferences in the next segments of the report. We focus mainly on the topic that do people exercise a
lot and think that are they excellent shaped? The next part is to find whether the income increases with the increased
product line for excellent shaped people or not.

DATA ANALYSIS OF KEELS AGENCY 2
Table of Contents
Introduction:-..........................................................................................................................................................................3
Data Collection:-.....................................................................................................................................................................3
Data Description:-...................................................................................................................................................................4
Hypothesis Testing:-...............................................................................................................................................................5
Data Analysis and Analysis Methodology with Findings:-....................................................................................................5
Conclusion and Interpretation:-............................................................................................................................................11

DATA ANALYSIS OF KEELS AGENCY 3
Introduction:-
The Keels Agency (TKA) is a small advertising agency in Portland, Oregon. They give supports to the clients for
biggest returns in the advertising dollars. This company has a fantastic track record of supporting the advertising
organizations for their expenses. As discovered by Beth Keels, founder and CEO of the agency cut the cost small
advertising groups. For first-time clients, the thought of investing and advertising always leads to question about whether
the expense would worth the investment or not. TKA always considers their clients’ opportunities and goodness. Client
groups require considerable explanation about how TKA locate the advertising outlets. The advertising outlets like TV,
newspaper and magazines are related with the particular profile of the public ground. Therefore, TKA targets to advertise
directly on high potential customers.
The report verifies the association between different types of factors such as Product Line, Age, Sex, Education,
Marital Status, Income, Times/Week, Miles/Week and levels of Fitness. The data is collected from the survey. It is a
secondary data analyzed by me.
In the next segments of the report, we are willing to deliver the details about data description, data collection, data
processing or data analysis and data interpretation. All the calculations are worked out with the help of “PSPP” software.
We focus mainly on the topic that do people exercise a lot and think that are they excellent shaped? The next part is to
find whether the income increases with the increased product line for excellent shaped people or not.
Data Collection:-
The data file KEELS.DAT on the data disk contains the coded data from the survey of Cycle World customers.
When TKA launched Cycle World’s advertising budgets, typical subscriber was expected to represent a larger proportion
of the subscriber base for the magazine with a single focus of the subject. We also have enlisted the potential magazine
outlets and the profile of the typical customer for each sample. The data was collected with the help of Questionnaire
method asked to the Cycle World employees. The set of questionnaire was such as-
1. What is the level of your product line?
2. What is your age?
3. What is your sex?
4. What is your education level?
5. What is your Marital Status?
6. What is your current income?

DATA ANALYSIS OF KEELS AGENCY 4
7. What is the average number of times you plan to use a bicycle each week?
8. What is the average number of miles you completed cycling each week?
9. What is your level of fitness?
The responses of the questionnaire set are tables in a file named KEELS.DAT for analysis purpose. The data
consists of 200 responses among which 20 sets responses are incomplete. Therefore we have filtered the dataset and 180
sets of responses were undertaken for calculation of part one. In the second part, we filtered the data according to the
excellent fitness level and analysis between income and product line is incorporated. In this part our calculation is related
with only 31 frequencies.
Data Description:-
The dataset have total nine factors each having 180 samples. Age, Income, Times/Week and Miles/Week are
numeric in nature. Therefore they have scale parameter. The all other variables such as Product line, Sex, Education,
Marital Status and level of fitness are ordinal in nature. The data are coded as follows:
Product Line: Levels are, 1 = low product line, 2 = middle product line, 3 = high product line.
Age : Age on last birthday.
Sex : 1 = male and 2 = female.
Education : 1 = no high school diploma, 2 = high school diploma, 3 = some college-level work, 4 = college
degree, 5 = graduate work on degree.
Marital Status: 1 = single, 2 = married.
Income : Annual family income, rounded off to the nearest $1000.
Times/Work : Average number of times the person uses or plans to use bicycle each week.
Miles/Work : Average number of miles completed or planned in each week.
Fitness : Self-rated fitness level, based on scale, 1=poor shape, 2= moderate shape, 3= good shape, 4=
very good shape and 5=excellent shape.
For the second part of the assignment, we filtered the data as per excellent fitness level (leveled as 5). We then
have only 31 frequencies whose fitness level is excellent. The income and product line association was found by filtered
data.

DATA ANALYSIS OF KEELS AGENCY 5
Hypothesis Testing:-
Some hypotheses are to be tested in the analysis and we are eager to draw the calculations.
Hypothesis1-
H0= the Fitness level has high frequency for excellent shape.
HA= the Fitness level does not have highest frequency for excellent shape.
Hypothesis2-
H0= the Product Line is high for excellent fitness level.
HA= the Product Line is not high for excellent fitness level.
Hypothesis3-
H0= the frequency distribution of amount of Income is normally distributed for excellent shaped persons.
HA= the frequency distribution of amount of Income is not normally distributed for excellent shaped persons.
Hypothesis4-
H0= miles per week travelling by bicycle should be very high for maintaining excellent shape.
HA= miles per week travelling by bicycle may not be very high for maintaining excellent shape.
Hypothesis5-
H0= Product Line has significant association and linear relationship with income for excellent shaped persons.
HA= Product Line does not have significant association and linear relationship with income for excellent shaped persons.
Data Analysis and Analysis Methodology with Findings:-

DATA ANALYSIS OF KEELS AGENCY 6
Table: Valid cases = 180; cases with missing value(s) = 0.
Variable N Mean Std Dev Minimum Maximum
PRODUCT LINE 180 1.77 0.78 1 3
AGE 180 28.99 7.05 18 50
SEX 180 1.41 0.49 1 2
EDUCATION 180 3.68 0.63 2 5
MARITAL STATUS 180 1.64 0.48 1 2
INCOME 180 35672.22 12423.1 16000 74000
TIMES/WEEK 180 3.29 1.06 1 7
MILES/WEEK 180 103.83 51.7 20 360
FITNESS 180 3.29 0.98 1 5
Table1: Descriptive statistics of the factors.
The descriptive summary in the above table indicates that, average value of product value is 1.77. Mean of Age is
28.99 whereas its range is 18 to 50. The mean of sex (1.41) indicates that majority of the people are female. Mean of
education level is 3.68. It indicates a high education status in the 180 participants. The average of marital status (1.64)
interprets that many of them are married. The average of Income is $35672.22 while the income range is $16000 to
$74000. The mean value shows that the participants on averages go or plan for travelling for over 3 times per week. The
average weekly covered distance is 103.83 miles/week, while it ranges from 20 to 360. The mean of fitness level (3.29)
indicates that most of the participants are good and very good body shaped.
Figure 1: Bar plot indicates different Fitness levels.
The frequency distribution of different types of shapes among all the 180 participants indicate that it is maximum
for good shaped people (level = 3). Therefore, the null hypothesis of hypothesis1 is rejected and the alternative is
accepted.

DATA ANALYSIS OF KEELS AGENCY 7
Figure 2: Histogram of Distribution of Product Line of excellent shaped people.
The Histogram of frequency distribution of Income indicates that the product line is 1 or 3 in case of those people
who have excellent shape. It is found that only 2 people have product line 1 and the other 29 have product line 3. Mean
of these 31peoples product line is 2.9. We can accept the null hypothesis here of assuming high product level among
excellent shaped people of Hyothesis2. We can reject the alternative hypothesis.
Figure 3: Histogram of Distribution of Income of excellent shaped people.

DATA ANALYSIS OF KEELS AGENCY 8
The Histogram of frequency distribution of income indicates that the frequency level is not normally distributed
for those people who have excellent body shape. In this case, lowest income (25000-3000) has highest frequency (7) and
moderate income (47000-55000) has lowest frequency (2). The frequency distribution is not normally distributed.
Therefore we reject the null hypothesis of hypothesis3 and accept the alternative hypothesis. The mean income of
selected 31 peoples is $49548.
Figure4: Product line vs Income bar graph for excellent body shaped persons.
The product line indicates two product lines: High product line (3) and Low product line (1). The high product
line in the bar chart indicates that most of the higher incomes lie in the figure. But their association could only be
calculated by linear regression model. The chart is also constructed with the data of excellent shaped people.

DATA ANALYSIS OF KEELS AGENCY 9
Figure5: Miles per week cycling excellent shaped persons.
The bar plot of Miles per week cycling factor with level of fitness (only excellent fitness) indicate that most of
these 31 people prefer to travel 200 miles/week by cycling. It indicates that the cycling of on an average of 200 km.
(moderate level) is good for maintaining excellent body shape. We can reject the null hypothesis of assuming excellent
body shape needs high amount of cycling in terms of miles/week in hypothesis4. We can accept the alternative
hypothesis.
.
Table: Correlations
PRODUCT LINE INCOME
PRODUCT LINE Pearson Correlation 1 0.55
Sig. (2-tailed) 0
N 180 180
INCOME Pearson Correlation 0.55 1
Sig. (2-tailed) 0
N 180 180
Table 5: Correlation coefficient between Product line and Income.
The Pearson’ correlation coefficient between Product Line and Income is (0.55) for all the people. It interprets a
moderate link between these two factors. Now we are eager to observe the link or association between these two factors
when only excellent body shaped people are taken into account.

DATA ANALYSIS OF KEELS AGENCY 10
Table: Model Summary (Income and Product Line)
R R Square
Adjusted R
Square Std. Error of the Estimate
0.29 0.08 0.05 15901.97
Table: ANOVA (Income and Product Line)
Sum of
Squares df Mean Square F Sig.
Regression 658367074.5 1 658367074.5 2.6 0.117
Residual 7333310345 29 252872770.5
Total 7991677419 30
Table: Coefficients (Income vs Product Bivariate Regression
Model)
Unstandardized Coefficients Standardized Coefficients 95% Confidence Interval for B
B Std. Error Beta t Sig.
Lower
Bound Upper Bound
(Constant) 22620.69 16931.09 0 1.34 0.192 -12007.28 57248.66
Prod_Line 9379.31 5812.83 0.29 1.61 0.117 -2509.27 21267.89
Table: Coefficient Correlations (Income and Product line)
Income Prod_Line
Income
Pearson
Correlation 1 0.29
Sig. (2-tailed) 0.117
N 31 31
Prod_Line
Pearson
Correlation 0.29 1
Sig. (2-tailed) 0.117
N 31 31
Table2: Linear regression analysis supposing Product line as independent variable (predictor) and income as independent
variable (response) for excellent body shaped people.
The regression analysis was employed in order to empirically identify whether the Product Line was a statistically
important to all other factors or not. The equation is, Y1=β0 +β1* X1 + …..+ β8 * X8 + μ, where Y1 refers to product line,
β0 refers to the constant or the intercept, X1, X2, …., X8 refers to the all other factors as predictors staring from Age to
levels of Fitness, β1, β2….β8 refers to the change of coefficient for the different predictors, while μ refers to the error
term. The regression result in table2 shows the goodness of fit for the regression between the Product line and all other
factors.
As the value of multiple R2 is 0.29, we can tell that there is a week association among Product Line and amount
of income whose body shape is excellent. It also interprets 29% of the variations in the Product Line could be explained
by the variations of income. The Value of adjusted R2 (0.05) indicates a very bad (0 to 0.3 or 0 to -0.3) fitting as per the
rules of goodness of fit.
The significant p-value of product line (0.117) has p-value more than 0.05. Therefore we cannot reject the null
hypothesis of Hypothesis5 of linear association of all factors with Product level at 95% confidence limit. However, the

DATA ANALYSIS OF KEELS AGENCY 11
value of F-statistic (2.6) indicates that income has very week and insignificant association with Product line evaluated as
per the significance of p-values.
The Pearson’s correlation coefficient (0.29) indicates that there is a week correlation between income and product
level of the excellent shaped people. The correlation between these two factors was better for all the persons than
excellent shaped persons.
Conclusion and Interpretation:-
The Cycle World customers’ data of Keels agency incorporates that the interrelationship, link and significance of
association between several factors or variables are more or less high in different levels. We have calculated the
descriptive summary of different attributes of the dataset. Besides, the histograms of income level and Times/week of
cycling also provided in the report. The one-to-one bar plots and histograms are presented in the report.
It is crucial to note that, the report analyzed the KEELS data and tested the hypotheses. It also helped us to draw
necessary interpretations and inferences. The percentage of excellent shaped persons among all is around 18% (31 out of
180). The moderate rate of cycling was found to be fit for excellent body shape. Too much cycling or rare cycling could
hamper the excellent body shape of the people. The income of these selected people is not normally distributed.
Therefore, the effect of excellent shape was not found in income. The value of product line is high in case of excellent
shaped people. The excellent ship is no doubt a reason of product line. We also found a weak linear relationship and
association between product line and income. We can conclude that income does not significantly put its effect on the
product line for excellent shaped people.