THE CCC CASE:

A credit card company, CCC wanted to understand how its customers use their credit card, i.e. what types of purchase they use the credit card for and how frequent they use the card etc. The company, CCC has randomly selected a small sample from its customer database. The dataset consists of the following fields:

Table 1: Dataset Fields (just a list of fields)

Customer_ID	Total_Installments	Airlines_Count	Airlines_Amount
Gender	Total_Transactions	Appliances_Count	Appliances_Amount
Birth_Date	Total_Amount	Auto_Moto_Count	Auto_Moto_Amount
Ref_Date		Beverages_Smoke_Count	Beverages_Smoke_Amount
Marital_Status		Books_Music_Count	Books_Music_Amount
Occupation_Category		Clothing_Count	Clothing_Amount
		Construction_Count	Construction_Amount
		Direct_Marketing_Count	Direct_Marketing_Amount
		Education_Count	Education_Amount
		Entertainment_Count	Entertainment_Amount
		Financial_Services_Count	Financial_Services_Amount
		Fitness_Count	Fitness_Amount
		Food_Count	Food_Amount
		Gas_Count	Gas_Amount
		Gifts_Accessories_Count	Gifts_Accessories_Amount
		Health_Count	Health_Amount
		Home_Count	Home_Amount
		Hotels_Count	Hotels_Amount
		Industrial_Count	Industrial_Amount
		Jewelry_Count	Jewelry_Amount
		Office_Count	Office_Amount
		Optical_Count	Optical_Amount
		Payments_Count	Payments_Amount
		Rent_Transportation_Count	Rent_Transportation_Amount
		Services_Count	Services_Amount
		Telcos_Count	Telcos_Amount
		Transportation_Count	Transportation_Amount
		Travel_Count	Travel_Amount

The data analyst has used the pivot table option in excel and produced the following summary tables for the Total_Amount field, see table 2a, b, c and d.

Table 2a: Summary for the Sum of the Total_Amount (gender versus marital_status)

	Female	Male	Grand Total
Divorced	505.32		505.32
Married	4354.07	19021.06	23375.13
Single	1074.22	8971.38	10045.6
Widow	151.19		151.19
Grand Total	6084.8	27992.44	34077.24

Table 2b: Summary for the Count of the Total_Amount (gender versus marital_status)

	Female	Male	Grand Total
Divorced	1		1
Married	7	14	21
Single	3	4	7
Widow	1		1
Grand Total	12	18	30

Table 2c: Summary for the Variance of the Total_Amount (gender versus marital_status)

	Female	Male	Grand Total
Divorced
Married	397600.4597	3796370.421	2713535.577
Single	127424.6785	3315939.532	2715405.869
Widow
Grand Total	266016.7452	3631348.173	2502342.468

Table 2d: Possible t-value at 95% confidence level for each customer category

	Female	Male	Grand Total
Divorced
Married	2.446912	2.160369	2.085963447
Single	4.302653	3.182446	2.446911851
Widow
Grand Total	2.200985	2.109816	2.045229642

In addition, the analyst wanted to go deeper into the analysis and wanted to calculate more metrics to understand the customer behavior. The analyst accordingly produced the below table for some selected categories, see table 3.

Table 3: Descriptive Analysis for some selected categories

Descriptive Metric	Total_Amount	Appliances	Clothing	Entertainment	Fitness	Food	Health	Telcos
Sum	34077.24	5984.32	6967.4	589.08	586.38	779.15	624.72	8972.55
Total Variation	72567931.58	7302091.642	5323442.163	335448.0715	43901.89	156393.6	261346.29	29077726.99
Minimum	22.8	0	0	0	0	0	0	0
Maximum	7178.26	2381.21	1848.36	589.08	139.22	329.46	511.38	4831.79
Median	547.31	0	38.94	0	0	0	0	0
t-value at 95% CL	2.045229642	2.045229642	2.045229642	2.045229642	2.04523	2.04523	2.0452296	2.045229642
Benchmark	1250	500	150	60	60	60	50	700
First Quartile	184.9775	0	0	0	0	0	0	0
Third Quartile	1487.4425	42.75	237.6975	0	0	0	0	0

Moreover, the analyst wanted to figure out whether there are some associations among various categories. So, the following tables were produced, see table 4.

Table 4: Linear Regression coefficients table for selected categories as independent variables versus the total_amount as the dependent variable.

Variables	Coefficient	Standard Error	t Stat	p-value
(Intercept)	-5.641	49.926	-0.113	0.91083685
Appliances_Amount	0.91	0.285	3.189	0.00350119
Books_Music_Amount	4.29	1.281	3.35	0.002323
Clothing_Amount	0.993	0.154	6.433	5.7544E-07
Direct_Marketing_Amount	1.007	0.156	6.443	5.6038E-07
Entertainment_Amount	-8.453	5.609	-1.507	0.14301106
Fitness_Amount	3.685	1.263	2.918	0.00687365
Food_Amount	-0.739	0.876	-0.844	0.40582279
Gas_Amount	13.584	7.213	1.883	0.07012785
Gifts_Accessories_Amount	1.316	0.672	1.959	0.06013942
Health_Amount	1.781	2.65	0.672	0.50709119
Home_Amount	0.691	0.217	3.191	0.00348353
Office_Amount	1.522	1.713	0.889	0.38157728
Services_Amount	1.423	0.265	5.371	1.0067E-05
Telcos_Amount	0.958	0.031	30.455	5.2173E-23
Travel_Amount	0.965	0.373	2.591	0.01502837

21

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Based on given values in table 3, answer the following questions:

Based on the given quartile values for the total_amount ($), we can conclude that:

a.

there is at least one outlier in the data

b.there is no outlier in the data

c.there is at most one outlier in the data

d.there is no outlier in the total_amount but there is in the rest of the data

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Based on given values in table 3, answer the following questions:

At the given value of the benchmark for the health_amount ($), we can conclude that:

a.

the health_amount mean ($) is within the benchmark at 95% confidence level

b.the health_amount mean ($) is significantly higher than the benchmark at 95% confidence level

c.the health_amount mean ($) is significantly lower than the benchmark at 95% confidence level

d.the health_amount ($) is within the benchmark at 95% confidence level

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Question 23

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Based on the given values in table 4, answer the following questions:

Comparing the Entertainment, Food, Gas and Health categories, the conclusion is:

a.

Entertainment and Food categories are significantly contributing in the total_amount ($) while the Gas and Health categories, are not significantly contributing in the total amount ($)

b.

Entertainment, Food, Gas and Health categories, are not significantly contributing in the total amount ($)

c.

Entertainment category is significantly contributing in the total_amount ($) while the Food, Gas and Health categories, are not significantly contributing in the total amount ($)

d.

Food category is significantly contributing in the total_amount ($) while the Entertainment, Gas and Health categories, are not significantly contributing in the total amount ($)

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Question 24

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Based on the given values in table 4, answer the following questions:

Comparing appliances, books & music, clothing and telcos categories, the conclusion is:

a.

Appliances category is not significantly contributing in the total_amount ($), while books & music, clothing and telcos categories are significantly contributing in the total amount ($).

b.

Appliances, books & music, clothing and telcos categories are significantly contributing in the total amount ($).

c.

Books & music category is not significantly contributing in the total_amount ($), while , appliances, clothing and telcos categories are significantly contributing in the total amount ($).

d.

Appliances and books & music categories are not significantly contributing in the total_amount ($), while clothing and telcos categories are significantly contributing in the total amount ($).

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Based on the given values in table 4, answer the following questions:

The analyst concluded that the most significant contribution to the total amount comes from

a.Telcos category.

b.Books & Music category.

c.Services category.

d.Gas category.

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Question 26

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Based on the given values in table 4, answer the following questions:

The analyst concluded that:

a.

By increasing the amount of fitness category by one dollar, the total amount ($) will increase by 3.685 dollars.

b.

By increasing the amount of fitness category by one dollar, the total amount ($) will decrease by 3.685 dollars.

c.

By increasing the amount of fitness category by one dollar, the total amount ($) will not be affacted.

d.

By increasing the amount of fitness category by one dollar, the total amount ($) will increase by 3.685 dollars in addition to $5.641.

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Based on the given values in table 4, answer the following questions:

The analyst concluded that:

a.By increasing the amount of gas category by one dollar, the total amount will change by an insignificant amount of $13.584.

b.By increasing the amount of gas category by one dollar, the total amount will decrease by 13.584 dollars.

c.By increasing the amount of gas category by one dollar, the total amount will increase by 13.584 dollars.

d.By increasing the amount of gas category by one dollar, the total amount will increase by 13.584 dollars in addition to $ 5.641.