MATH 533 Course Project Data AJ DAVIS

The economic value of the existing customers can be calculated by multiplying the customer lifetime value of each segment by the segment’s size and then summing the value of all segments.

In this case, the independent variable is the gender and the dependent variable is the

I understand that very few clients maintain all of their accounts with a single provider; an integrated view of their overall financial picture is critical if clients are to be able to make informed decisions. Financial advisors, too, should be able to access and analyze customer data efficiently. When information is

(21) You took a sample of size 21 from a normal distribution with a known standard deviation, . In order to find a 90% confidence interval for the mean, You need to find.

5. Give the standard deviation for the mean and median column. Compare these and be sure to identify which has the least variability?

Standard Deviation for the mean column is 0.476Standard Deviation for the median column is 0.754Standard deviation for the mean column has least variability

Topics Distribution of the sample mean. Central Limit Theorem. Confidence intervals for a population mean. Confidence intervals for a population proportion. Sample size for a given confidence level and margin of error (proportions). Poll articles. Hypotheses tests for a mean, and differences in means (independent and paired samples). Sample size and power of a test. Type I and Type II errors. You will be given a table of normal probabilities. You may wish to be familiar with the follow formulae and their application.

There are only 25 numbers in the sample collected. For purposes of analysis, it will be considered that the process itself is normally distributed, and its standard deviation is unknown. Under these conditions, the formula that gives the confidence interval is:

A retail store gives each of its customers a maximum amount of credit. A customer’s available credit is determined by subtracting the amount of credit used by the customer from the customer’s maximum amount of credit. As you did in programming challenge 1, perform steps 1 through 6 of the programming process to design an application that determines a customer’s available credit.

The Bandon Group has identified several specific sets of information needs. First they must have the ability to analyze existing customer accounts for profitability. The have a need to understand which account they are making money off of and which account represent high volume profits. Second, they must have an effective sales prospecting system. The ability to measure the

Whereas, poverty shows a positive skewness value of .670 since its variables have numerous high values, which justifies the right skewness of the histogram.

So it is also attractive for Jules Kroll to take this opportunity to enter the credit rating industry. In addition, the “issuer pays” model, which used by the big three rating agencies, lets companies shop around for the best ratings, putting pressure on the agencies to inflate their grades. As a result, it is very difficult to argue that they can adequately represent the users’ side. Therefore, Jules Kroll wants to use another model that can assign unbiased and reliable ratings.

On the other hand, my interest in the incorporation of artificial intelligence and machine learning by financial institutions stems from my work as a Credit Risk Analyst for BECU. I recently joined the institution, and am working on a year-long project to integrate the Synergy Component System which will enable increased automation, improved attribution management, and reduced decision times for credit card and auto loans. Beyond testing plans and cases, I am most excited about designing decision matrices by analyzing member data such as credit quality, debt-to-income ratio, membership years, and trade lines to reach a credit decision. While I have had already experienced academic research, this will be my first taste of applied industry research.

How data mining can assist bankers in enhancing their businesses is illustrated in this example. Records include information such as age, sex, marital status, occupation, number of children, and etc. of the bank?s customers over the years are used in the mining process. First, an algorithm is used to identify characteristics that distinguish customers who took out a particular kind of loan from those who did not. Eventually, it develops ?rules? by which it can identify customers who are likely to be good candidates for such a loan. These rules are then used to identify such customers on the remainder of the database. Next, another algorithm is used to sort the database into cluster or groups of people with many similar attributes, with the hope that these might reveal interesting and unusual patterns. Finally, the patterns revealed by these clusters are then interpreted by the data miners, in collaboration with bank personnel.4

To better understanding the customers, Mr. Selig asked Mrs. Wendy Lamberg, Director of planning, to select a sample of customers and prepare a report. To begin, she has appointed a team of us. Then, we started by selecting a random sample of 60 customers. In