First chart up is the variable location. Three categories are listed in the charts. Frequency Distribution:
LocationFrequency
Urban21
Suburban15
Rural14

As the Pie Chart above shows the majority of the customers comes from the rual areas totaling 42%

The Second will be the size chart. This will measure tendency, variation, mean, median and mode. Descriptive Statistics:
Size
Mean3.42
Standard Error0.24593014
Median3
Mode2
Standard Deviation1.73898868
Sample Variance3.02408163
Kurtosis-0.7228086
Skewness0.52789598
Range6
Minimum1
Maximum7
Sum171
Count50

Frequency Distribution:
SizeFrequency
15
215
38
49
55
65
73

The mean household size of the customers is given as 3.42. The median of the data is 3 and the mode is 2. The standard deviation is given approximately as 1.74. Maximum number of customers has a household size of 2 as is evident from the frequency distribution and the bar graph.

The Third chart is over credit Balance.

Descriptive Statistics:
Credit Balance($)
Mean3964.06
Standard Error132.0159991
Median4090
Mode3890
Standard Deviation933.4940816
Sample Variance871411.2004
Kurtosis-0.741830067
Skewness-0.129506489
Range3814
Minimum1864
Maximum5678
Sum198203
Count50

The mean credit balance of the customers is given as $3964.06. The standard deviation is 933.49. The credit balance of the customers is more of the bell shaped distribution lying in the range $4000 - $4500. This is where your most customers with a credit balance will lie. At the top of the peak.

The relationship between the variables Income and Size
There is no definite relationship or association between the two...

...The below report presents the detailed statistical analysis of the data collected from a sample of credit customers in the department store “AJ DAVIS Departmental stores”.
The 1st individual variable considered is Location. It is a category variable. The three subcategories are Urban, Suburban and Rural. Category variable, the measures of central tendency and descriptive statistics has not been calculated for this variable. The frequency distribution and pie chart are below:
|Frequency Distribution: |
|Location |Frequency |
|Urban |21 |
|Suburban |15 |
|Rural |14 |
[pic]
From the frequency distribution and pie chart, it is display the maximum number of customers belongs to the rural category (42%), suburban category (30%) and Only 28% of the customers belong to the urban category.
The 2nd individual variable considered is Size. It is a quantitative variable. The measures of central tendency, variation and other descriptive statistics have been calculated for this variable are below:
|Descriptive Statistics: |
|Size |
|Mean |3.42 |
|Median |3 |
|Mode |2 |
|Standard Deviation...

...
Course ProjectPart Three
Professor Douglas Nottingham
March 27, 2014
1. Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line. Interpret.
The larger the size of the family the larger the credit balances is for the family. The larger families have the financial needs to have a larger credit balance.
2. Determine the equation of the "best fit" line, which describes the relationship between CREDIT BALANCE and SIZE.
Credit Balance ($) = 2591 + 403.2 Size
3. Determine the coefficient of correlation. Interpret.
The square root of R-Squared = .566 equals R; R = .75
4. Determine the coefficient of determination. Interpret.
The R-Squared is .566. The R-Squared is stating that 56.6% of the data is correct which indicates that the percentage of the total sample variation of the credit balance value is accounted for by the model.
5. Test the utility of this regression model (use a two tail test with α =.05). Interpret your results, including the p-value.
Regression Analysis: Credit Balance ($) versus Size
The regression equation is
Credit Balance ($) = 2591 + 403 Size
Predictor Coef SE Coef T P
Constant 2591.4 195.1 13.29 0.000
Size 403.22 50.95 7.91 0.000
The p-value is 0.000 and therefore less than the α=.05 and we reject the Ho because there was not enough evidence too.
6. Based on your findings in 1-5, what is...

...Gm533 Final Paper
Executive Summary
An analysis was performed for Quick Stab Collection Agency. This agency specializes on relatively small accounts and avoids risky collections such as debtor that tends to be chronically late with payments or is known to be hostile. The collection business can be very profitable. Quick Stab Collection Agency has been known to purchase small accounts for $10.00 to collect a debt of $60.00.
The profitable of this agency depend critically on the numbers of days to collect the debt, the size of the bill and the discount rate offer. QSCA has asked us to find a relationship between the size of the bill and the days collected if any. In this data set there’s variable DAYS is the number to collect the payment, and also the BILL would be the amount of the overdue bills. The data will define that the TYPE=1 for residential accounts and the 0 for commercial accounts. A 95% confidence level was chosen.
Introduction
The strategy for QSCA Company it depends on how fast they can collect the debt and the amount of money received above what they paid for the account of course their intend is to get all of the amount but they realize that that may not those offering discounts in order to get payment. We will randomly select accounts from January to June that have been designated has overdue in the data set and try to establish a relationship between the size of the bill and the numbers of days to collect. In order to give...

...Math 533ProjectPart B
In regards to the dataset from AJ Department store, your manager has speculated the following:
the average (mean) annual income is less than $50,000,
the true population proportion of customers who live in an urban area exceeds 40%,
the average (mean) number of years lived in the current home is less than 13 years,
the average (mean) credit balance for suburban customers is more than $4300.
Part 1. Using the sample data, perform the hypothesis test for each of the above situations in order to see if there is evidence to support your manager’s belief in each case a.-d. In each case use the Seven Elements of a Test of Hypothesis, in Section 6.2 of your text book with α = .05, and explain your conclusion in simple terms. Also be sure to compute the p-value and interpret.
Our textbook tells us the following are the elements used to test a hypothesis:
Elements of a Test of Hypothesis
1. Null hypothesis (H0): A theory about the specific values of one or more population parameters. The theory generally represents the status quo, which we adopt until it is proven false. The theory is always stated as H0: parameter = value.
2. Alternative (research) hypothesis (Ha): A theory that contradicts the null hypothesis. The theory generally represents that which we will adopt only when sufficient evidence exists to establish its truth.
3. Test statistic: A...

...
PROJECTPART B: Hypothesis Testing and Confidence Intervals
Math-533 Applied Managerial Statistics
Prof. Jeffrey Frakes
December 8, 2014
Jared D Stock
A.) The average (mean) annual income was greater than $45,000
Null Hypothesis: The average (mean) annual income is greater than or equal to $45,000.
Ho: u > $45,000
Alternative Hypothesis: The average (mean) annual income was less than $45,000
Ha: u < $45,000
I will use a = .05 as the significance level, and observing the sample size of n < 30 which tells me I need to use a Z-Test to find the mean of this test and the hypothesis. As the alternative hypothesis is Ha: u < $45,000, the given test is a one-tailed Z-Test.
The critical value for the significance level, α=0.05 for a one-tailed z-test is -1.645.
Decision Rule: We must reject the hypothesis if z >1.645
Test Statistics from Minitab:
One-Sample Z: Income ($1000)
Test of mean = 45, < 45
The proposed standard deviation = 14.64
95% Upper
Variable N Mean StDev SE Mean Bound Z P
Income ($1000) 50 43.48 14.55 2.06 46.86 0.49 0.311
Confidence Intervals from MiniTab:
One-Sample Z
The assumed standard deviation = 14.64
N Mean SE Mean 95% CI
50 42.61 2.06 (39.45, 47.51)...

...I. Statement of Goal/Problem Statement/Research Question
There are several perceptions about the causes of property crime in the United States. Many believe that the degree of property crime is determined by various factors including per capita income for each state, percentage of public aid recipients, high school dropout rates and many more. This project seeks to provide evidence for or against some of these common perceptions about property crime. Specifically it seeks to answer the questions: are crime rates higher in urban than rural areas? Does unemployment or education level contribute to property crime rates? Other independent variables that will be studied include public aid for families with children, population density, and average precipitation in the major city in each state.
II. Description of Sample
Data for this study was obtained from the course textbook which stated the sources as being for all the 50 states of the US (i.e. sample size of 50) and were gathered from various sources, including a variety of US government sources, among which are: the 1988 Uniform Crime Reports, Federal Bureau of Investigation, Office Research and Statistics, Social Security Administration, The Commerce Department and other government sources. The variables analyzed are as follows:
CRIMES - Property crime rate per hundred thousand inhabitants (includes burglary, larceny, theft, and motor vehicle theft) calculated as number of property crimes committed...

...A. Brief Introduction
There are 50 credit customers who were selected for the data collection on five variables such as location, income, size, years, and credit balance. In order to understand more about their customer, AJ DAVIS must use graphical, numerical summary to be able to interpret and better expand their business in the future.
B. Discuss your 1st individual variable, using graphical, numerical summary and interpretation
A histogram shows the distribution of data within the Income. In this Histogram graph of Income, it shows that the graph is not symmetrical. This histogram graph has a wider bell shape form. The graph shows that this graph is more like two graph because there is a clear difference between income generating from 20-40 and from 50-above. There are two separated cluster; therefore, the skewness of this graph is skewed right. Income has a lower value of kurtosis which indicates a lower, less distinct peak. The following table shows the numerical summary of Income:
Total
Variable Count N N* CumN Percent CumPct Mean SE Mean TrMean StDev
Income ($1,000) 50 50 0 50 100 100 46.02 1.96 45.70 13.88
Sum of
Variable Variance CoefVar Sum Squares Minimum Q1 Median Q3
Income ($1,000) 192.75 30.17 2301.00 115337.00 25.00 33.00 44.50 57.25
N for...

...
Course Project: AJ Davis Department Stores
Natasha Unaphum
MATH533: Applied Managerial Statistics
September 10th, 2014
Professor Rolston
Introduction:
AJ Davis is a department store chain, they are trying to get to know more about their clientele and to further expand their business. A sample of 50 credit customers are selected for this research, information that includes, location (rural, urban or suburban), Income (in $1,000), size (household size), years (number of years lived in that location), and credit balance (customers current credit card balance on the store’s credit card).
Discuss your 1st variable, using graphical, numerical summary and interpretation
Numerical Summary of Credit Balance are as follows:
Mean: 3970.5 Minimum: 1864
Standard Deviation: 931.9 Q1: 3109.3
Variance: 868429.8 Median: 4090
Skew: -0.15043 Q3: 4747.5
N: 50 Max: 5678
The histogram above shows the Credit Balance variable of the 50 customers surveyed. The histogram is almost symmetrical with one outlier which is the credit balance of $2,000. While it being symmetrical you can almost fold the y-axis in half to have it look the same. While observing the histogram, its skewed to the left because of the outlier, and the skew is -.015043. Using the Anderson-Darling Normality Test, the P-value for Credit Balance is 0.400, and A^2 is 0.38. Throughout the mean, median, and...