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Words 727

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Words 727

Pages 3

Project Part A

January 18, 2013

Overview of Analysis 3 Individual Variable Analysis 4 Relationship between variables 8 Conclusion 10 Bibliography 11

Overview of Analysis

AJ Davis is a department store chain, which has many credit customers and wants to find out more information about these customers. The department store provided a sample of 50 customers.

The following data was provided for analysis: 1. Location a. Rural b. Urban c. Suburban 2. Income (measured in $1000’s) 3. Household size 4. Years in current location 5. Store credit balance

Individual Variable Analysis

Five individual variables were provided for analysis: Location, Income, Household size, Years at location and Credit balance. Below is a summary of the key points on the following individual variables: Location, Size and Credit Balance.

Variable: Location

The first individual variable summary presented is Location. The variable location has 2 subcategories from which data was reported: Rural, Urban and Suburban. As this is qualitative data only no measures of central tendency or descriptive statistics will be given. Based on the frequency distribution and the pie chart, the maximum number of customers from the sample live in Urban locations, with this variable representing 44% of the sample. Customers in the represented in the Rural areas is 26% and customers in the Suburban area represents 30% of the sampled customers. Frequency | Distribution | Location | Frequency | Urban | 22 | Rural | 13 | Suburban | 15 |

Pie Chart

Variable: Household Size

The second individual variable summary presented is Household size. The mean household size is 4.5 persons per household. The median is 4.5 and the data is bimodal with 1 and 8 being the data sets…...

...The following report presents the detailed statistical analysis of the data collected from a sample of credit customers in the department chain store AJ DAVIS. The 1st individual variable considered is Location. It is a categorical variable. The three subcategories are Urban, Suburban and Rural. Since this is a categorical variable, the measures of central tendency and descriptive statistics has not been calculated for this variable. The frequency distribution and pie chart are given as follows: |Frequency Distribution: | |Location |Frequency | |Urban |21 | |Suburban |15 | |Rural |14 | [pic] From the frequency distribution and pie chart, it is evident that the maximum number of customers belongs to the rural category (42%), followed by those in the suburban category (30%). 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 and are given as follows: |Descriptive Statistics: | |Size | |Mean |3.42 | |Standard Error |0.24593014 | |Median |3 ...

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...AJ Davis Department Store Introduction The following information will show whether or not the manager’s speculations are correct. He wants to know the following information: Is the average mean less than $50,000? Does the true population proportion of customers who live in an urban area exceed 40%? Is the average number of years lived in the current home less than 13 years? Is the credit balance for suburban customers more than $4300? Hypothesis testing and confidence intervals for situations A-D are calculated. A. THE AVERAGE (MEAN) ANNUAL INCOME WAS LESS THAN $50,000. Solution: Step 1: Null Hypothesis: The average (mean) annual income was equal to $50,000. H_0: μ=50,000 Step2: Alternate Hypothesis: The average (mean) annual was less than $50,000. H_a: μ 50 , a z-test for the mean will be used to test the given hypothesis. As for the alternative hypothesis, which is Ha:μ0.40 and the given test is a one-tailed (upper-tailed) z-test. Step 4: Critical Value and Rejection Region: The critical value for significance level is ∝=0.05. The upper tail z-test is 1.645. Rejection Region: Reject H_0,if z-statistic>1.645. Step 5: Assumptions: The sample size in this experiment is n 0.4 95% Lower Sample X N Sample p Bound Z-Value P-Value 1 21 50 0.420000 0.305190 0.29 0.386 Step 7:......

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...DeVry University Miami Campus Math 533, Managerial Statistics Student: Elias Hane Professor: Norman Ebsary Course Project: AJ DAVIS DEPARTMENT STORES Date: 24th of January, 2013 Introduction AJ DAVIS is a department store chain, which has many credit customers and wants to find out more information about these customers. A sample of 50 credit customers is selected with data collected on the following variables: 1. LOCATION (Rural, Urban, Suburban) 2. INCOME (in $1,000’s) 3. SIZE (Household Size, number of people living in the household) 4. YEARS (number of years that the customer has lived in the current location) 5. CREDIT BALANCE (the customers current credit card balance on the store's credit card, in $) LOCATION // INCOME ($1000) // SIZE // YEARS // CREDIT BALANCE ($) Urban | 54 | 3 | 12 | 4016 | Rural | 30 | 2 | 12 | 3159 | Suburban | 32 | 4 | 17 | 5100 | Suburban | 50 | 5 | 14 | 4742 | Rural | 31 | 2 | 4 | 1864 | Urban | 55 | 2 | 9 | 4070 | Rural | 37 | 1 | 20 | 2731 | Urban | 40 | 2 | 7 | 3348 | Suburban | 66 | 4 | 10 | 4764 | Urban | 51 | 3 | 16 | 4110 | Urban | 25 | 3 | 11 | 4208 | Urban | 48 | 4 | 16 | 4219 | Rural | 27 | 1 | 19 | 2477 | Rural | 33 | 2 | 12 | 2514 | Urban | 65 | 3 | 12 | 4214 | Suburban | 63 | 4 | 13 | 4965 | Urban | 55 | 6 | 15 | 4412 | Urban | 21 | 2 | 18 | 2448 | Rural | 44 | 1 | 7 | 2995 | Urban | 37 | 5 | 5 | 4171 | Suburban | 62 | 6 | 13 | 5678 | Urban |......

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...Introduction AJ Davis, a large department store chain, has enlisted my company to help them find out more about their customers who shop using credit. A sample of 50 credit customers were selected based on five variables which included location, income, size, years, and credit balance. Location (Rural, Urban, Suburban) Income (in $1,000s) Size (Household Size) Years (Number of Years That the Customer Has Lived in the Current Location) Credit Balance (The Customers Current Credit Card Balance on the Store’s Credit Card) We will take a look at three different variables at this time. Location The location variable has three subcategories which include rural, urban, and suburban. This variable is looking at where customers live. A pie chart is a circular graph which divides information into sections based on numerical proportions. Frequency distribution tables allow us to look at variables and their frequencies (or how many times the variables occur). Frequency Distribution Location Frequency (# of Customers) Urban 21 Suburban 15 Rural 14 Interpretation: Based on the information shown in both the pie chart and the frequency distribution chart, we can see that more of the customers (21/50 = 42%) are from urban areas. Suburban areas are next with (15/50) 30% of the customers and rural areas have the least amount of customers with (14/50) 28%. Credit Balance The credit balance is the amount of funds that are currently charged......

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...Individual Variable: Income 3.0 Individual Variable: Size 4.0 Individual Variable: Credit Balance($) 5.0 Pairing of Variables: Income($1000) vs. Years 6.0 Pairing of Variables: Credit Balance($) vs. Income($1000) 7.0 Pairing of Variables: Credit Balance($) vs. Location 8.0 Conclusion 9.0 References List 1.0 Introduction This report is done base on a sample of 50 credit customers with AJ DAVIS is selected with data collected base on five variables as following: • Location (Rural, Urban, Suburban) • Income (in $1,000's) • Size (Household Size, meaning number of people living in the household) • Years (the number of years that the customer has lived in the current location) • Credit Balance (the customers current credit card balance on the store's credit card, in $). To have more understand of what the data truly mean, three of individual variable and three of pairing variable are analyzed by using numerical techniques of summarization as and graphical such as stem-leaf diagram, histogram, boxplot, and bar chart on this report. Since a bar graph is useful for comparing facts and help us to......

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...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.......

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...533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis......

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...533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://devrycourse.com/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis......

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...Statistical Information Paper HCS 438 July 17, 2011 Monica Vargas Statistical Information Paper Statistics are used in many different ways in my workplace. The use of statistics is for the improvement of quality care and safety. Statistics are also used to measure employee compliance in regards to hand washing and proper use of policies and procedures. We also use charts and graphs to show infection rates, skin integrity, falls within the facility, budget concerns, and many more. These graphs help hospital personal improve care and safety to provide quality care to all patients. Graphs can also be used to measure patient and employee satisfaction. Descriptive statistics are used to describe the basic features of the data in a study and do not involve generalizing the data that has been collected. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data (Trochim, 2006). An example of descriptive statistics used at my workplace can be the number of patients that are admitted into the hospital on a Monday versus a patient admitted on any other day of the week. This information can also be broken down into more descriptive categories such as how many patient were men, women , children, what is their diagnosis, why were they admitted, and so on and so forth. We use inferential statistics to make judgments of the probability that an observed......

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...Project: AJ DAVIS DEPARTMENT STORES Purchase here http://chosecourses.com/MATH%20533/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://chosecourses.com/MATH%20533/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://chosecourses.com/MATH%20533/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://chosecourses.com/MATH%20533/math-533-course-project-aj-davis-department-stores Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data......

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...References Agresti, Alan (1996).Introduction to categorical data analysis. NY: John Wiley and Sons. Ahmed, N. (1976); Development Agriculture of Bangladesh. Dhaka, Bangladesh Books of International Ltd. BANBEIS (1998); Bangladesh Education Statistics (At a glance), Bangladesh Bureau of Statistics. BBS, 2003: Report of the household expenditure survey 2000, Statistical Division, Ministry of Planning, Government of Bangladesh, Dhaka. BBS (2002); Statistical Year book of Bangladesh, 2001, Dhaka. Bunce, L. and R. Pomeroy. 2003. Socioeconomic Monitoring Guidelines for Coastal Managers in the Caribbean: SocMon Caribbean. GCRMN. Bunce, L., P. Townsley, R. Pomeroy, and R. Caribbean. GCRMN. Bunce, L., P. Townsley, R. Pomeroy, and R. Pollnac. 2000. Chandra KJ. Fish parasitological studies in Bangladesh: A Review. J Agric Rural Dev. 2006; 4: 9-18. Cochran, W.G. (1977); Sampling Techniques, 3rded. New Delhi: Wiley Eastern. Davis, James A. (1971). Elementary survey analysis. Des Raj (1971); Sampling Theory. Fox, J. 1984: Linear Statistical Models and Ravallion, M. and B. Sen 1996: When Method Matters: Monitoring Poverty in Bangladesh, Economic Development and Cultural Change, 44: 761-792 Gujarati, Damodar.N; Basic Econometrics. 4TH edition; Mcgraw-Hill. Gupta, S.C., Kapoor; Fundamental of Mathematical Statistics. New Delhi. Heyman, W. and R. Graham (eds.). 2000. The voice of the fishermen of Southern Belize, Toledo Institute for......

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...MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase......

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...MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase here http://sellfy.com/p/hIox/ Product Description MATH 533 Week 2 Course Project Part A - Exploratory Data Analysis MATH 533 Week 6 Course Project Part B - Hypothesis Testing and Confidence Intervals MATH 533 Week 7 Course Project Part C - Regression and Correlation Analysis MATH 533 Course Project: AJ DAVIS DEPARTMENT STORES Purchase......

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...Professor Vaccaro MBAMS 630 – STATISTICAL ANALYSIS FOR MANAGERS SUMMER SESSION - I , 5th EXAMINATION ( Chapter 13 ) – Part I NAME ___Due Thursday, July 14th, 2016 MULTIPLE CHOICE : ( select the most correct response ) 1. The Y-intercept ( bo ) represents the: a. estimated average Y when X = 0. b. change in estimated average Y per unit change in X. c. predicted value of Y. d. variation around the sample regression line. 2. The slope ( b1 ) represents: a. predicted value of Y when X = 0. b. the estimated average change in Y per unit change in X. c. the predicted value of Y. d. variation around the line of regression. 3. The least squares method minimizes which of the following? a. SSR b. SSE c. SST d. all of the above A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of its product. To do this, the firm randomly chooses six (6) small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will construct a simple linear regression on the data below: City Price ($) Sales River Falls 1.30 100 Hudson 1.60 90 ...

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