...Course Project A – AJ Davis Department Store Keller Graduate School In reviewing the data for AJ Davis Department Store, the below diagrams represents the detailed statistical analysis of the data collected from a sample of 50 credit consumers. The data collected was based on the following five variables: location, income, size, years and credit balances. The first individual variable considered was Location. The three subcategories are Rural, Suburban, and Urban. Shown below is the frequency distribution and pie chart, the maximum number of customer belonging to the Urban category were 42%, followed by the Suburban of 30% and Rural at 28%. Since this is a categorical variable, the measure of central tendency and descriptive statistics was not calculated. Frequency Distribution Location Frequency Rural 14 Suburban 15 Urban 21 The second variable is Credit Balances, displayed in the histogram below in the frequency of how many consumers and their credit balances at department store. Descriptive Statistics: Credit Balances ($) Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3 Credit Balance ($) 50 6 3964 132 933 1864 3109 4090 4748 Variable Maximum ...
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...Maurice S. Butler Math533—Applied Managerial Statistics Course Project: Part A Introduction This project is based upon statistical data compiled concerning AJ Davis Department Stores, specific to a sample of its customer base. It is with intent of establishing relationship between location, gross income, and credit balances carried by customers that the following statistical analysis has been performed. It is assumed that information obtained as well as the interpretation of statistical analysis will enable credible recommendations in regard to future revenues or continued handling and/or maintenance of its receivables. Variables The first variable is the gross income of the stores’ customers. The data set includes 50 customers with gross income ranging from $20,000 to $79,000 per year. Compilation of the data into a frequency/relative frequency table (see below) reveals that the greatest frequency and relative frequency of the store’s customers is found within the $30,000 to $49,000 range. Fifty-two percent of the store’s customer base gross income is found within this range. First and third quartiles have been calculated to be 33 and 57 respectfully. However, no outliers have been identified within the data set. Income ($1000) | Frequency | Relative Frequency | 20-29 | 5 | 10% | 30-39 | 13 | 26% | 40-49 | 13 | 26% | 50-59 | 8 | 16% | 60-69 | 9 | 18% | 70-79 | 2 | 4% | | 50 | 100% | My second variable is the outstanding credit balances of...
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...MATH 533 WEEK 6 COURSE PROJECT PART B To purchase this, Click here http://www.activitymode.com/product/math-533-week-6-course-project-part-b/ Contact us at: SUPPORT@ACTIVITYMODE.COM MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 Week 6 Course Project Part B MATH 533 WEEK 6 COURSE PROJECT PART B To purchase this, Click here http://www.activitymode.com/product/math-533-week-6-course-project-part-b/ ...
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...Keller University Math 533 Prof Ron Deluca Project Part C AJ Davis Inc., Regression Analysis June 18, 2016 Abstract: This is the final project C from Keller University math 533 using AJ Davis company data base provided in our doc sharing student portal. The bold questions are taken from Part C in the Project outline. My answer is underneath each of 14 questions. *Please note the formatting was difficult because Minitab fonts output is different than my desired Arial 12 point font. From AJ Davis database from our class. 1. Generate a scatterplot for income ($1,000) versus credit balance($), including the graph of the best fit line. Interpret. note y=income, x=credit balance Interpretation: The scatter plot of Income Vs Credit balance ($) show that the slope of the ‘best fit’ line is upward (positive); this indicates that Income varies directly with Credit Balance. As Income increases, Credit Balance also increases vice versa. 2. Determine the equation of the best fit line, which describes the relationship between income and credit balance. Y= -3.516 + 0.01193(x) 3. Determine the coefficient of correlation. Interpret. Correlations: Income ($1,000), Credit Balance($) Pearson correlation of Income ($1,000) and Credit Balance($) = 0.801 P-Value = 0.000 The coefficient of correlation is given as r = 0.801. The correlation coefficients between the variables show a positive sign OR direct relationship. The correlation coefficient is far from the P-Value...
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...MATH-533 Applied Managerial Statistics Course Project Part A Introduction It is so interesting to choose sections location, an income and size as representative of my course project. From the list, it really makes me an interest of trend of people who is living in urban, has how many of family members and how much they earn. Basically I would like to categorize the direction of cluster of people and their desires to live which area of country. First, I am going to analyze the locations versus family size, and then family size versus to income. Then I will analyze further how many of family and location has how much income which helps us to reveal to pay back the credits. 1st Individual Variable (Location) Table 1 and graph 1 Location | Number of houses | Rural | 14 | Suburban | 15 | Urban | 21 | From the table 1 and graph 1 shows the number of houses and number of locations. Based on the information customers from AJ Davis tend to live in the Urban area rather than the suburban and rural area. 2nd individual variable (Size) Size (family members) | number of objects | 1 | 5 | 2 | 15 | 3 | 8 | 4 | 9 | 5 | 5 | 6 | 5 | 7 | 3 | Graph 2 and table 2 Based on table 2 information following data comes out. (Numbers of customers) Minimum: 3 Median:5 Q3:8.5 Maximum:15 Based on the graph 2 and table 2, most of the customers intend to have less than 5 family members. The majority of customers have 2 family members and only 3 of them...
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...Natasha Douglas Managerial Statistics Math 533 Project A The following statistical information is based on Data from the AJ Davis Dept. Store, who wanted to find out a little more about their credit customers. The Data is compromised of a sample of 50 credit customers based on five different variables The 1st individual variable is based on location divided in 3 different categories. 1) Rural-which is an area outside of cities and towns. 2) Urban-pertaining to a city or a town and 3) Suburban-a residential district located on the outskirts of a city. According to the graph you have 26% of the population lives in the Rural area, 30% lives in the Suburban area and the largest 44% lives in the Urban. To sum it up the majority of the Credit Customers leaves in the Urban locations. The first pairing is between Size and Income. Based on the Data at hand the graph shows that the size of people’s income is less than their household size. According to the Data people’s households have higher percentages than the money they bring in to support their households. The 2nd Individual pairing shows a graph of credit balances based on the data provided. The Median credit balance is $4,090. The data starts with $1,864 as the lowest credit balance and the highest is $5,678. The range of the Credit balances is $1638.25 In the 2nd pairing based on the data provided. People who have lived in the same location the longest have high credit balances, than the majority of those that...
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...Course Project Part II: Bertram Proprietary Credit Scoring Model Jordan Spence Student ID: D01326483 In partial fulfillment of the requirements for MATH 533 – Applied Managerial Statistics Keller Graduate School of Management Dr. Gerard L. Kiely June 24, 2015 PROJECT PART II: Bertram Proprietary Credit Scoring Model The preliminary analysis carried out in Part I of our project has shown that the data is consistent and reliable, with no missing values. The next step is to construct preliminary and final models. A. Create indicator (dummy) variables for the qualitative variables Own/Rent and Location using Minitab. First, label columns for the new indicator variables: Own, Rent, Urban, Suburban, Rural. Pull down the Calc menu in Minitab and select “Make Indicator Variables”. In the box labeled “Indicator Variables for … “ put the variable for which indicators are desired. Minitab will automatically code the values of Own/Rent and create two new variables named “Own” and “Rent”. Repeat this process for the variable Location. See screenshot below for new indicator variables: [pic] B. Develop a preliminary model and display its output in your paper. Describe its statistical characteristics and state your conclusions. Identify which variables you will keep and those you will drop. Be sure to explain why you made your choices. Be specific. ...
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...New,Project,Keller,Homework,Phoenix,Ashford, ACC 561,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 561,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 565,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 565,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACCT 346,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACCT 346,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACCT 434,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACCT 434,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACCT 567,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACCT 567,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, PHI 200,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, PHI 200,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, Res 301,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, Res 301,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford BA 215,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, BA 215,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller...
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...Descriptive Statistics Project A Baron J Hamilton Jr. D00743033 Keller Graduate School of Management Math 533-65352 Applied Managerial Statistics Introduction Data was collected from a sample of credit customers in the department chain store of AJ Davis using statistical analysis. The analysis below consists of 4 quantitative methods which are income, size, years and credit balance and one qualitative method, location. Descriptive statistics can explain some of the relationships of the data collected. 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 | 42 | 6 | 15 | 4412 | Urban | 21 | 2 | 18 | 2448 | Rural | 44 | 1 | 7 | 2995 | Urban | 37 | 5 | 5 | 4171 | Suburban | 62 | 6 | 13 | 5678 | Urban | 21 | 3 | 16 | 3623 | Suburban | 55 | 7 | 15 | 5301 | Rural | 42 | 2 | 19 | 3020 | Urban | 41 | 7 | 18 | 4828 | Suburban | 54 | 6 | 14 | 5573 | Rural | 30 | 1 | 14 | 2583 | Rural | 48 | 2 | 8 | 3866 | Urban | 34 | 5 | 5 | 3586 | Suburban...
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...MATH 533: Applied Managerial Statistics Course Project –Part A I. Introduction. SALESCALL Inc. is a company with thousands of salespeople. The data provided; SALES (the number of sales made this week), CALLS (the number of sales calls made this week), TIME (the average time per call this week), YEARS (years of experience in the call center) and TYPE (the type of training, either group training, online training of no training). The data is used to determine the most productive sales person. With this information the company can tailor it’s training to achieve the greatest number of sales. II. Individual Variables. 1. Sales Descriptive Statistics: SALES Total Variable Count Mean StDev Variance Minimum Q1 Median Q3 SALES 100 42.340 4.171 17.398 32.000 39.250 42.000 45.000 N for Variable Maximum Range IQR Mode Mode SALES 52.000 20.000 5.750 44 12 Data for sales made in a week for SALESCALL Inc. shows that an average of 42 sales are made. The company can expect to have as few as 32 and up to 52 sales in a week. From the data gathered the company can expect to see the average sales made. Looking at the Histogram above shows sale have a bell shaped curve. 2. Calls Descriptive Statistics: CALLS Total Variable Count Mean StDev Variance Minimum Q1 Median Q3 CALLS 100 162.09 18.01 324.53 ...
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...Course Project Part A Math 533 Keller Graduate School of Management Sept. 13, 2013 The purpose of this report is to provide feedback to AJ Davis Department Store so that they will have a better understanding of the makeup of their customer. This report will utilize three of the five customer variables that were determined by AJ Davis. The variables that were used for this study were: Income, location, credit balance, family size, and years at current household. Using these variables I will show how there may or may not be correlations between some variables and not others. Also, numerical descriptions will be used to show mean income, minimum and maximum credit balances, and family size just to give a few examples. Now that we have established a basis for this report let’s look at the analysis of the data. The first information we are going to discuss is customer income. Here is a statistical description of the income of all 50 of the customers that were sampled for this report. Descriptive Statistics: INCOME ($1000) Variable N Mean StDev Minimum Q1 Median Q3 INCOME($1000) 50 43.48 14.55 21.00 30.00 42.00 55.00 Variable Maximum INCOME($1000) 67.00 The information above shows that the average income of the customers at AJ Davis is $43,480 per year given the 50 customers who were sampled. It also shows us that 25% of the customers that were sampled have an income of #30,000 or less and 75% of their customers...
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...Math 533: Applied Managerial Statistics Course Project Part C: Regression and Correlation Analy Summary After statistical analysis of data collected on a random sample of AJ Davis’ customers, several inferences can be made. The goal of this analysis was to determine the best model for predicting customer income. This knowledge is paramount in most aspects of the company including but not limited to advertising, sales and merchandising. Based on the analysis, it is determined that a model using customer credit balance and household size is the most efficient. These variables gave the most reliable predictions of customer income. At the close of the analysis, the data yielded the following equation for determining customer income, income== - 1.90 + 0.0173 CREDIT BALANCE($) - 5.30 SIZE - 0.390 YEARS. Though, the number of years a customer has live in their home did not show high effectiveness in predicting income, it increased the overall fit of the model and has been included in the equation. We can be 95% confident in the data obtained through the use of this formula. Appendix 1. 2. Regression Analysis: INCOME($1000) versus CREDIT BALANCE($) The regression equation is INCOME($1000) = 4.45 + 0.00987 CREDIT BALANCE($) Predictor Coef SE Coef T P Constant 4.448 7.037 0.63 0.530 CREDIT BALANCE($) 0.009866 0.001728 5.71 0.000 S = 11.3247 R-Sq = 40.5% R-Sq(adj) = 39.2% Analysis of Variance Source...
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...final,Week,1,2,3,4,5,6, ,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 307,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 307,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 344,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 344,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 346,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 346,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 403,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 403,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 504,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 504,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 560,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 560,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 561,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ACC 561,dq,discussion,question,assignment,midterm, ,exam,quiz,Strayer,Latest,New,Project,Keller,Homework,Phoenix,Ashford, ACC 565,Course,Complete,All,Entire,final,Week,1,2,3,4,5,6, ...
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...1 AMERICAN INTERNATIONAL UNIVERSITY-BANGLADESH SUMMER, 2009-2010 SEC A B C TIME 8:0 - 9:30 8:0 - 12:0 9:30 - 11:0 8:0 - 12:0 11:0 - 12:30 8:0 - 12:0 DAY ST M MW T ST S RM 423 CL2 423 CL2 423 CL2 COMPUTER SCIENCE CODE COURSE NAME 00512 INTRO TO PROGRAMMING (BBA) LABORATORY 00513 INTRO TO PROGRAMMING (BBA) LABORATORY 00514 INTRO TO PROGRAMMING (BBA) LABORATORY 00516 00517 00518 00519 00520 00521 00522 00523 00524 00525 00526 00527 00528 00529 00530 00531 00532 00533 00534 00535 INTRO TO PROGRAMMING (BBA) LABORATORY INTRO TO PROGRAMMING (BBA) LABORATORY INTRO TO PROGRAMMING (BBA) LABORATORY INTRO TO PROGRAMMING (BBA) LABORATORY INTRO TO PROGRAMMING (BBA) LABORATORY COMPUTER FUNDAMENTAL (BBA) COMPUTER FUNDAMENTAL (BBA) COMPUTER FUNDAMENTAL (BBA) THEORY OF COMPUTATION THEORY OF COMPUTATION PROGRAMMING LANGUAGE 1(EEE) LABORATORY PROGRAMMING LANGUAGE 1(EEE) LABORATORY PROGRAMMING LANGUAGE 1(EEE) LABORATORY PROGRAMMING LANGUAGE 1(EEE) LABORATORY PROGRAMMING LANGUAGE 1(EEE) LABORATORY PROGRAMMING LANGUAGE 2 (EEE) LABORATORY PROGRAMMING LANGUAGE 2 (EEE) LABORATORY PROGRAMMING LANGUAGE 2 (EEE) LABORATORY PROGRAMMING LANGUAGE 2 (EEE) LABORATORY PROGRAMMING LANGUAGE 2 (EEE) LABORATORY E F G H I A B C A B A B C D E A B C D E 2:30 - 4:0 12:0 - 4:0 4:0 - 5:30 12:0 - 4:0 5:30 - 7:0 12:0 - 4:0 8:0 - 9:30 8:0 - 12:0 9:30 - 11:0 8:0 - 12:0 8:0 - 10:0 10:0 - 12:0 12:0 - 2:0 2:0 - 4:0 4:0 - 6:0 11:0 - 12:30 8:0 - 12:0 12:30 - 2:0 12:0 - 4:0 2:30 - 4:0 12:0 - 4:0 4:0 - 5:30 8:0 -...
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...Desiree Green Laura Sanders Writing 222 12 August 2013 EARLY EDUCATION Abstract Social scientists have posited that education can make a significant and long-lasting difference on the lives of children, especially those who experience poverty (Barnett, 1995; Brooks-Gunn, 2003; Karoly, et al, 1998). In recent years, policymakers and researchers in the United States have re-examined the role that quality early education can play in the lives of young children, primarily those growing up in poverty (Rouse, Brooks-Gunn, & McLanahan, 2005). Specifically, some have argued that high quality education and care programs that begin early in life have the potential to close gaps in school achievement that often exist between poor and minority children and their middle-class, mostly White, counterparts (Magnuson & Waldfogel, 2005).Based on these conclusions, U.S. policymakers and practitioners interested in improving the lives of poor children have considered the possibility that early education programs may contribute to solving the myriad of problems that growing up in poverty poses. This paper will examine the benefits of early education and the arguments against early education and references or in text citation with bibliography. Introduction During early years, children go through critical stages of development, and consistent, high-quality early education can have long-lasting, beneficial effects on the overall development of children. Choosing a preschool in which...
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