...PROJECT PART A Exploratory Data Analysis Keller Graduate School of Management GM533: Managerial Statistics (Downers Grove, IL) Table of Contents I. Introduction & Overview .................................................................................................... 3 II. Individual Variables............................................................................................................. 4 Variable: Location................................................................................................................ 4 Variable: Income.................................................................................................................. 4 Variable: Credit Balance .................................................................................................... 5 III. Relationships.......................................................................................................................... 6 Location and Credit Balance................................................................................................ 7 Credit Balance and Household Size .................................................................................... 8 Income and Credit Balance .................................................................................................. 8 References............................................................................................................................... 9 I. Introduction...
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...What elements should be considered when buying a home? Does the age of the home make a difference in the price? What about the square feet of the home? Does having a house with more square feet mean that the price of the home will increase? Well my fiancé and I are looking to buy a home and have come to the conclusion of five factors that we think are the most important. Using statistics, we will narrow our search down from 108 homes to only a few homes. The first thing, we need to discuss is the dependent and independent variables. Since we are most concerned with the price of the home and how other factors affect it, we will use PRICE as the dependent variable. We have other factors that influence the price such as square feet (home size), number of bedrooms, number of bathrooms, heat (gas forced or electric), style (ranch, two floor, or tri-level), garage, basement, fireplace, age of the home, and the school district which will be our independent variables. Out of these, we have decided to pay close attention to the square feet (home size of 1900 square feet or more), number of bathrooms (3 or more), heat (gas forced), basement, and the age of the home (less than or equal to 10 years). We chose these factors because we wanted to know what kind of relationship, if any, they have with the price of the home. To figure that out, we will be doing a series of tests to discuss the price difference for the independent variables. Then we will get the probability and confidence...
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...------------------------------------------------- CASE 7: The Forgotten Group Member Part I: Group Development The formation of a group consists of five important stages. The stages are adjourning, forming, storming, performing, and norming. These stages are critical because it creates stability and union ship among people to form teamwork. In the case study the group dealt with inconsistency and social lofting from a few of the group members. Group member Diane was “quiet and never volunteered suggestions, but when directly asked, she would come up with high-quality ideas.” She doesn’t have the determination to speak up voluntarily and doesn’t have input unless asked too. She derails the teamwork dynamics. In addition, Mike completely has no regard for being in a team. He is a perfect example of a social loffer who “miss most meetings and would send in brief notes.” He expects too much from his group and doesn’t take his group in consideration by making excuses for himself for missing meetings or not be able to attend. I believe the group is in the storming stage. There is so much confusion and stress that not all members are working together. Mike feels rejected by his group and Christine feels that Mike doesn’t want to be part of the group. If Christine understood the dynamics of the 5 group stages she would have an easier time managing the group. Since the beginning there wasn’t any adjourning or forming phase where all the members could get to know each other and their...
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...In: Business and Management Applied Managerial Statistics 12 Course Project AJ DAVIS DEPARTMENT STORES PROJECT PART A In this course project, my aim is to present the statistical analysis of the data for Aj Davis departmental store chain, which has many credit customers and wants to find out more information about these customers. In analyzing the individual variable, using graphical illustrations I would be using histogram, bar chart and a pie chart because there are useful when using numerical comparison. The 3 individual variables 1) The 1st individual variable, I choose is the credit balance of the customers. Credit Balance Numerical Summary: | Credit Balance($) | Mean | 3970 | Median | 4090 | Mode | 3890 | Standard Deviation | 932 | Skewness | -0.15 | Range | 3814 | Minimum | 1864 | Sum | 198523 | Maximum | 5678 | Interpretation The mean credit balance of the customers is given as $3970. The standard deviation is given approximately as 932. The credit balance of the customers is more or less normally distributed with the peak of the bell shaped distribution lying in the range 3814. 2) The 2nd individual variable, I choose is the household size. Thus the number of people living in the households. SIZE Frequency Distribution | Size | Frequency | 1 | 5 | 2 | 15 | 3 | 8 | 4 | 9 | 5 | 5 | 6 | 5 | 7 | 3 | Interpretation The mean of the household size is known as 3 approximately as...
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...EXECUTIVE SUMMARY During the 1980s there was rash of property crimes in the United States. Business executives and community leaders was taken at back. They chose to look for and find these determinants of property crimes and figure out how to remedy those issues. The task at hand was to provide evidence for or against common perceptions about property crime. Prove if crime rates are higher in urban areas or rural areas. Whether unemployment or education levels contributed to property crime rates, as well as public assistance? Lastly establish what other possible factors relate to property crimes? PCRIMES data was used to prepare a report on the characteristics of and determinants of property crimes in the United States. PCRIMES Property crime rate per hundred thousand inhabitants (property crimes include burglary, larceny, theft and motor vehicle theft); calculated as number of property crimes committed divided by total population/100,000 PINCOME Per capita income for each state DROPOUT High School Dropout rate (%, 1987) PRECIP Average precipitation in inches in the major city in each state over 1951- 1980 PUBAID Percentage of public aid recipients (1987) DENSITY Population/total square miles KIDS Public aid or families with children, dollars per family UNEMPLOY Percentage of unemployed workers URBAN Percentage of the residents living in urban areas STATE 50 States of America The task required that it show the association between crime and the different...
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...Multiple Regression Model | Case # 28 House Prices | | A group of statistic student’s objective is to provide a business solution using statistical calculations and tools on a sample data. | | Upaiwan Porndumrongkit Ana Sanchez George Satiah Kritchapon Sopawatjirarich | 10/16/2010 | Executive Summary Summary: Home owners want to determine a reasonable asking price of a house based on a collection of home descriptions and its characteristics. However, home owners can get confused very easily as they see close variation in price based on different descriptive characteristics. Home owners hesitate to get professional advice from real estate agents due to service price. On the other hand, our Multi Regression Model can help real estate agents provide fast predictions and advice to home owners at a minimum service price. Our solution would give real estate agents a competitive advantage in the real estate market. Problem statement: How to determine a reasonable asking price on n number of houses with its descriptive characteristics. Solution: Multi Regression Model is one way to assist real estate agents use data to provide an estimate. Introduction The objective of this project is to provide the detailed data analysis and resolution to the problem statement mentioned above. As a group...
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...Lab #1 Solutions Mathew Arndt Desktop Computer Sales at a Computer Store for One Week Sale # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 Price 1201 1064 1174 1086 1197 1065 1133 1075 1051 1190 1183 1119 1128 1155 1229 1228 1047 1113 1222 1159 1079 1216 1115 1049 1119 1160 1172 1193 1105 1241 1141 1183 1148 1078 1180 1194 1192 1093 1102 1030 1184 1245 1076 Store South North South Downtown Downtown Downtown North Downtown Downtown South South Downtown North North South North South North North Downtown Downtown South South Downtown North North North North North North South North South South North North Downtown North South South South Downtown Downtown Sell Time 20 20 35 30 30 35 35 30 35 40 30 45 20 15 30 20 25 35 35 30 25 15 30 25 30 20 35 20 35 40 30 20 30 20 25 35 15 40 10 25 30 25 30 Manufacturer Compaq Compaq Gateway Acer Hewlet Packard Gateway Gateway Gateway Compaq Hewlet Packard Compaq Gateway Compaq Compaq Hewlet Packard Compaq Gateway Compaq Hewlet Packard Compaq Hewlet Packard Compaq Gateway Acer Hewlet Packard Compaq Compaq Compaq Acer Gateway Gateway Acer Compaq Gateway Gateway Gateway Gateway Compaq Compaq Acer Compaq Gateway Acer Salesperson Ann Gerardo Ann Simon Lucinda Simon Gerardo Isaac Lucinda Dominick Dominick Isaac Kristof Gerardo Dominick Kristof Ralph Blake Blake James Isaac Dominick Ralph Simon Gerardo Kristof Gerardo Norm Gerardo Gerardo Ralph Blake Ralph Ralph...
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...Executive Summary This paper will use both simple and multiple linear regression techniques to show the relationship between the amount of a bill and the number of days it takes to collect for both commercial and residential accounts for Quick Stab Collection Agency. It will examine if the size of the bill impacts the time it take to collect, analyze the differences between procuring delinquent residential and commercial bills and recommend strategic actions that may be taken to maximize Quick Stab Collection Agency’s return on investment. Introduction In order to remain profitable Quick Stab Collection Agency, (QSCA) must prove that the size of the bill and the type of account, whether commercial or residential, is directly related to the amount of time it takes to collect the debt. Determining the correlation between the type of bill, the dollar amount and the number of days it will take to collect is essential in the analysis. The end goal of this examination is to enable QSCA to better predict how long it will take to collect payment. Predicting how long it should take to collect on an outstanding bill will enable QSCA to develop a strategic plan to maximize their collection processes and return on investment. To validate the relationship between the amount of a bill and the number of days it takes to collect for both commercial and residential accounts, we utilized a multiple linear regression method to generate an accurate statistical analysis of the data. By using...
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...Analyzing Property Crimes in the United States GM 533 Applied Managerial Statistics April 15, 2010 To: Mr. Livingston Date: April 15, 2010 Subject: Analysis of Property Crimes Per your request, we have analyzed the content of Case #49: Property Crimes. There are many preconceived ideas about Property Crimes. Property Crimes do not involve force, but the taking of property or money and is considered to some to be a “high-volume” crime. Property crime would include the taking of jewelry, money, electronics, motor vehicles, cash, and other high priced items. We were given the task of analyzing the data and answering the following questions: 1. Are crime rates higher in urban than rural areas? 2. Does unemployment or education level contribute to property crime rates? 3. Does public assistance contribute to property crime rates? The results are as follows: Data: We are allowed access to a data set containing the following information: 1. Crimes: Property crime rate per hundred thousand inhabitants. The crimes include burglary, larceny, theft, and motor vehicle theft. The calculations for the crimes are noted as the number of property crimes divided by the total population (100,000). 2. P-income: P-income is the per capita income for each state (All 50 states are included in this data set). 3. Dropout: This shows the high school dropout rate. 4. Precipitation: This shows the average precipitation in inches in the major city in each state. 5. Public Aid: This...
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...Final Exam Study Guide (Revised 5/17/2011) In preparing for the Final Exam, it will serve you well to take a step back and reflect on the content, structure, and flow of the course. This will enable you to organize your notes, your completed homework and case problems, your annotated Minitab output, and your thoughts. The course is organized around the Terminal Course Objectives (TCOs) and each week builds on the preceding week’s concepts and skills. Each week introduces, explains, and demonstrates the essential concepts, major problem types, and key variations, which embody the TCOs. Here is a week-by-week summary of the major problem types. * Week 1 - Descriptive Statistics: includes central tendency, dispersion, and the shape of the distribution, in numbers, pictures, and tables. * Week 2 – Probability: includes 3 major problem types, and their most important variations: contingency tables, expected value, and the binomial distribution. * Week 3 – Probability continued: includes the normal distribution, its application to sampling distributions, and its most important variations. * Week 4 – Confidence intervals and sample size determinations, and their most important variations. * Week 5 – Hypothesis testing: includes the 5-step hypothesis testing procedure, applied to means and proportions, and its most important variations. * Week 6 – Simple linear regression: includes interpreting Minitab output for point estimates, hypothesis tests, and confidence...
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...* * * * * * Course Project GM533 Applied Managerial Statistics Professor Cook February 14, 2011 TO: FROM: DATE: February 14, 2011 SUBJECT: Analysis of Factors that Affect Property Crime Rates I have analyzed the data of the factors that affect property crime rates in the United States. Here are my results. Data The data provided included; crime rate per hundred thousand inhabitants (which included burglary, larceny, theft, and motor vehicle theft), pincome- per capita income for each state, dropout- high school drop-out rate, precip- average precipitation in inches in the major city in each state over 1951-1980, pubaid- percentage of public aid recipients, density- population/total square miles, kids- public aid for families with children (dollars per family), unemploy- percentage of unemployed workers, urban- percentage of the residents living in urban areas. This data was provided by U.S. government sources: “the 1988 Uniform Crime Reports, Federal Bureau of Investigation, the Office of Research and Statistics, Social Security Administration, the Commerce Department, Bureau of Economic Analysis, the National Center for Education Statistics, U.S. Department of Education, the Bureau of the Census, Department of Commerce and Geography Division, the Labor Department, Bureau of Labor Statistics, the National Climatic Data Center, U.S. Department of Commerce” (Bowerman, O’Connell, Orris, and Murphree). I have used a multiple regression...
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...Sarah R Klemm GM533 Course Project Part A 7/21/13 Introduction: This report includes a comparision of 50 AJ DAVIS department store credit card customers. The report takes 5 individual variables, location, income ($1000), size, years (the number of years a customer lived in a certain location) and credit balance (the customer’s current credit card balance on the store’s credit card, in dollars) and compares them both graphically and numerically. A. The first variable discussed in location of the customers. The chart below is a bar graph seperating the 50 credit card customers into rural, suburban and urban locations. The bar graph shows that most of the credit card customers are located in urban areas and the least amount of credit card customers are located in rural areas. B. The second variable discussed in the income of the household in thousands based on the histogram and box plot below. The histogram shows the frequency in which credit card customers with household incomes between $20,000 and $65,000 have credit cards. The boxplot shows where the highs and lows of those income categories fall. C. The third variable graphed is the years the credit card customer has lived in their current location. Based on the histogram below most credit card customers have lived in their current location around 15 years. The histogram shows the tallest frequency above the 15 year mark. Whereas the dotplot shows that the highest number of years is at 14 and...
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...GM533 Applied Managerial Statistics Course Project Ebenezer Newman and Mark Cherry * NE (Northeast) 1: Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, Pennsylvania, New Jersey 0: Others * MW (Midwest) 1: Wisconsin, Michigan, Illinois, Indiana, Ohio, Missouri, North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa 0: Others * WEST (West) 1: Idaho, Montana, Wyoming, Nevada, Utah, Colorado, Arizona, New Mexico, Alaska, Washington, Oregon, California, Hawaii 0: Others * Region 3 (South) Delaware, Maryland, District of Columbia, Virginia, West Virginia, North Carolina, South Carolina, Georgia, Florida, Kentucky, Tennessee, Mississippi, Alabama, Oklahoma, Texas, Arkansas, Louisiana Scatter plots Get the scatter plots for each variable against the crime rate VIF From the result we see that there is no Multicorinality Predictor Coef SE Coef T P VIF Constant -340 1101 -0.31 0.759 NEAST -304.9 508.9 -0.60 0.553 3.307 MID-WEST -164.5 475.2 -0.35 0.731 3.564 WEST 351.6 588.9 0.60 0.554 5.773 PINCOME -0.01055 0.07966 -0.13 0.895 4.154 DROPOUT 70.66 26.61 2.66 0.011 2.975 PUBAID -76.43 86.78 -0.88 0.384 2.305 DENSITY -1.6666 0.9109 -1.83 0.075 3.760 KIDS 0.851 1.801 0.47 0.639 3.959 PRECIP 7.69 13.85 0.56 0.582 3.328 UNEMPLOY -93.30 ...
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...GM533 PROJECT PART C: Regression and Correlation Analysis 1. 2. The equation of the ‘best fit’ line which describes the relationship between credit balance(y) vs size(X) is given as follows: y = 404.13x + 2581.9 3. The coefficient of correlation = 0.752483 Correlation coefficient, r is a measure of the degree of correlation or interdependence between two variables. The value of the correlation coefficient can range between -1 and +1. A negative value of r indicates an inverse relationship; a positive value of r indicates a direct relationship; a zero value of r indicates that the two variables are independent of each other. The closer r is to +1 or -1, the stronger is the relationship between the two variables. For the given regression model, the correlation coefficient is very close to its ideal value of +1, thus indicating a strong positive correlation among the variables credit balance(y) vs size(X). 4. The coefficient of determination = 0.566773. Coefficient of determination, r2, is a measure of the amount of possible variability in the dependent variable that can be explained by its relationship to the independent variable. It is the square of the coefficient of correlation. The value of r2 ranges from 0 to 1 and higher the value, the better the fit. For the given regression model, about 94.81% of the variability in the dependent variable credit balance (Y) can be explained by the variability in the independent...
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...Project Part A Exploratory Data Analysis Keller Graduate School of Management GM533: Managerial Statistics (Downers Grove, IL) Michael Knapp Table of Contents I. Introduction...pg 3 II. Individual Variables a. Location…pg 3 b. Income…pg 3 and 4 c. Size…pg 4 and 5 III. Relationships d. Size and Location…pg 5 and 6 e. Income and Location…pg 7 and 8 f. Size and Income…pg 8 and 9 This analysis is to compare the information presented by AJ DAVIS which 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 five variables, I have selected 3 to compare side by side to one another and analyze the data provided. The 3 variables are LOCATION (Rural, Urban, Suburban), INCOME (in $1,000ʼs – be careful with this) and SIZE (Household Size, meaning number of people living in the household). After I cover each variable I will compare the relationship between size and location, income and location, and size and income. The first variable is location. The location refers to three categories: rural, suburban and urban. Based off of the information provided I found that 13/50 or 26% of the sample group live in a rural area, 15/50 or 30% of the sample group live in a suburban area and 22/50 or 44% live in a urban area. Tally for Discrete Variables: Location Location Count Percent ...
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