...The True Richard I have chosen to analyze the character of Richard III due to his cynical yet honest personality. One thing that sticks out about his character is his relationship with the reader and how he tells us exactly what he’s planning. Even when we are told what Richard will do, the reader is still surprised when he goes about the actions. For example, Richard tells us he is “subtle, false, and treacherous”(I.i), but there is nothing we can do to stop him from being that way. Another quality of Richard that sticks out to me is his ability to manipulate his peers. In act one scene two Lady Anne is mourning the death of King Henry VI, but Richard is set on manipulating her into loving him. Richard has no interest in Lady Anne, but instead wants to be closer and closer to becoming King. Richard’s dedication to achieve his goal no matter what obstacles come his way is a defining feature that sticks out to me. Throughout the play, Richard is faced with obstacles that test his commitment to becoming king. His first major test is when he has to win over Lady Anne. Richard killed Henry and knows Anne is out for his guts, but he has the courage to lie to her saying that he did not kill him. Later, Richard admits to killing Henry, but sweet talks Anne by making her believe that the murder was in an attempt to win her over. While reading this scene, we see Richard being honest to the audience, but a liar to the characters in the show. He knows that his actions have caused Lady...
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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 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) 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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...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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...Keller Graduate School of Management Applied Managerial Statistics (GM533) Course Project Case Study: Grocery Bags data gathered and written by ME Applied Managerial Statistics GM533 Instructor: XX I. Executive Summary For this research I decided to develop my own case study and collect all the data myself. The data file named “Grocery Bag Study” (separate attachment), contains observations on 33 sample groups with a variation of 8 different characteristics (see table below). These characteristics include ethnicity, number of adults and minors in household, the number of bags collected weekly, the number of bags they recycle or reuse, the use of reusable fabric shopping bags, how many they throw away and the sex of the adults in the household. The totals for the bags are shown in the table below (Table A.1). For the sake of this study, I am going to combine the paper and plastic together because I want my dependent variable to be the number of bags recycled each week. I want to determine if the independent variables affect the recycle rate and which ones affect it the most, which can be eliminated and what my conclusions will be. II. Calculations This file was used to prepare a report on the influence of various options on grocery bags collected each week and to relay how this information could be used to determine the recycle rates (y). Statistical analysis by Hypothesis Testing and Multiple Regression Analysis was performed on the collection of grocery...
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...Case 28: Housing Prices GM533 Managerial Statistics April 11, 2012 I’m conducting an analysis between the price of a home in Eastville, Oregon and the factors which develop a home’s price. The data is analyzed using ANOVA (Analysis of Variance) and multiple regression hypothesis testing procedures. The regression analysis will help create a multiple regression fit which will incorporate the ten predictor variables of a home’s price. After the regression analysis is complete, global and local ANOVA tests will help eliminate the insignificant predictor variables and create the net significant regression equation. Even though the sample size is only representative of the houses in Oregon, the general trends that affect house prices are the same. Therefore, this multiple regression model will be useful in its predictions but limited in it’s use on more complex homes. In addition, there are more factors than the ten discussed that affect the price of a home (i.e. the economy, inflation, etc.); therefore the tool has limited power. As a homeowner, one should know his or her home’s accurate price in the market. This is an extremely important fact to know, so a person can decide when it’s appropriate to sell their home. There are several tools out there already such as Zillow.com but, these websites are giving prices that are estimated from the original price of a home. Using a regression equation will give the ability to have a second opinion on his or her home’s price. Even though...
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