...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......
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...TQM and Reinventing Government on the web-site for teachers and learners of English as a secondary language from a German point of view. [pic] Table of contents |Total Quality Management and Reinventing Government |HOME[pic]PAGE |[pic] |back to An introduction to QM |go on to: Committee:TQM Information |[pic] | |[pic] TOTAL QUALITY MANAGEMENT AND REINVENTING GOVERNMENT I. What is TQM? TQM is a new paradigm of management! TQM is both a philosophy and methodology for managing organizations. TQM includes a set of principles, tools, and procedures that provide guidance in the practical affairs of running an organization. TQM involves all members of the organization in controlling and continuously improving how work is done. Government agencies that use TQM agree that it is fundamentally different from traditional management. II. History of TQM! TQM Japanese Management? Yes and No! The American Walter A. Shewhart of Bell Laboratories developed a system of measuring variance in production systems known as statistical process control (SPC). Statistical process control is one of the major tools that TQM uses to monitor consistency, as well as to diagnose problems in work processes. His student W. Edwards Deming, a mathematical physicist and U.S Department of Agriculture and Census Bureau research scientist, was hired to teach SPC and quality control to the U.S. Defense industry. These methods were considered so important to the war effort that they were classified as......
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...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 ......
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...1) Mean: The mean of a set of numbers is the average. The mean is calculated by finding the sum of all the values and dividing by the number of values. 11+12+12+13+14+16+18+19+20 = 135 There are 9 numbers in the series, so the mean is: Mean = 135/9 = 15 Median: The median of a series of numbers is the number that appears in the middle of the list when arranged from smallest to largest. For a list with an odd number of members, the way to find the middle number is to take the number of members and add one. Then divide that value by two. In our case, there are 9 numbers in the series. 9+1 = 10 and half of 10 is 5. The fifth number in the series is the median or 14. If the number of members of the series was even, the average of the two middle numbers would be the median. Mode: The mode is the number in the series that appears the most often. If there is no single number that appears more than any other number in the series, there is no value for the mode. The number 12 appears twice in the series. The mode of this series is 12. Quintile: The first quartile of a group of values is the value such the 25% of the values fall at or below this value. The third quartile of a group of values is the value such that 75% of the values fall at or below this value. The first quartile may be approximately calculated by placing a group of values in ascending order and determining the median of the values below the true median, and the third quartile is approximately calculated by......
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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 ($1000) | Frequency | Relative Frequency | 20-29 | 5 | 10% | 30-39 | 13 | 26% | 40-49 | 13 | 26% | 50-59 | 8 | 16% | 60-69 | 9 | 18% | 70-79 | 2 | 4% | | 50 | 100% | My second variable is the outstanding credit balances of...
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...1. The graph below shows a slight positive relationship between having a higher credit balance with a larger household size. 2. The equation of best fit, also known as the regression equation, is Credit Balance($) = 2591 + 403 Size. This means that for each 1 increase in household size that that credit balance increase by $403. 3. The coefficient of correlation is the square root of .566 which is .7523. This value being closer to one than to zero and being positive implies strong linear relationship between credit balance and size. It confirms that as credit balance increases, household size will also increase. 4. The coefficient of determination is .566. This value represents the proportion of the total sample variation of y, measured by the total sum of squares of deviations in the sample from the mean, can be explained by using credit balance to predict household size. This means that 56.6% of the sample variation credit balance (y) can be explained by using household size (x) in this straight line model. 5. The null hypothesis is that Beta1equals 0 and the alternative hypothesis is that Beta1 does not equal 0. The p-value is 0.000, which is less than 0.05. Therefore, the null hypothesis that Beta1 equals zero can be rejected. There is sufficient evidence that Beta1 does not equal zero. This shows that our regression model is useful, in that credit balance does depend on household size. 6. Based on my findings, credit balance will increase by $403 per......
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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......
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...Keller graduate school of management | Department Store part B | Week 6 project for AJ Davis | Information from the project for AJ Davis department store. Attached in this report is all information related to the information listed from Excel. | Results from Minitab findings The mean income was less than $50,000. The Null Hypothesis: which states the average annual income was greater than or equal to 50. The number of trials (n) is larger than 30 use ztest to check the hypothesis. With the Critical Value and decision rule: Reject H0 if Z –value is -1.645. By using the z-test the one sample z: results are as follows: z -3.02 p .001 mean 43.74. The p value .999 is larger than .05, so I would not reject the null hypothesis. The p value shows the probability of rejecting null hypothesis. The confidence level of .05 shows there is enough data to support the claim that the average annual income was less than $50,000. The confidence level at 99.5% is 38.41 and since 50 are above the 99.5% confidence level shows support for the claim that the mean income is less than $50,000. The proportion of customers who live in urban areas exceeds 40%. For this 21 out of the 50 people in the data live in an urban area. This equates to .42 so my point estimate would be .42 or 42%. The Ho: p=.40 vs. Ha: p >.40. z= .29. The reject area is z> 1.645; since .29 is less than 1.645 I would not reject Ho. The p value is .386. By not rejecting this is saying there is......
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...This article is about the study of topics, such as quantity and structure. For other uses, see Mathematics (disambiguation). "Math" redirects here. For other uses, see Math (disambiguation). Euclid (holding calipers), Greek mathematician, 3rd century BC, as imagined by Raphael in this detail from The School of Athens.[1] Mathematics is the study of topics such as quantity (numbers),[2] structure,[3] space,[2] and change.[4][5][6] There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics.[7][8] Mathematicians seek out patterns[9][10] and use them to formulate new conjectures. Mathematicians resolve the truth or falsity of conjectures by mathematical proof. When mathematical structures are good models of real phenomena, then mathematical reasoning can provide insight or predictions about nature. Through the use of abstraction and logic, mathematics developed from counting, calculation, measurement, and the systematic study of the shapes and motions of physical objects. Practical mathematics has been a human activity for as far back as written records exist. The research required to solve mathematical problems can take years or even centuries of sustained inquiry. Rigorous arguments first appeared in Greek mathematics, most notably in Euclid's Elements. Since the pioneering work of Giuseppe Peano (1858–1932), David Hilbert (1862–1943), and others on axiomatic systems in the late 19th century, it has become......
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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 | ......
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...can see how to split up the original equation into its factor pair, this is the quickest and allows you to solve the problem in one step. Week 9 capstone part 1 Has the content in this course allowed you to think of math as a useful tool? If so, how? What concepts investigated in this course can apply to your personal and professional life? In the course, I have learned about polynomials, rational expressions, radical equations, and quadratic equations. Quadratic equations seem to have the most real life applications -- in things such as ticket sales, bike repairs, and modeling. Rational expressions are also important, if I know how long it takes me to clean my sons room, and know how long it takes him to clean his own room. I can use rational expressions to determine how long it will take the two of us working together to clean his room. The Math lab site was useful in some ways, since it allowed me to check my answers to the problems immediately. However, especially in math 117, it was too sensitive to formatting of the equations and answers. I sometimes put an answer into the math lab that I knew was right, but it marked it wrong because of the math lab expecting slightly different formatting Week 9 capstone part 2 I really didn't use center for math excellence because i found that MML was more convenient for me. I think that MML reassures you that you’re doing the problem correctly. MML is extra support because it carefully walks you through the problem visually......
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...and solve problems in everyday life”. In my everyday life I have to keep the balance in my check book, pay bills, take care of kids, run my house, cook, clean etc. With cooking I am using math, measuring how much food to make for four people (I still haven’t mastered that one). With bills I am using math, how much each company gets, to how much money I have to spare (which these days is not much). In my everyday life I do use some form of a math. It might not be how I was taught, but I have learned to adapt to my surroundings and do math how I know it be used, the basic ways, none of that fancy stuff. For my weakest ability I would say I fall into “Confidence with Mathematics”. Math has never been one of my favorite subjects to learn. It is like my brain knows I have to learn it, but it puts up a wall and doesn’t allow the information to stay in there. The handout “The Case for Quantitative Literacy” states I should be at ease with applying quantitative methods, and comfortable with quantitative ideas. To be honest this class scares the crap out of me, and I am worried I won’t do well in this class. The handout also says confidence is the opposite of “Math Anxiety”, well I can assure you I have plenty of anxiety right now with this class. I have never been a confident person with math, I guess I doubt my abilities, because once I get over my fears and anxiety I do fine. I just have to mentally get myself there and usually it’s towards the end of the class. There are......
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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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...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......
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...Assignment 2 USA Motors HRM 533 Total Rewards July 20, 2010 Will the incentive plan to reduce absenteeism succeed? Explain your opinion. The incentive plan shows the ability to succeed. Jack Parks is the benefits and services manager, whom did a review of the division’s absenteeism rates for controllable absences, which are absences believed to be of the employees choice. “He finds that the company could reduce this rate from the previous year’s figure of 11%” (USA Motors, 2010, p.9). Parks seems to have ensured that the incentive plan will meet the organization’s needs and hopefully keep the employees motivated. Ten years ago an incentive plan was negotiated between USA Motors and the national union, to pay employess for being absent. “The theory was that by giving workers one week of paid absence against which they could charge personal absences, the company would be encouraging employees to notify their supervisors then they would be out of work, so that arrangements could be made and production could be maintained” (USA Motors, 2010, p.9). The employees found a loop hole in the incentive plan, ”by not charging off any paid absences, they could receive a full week’s pay in June when the company paid off the balance of unused paid absences for the previous year” (USA Motors, 2010, p.9). Parks felt that a need for an improved incentive plan to reduce absentee should be in place. “The plan Parks had in mind entails a standard for the amount of......
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