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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

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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…...

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... 2 | 15 | 3 | 8 | 4 | 9 | 5 | 5 | 6 | 5 | 7 | 3 | The mean household size of the customers is found to be 3.42. The median of the data is 3 and the mode is 2. The standard deviation is approximately 1.74. The largest number of customers have a household size of 2 as is displayed in the frequency distribution and the bar graph. The 3rd individual variable considered is Credit Balance. It is a quantitative variable. The measures of central tendency, variation and other descriptive statistics have been calculated for this variable. Descriptive Statistics: Credit Balance Total Variable Count N N* CumN Percent CumPct Mean SE Mean TrMean StDev C1 50 50 0 50 100 100 3970 132 3980 932 Sum of Variable Variance CoefVar Sum Squares Minimum Q1 Median Q3 C1 868430 23.47 198523 830780693 1864 3109 4090 4748 N for Variable Maximum Range IQR Mode Mode Skewness Kurtosis MSSD C1 5678 3814 1638 3890 2 -0.15 -0.72 373840 RELATIVE FREQUENCY DISTRIBUTION CREDIT BALANCE | FREQUENCY | RELATIVE FREQUENCY | $1500-$2000 | 1 | 0.02 | $2000-$2500 | 2 | 0.04 | $2500-$3000 | 6 | 0.12 | $3000-$3500 | 6 | 0.12 | $3500-$4000 | 8 | 0.16 | $4000-$4500 | 12 | 0.24 | $4500-$5000 | 7 | 0.14 | $5000-$5500 | 6 | 0.12 | $5500-$6000 | 2 |......

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...Week 6 Course Project B) Written below is a sample communication via the use of a memorandum George Fisher has sent out to his employees at Kodak. In this communication message it will give information about the change Kodak is making into the digital world. Memorandum To: All Staff From: Mr. George Fisher, CEO Date: January 12, 2002 Subject: Changes to come For the past ten years we have been designing a strategy to embark on a new change into the digital world, leaving behind the existence of the use of film. It is crucial that we stay above our competitors and continue to change and explore new ways of finding our way in staying competitive in this ever-changing market. That is why we are beginning our conversion from film to digital imaging in the weeks to come. We will launch the changes immediately to in order to get our production started as our estimates are growth show much potential for success. Now, we might face some obstacles along the way in executing the change. Such as the need to invest in research and “release incrementally updated products in a number of different fields” as soon as possible, but we believe this will work. Therefore, we need you on board because you are the essential factor in helping us making this change a success story, and a great moment in Kodak’s history. We might have to cut some of our losses along the way, such as factories and jobs, in order to keep up with our counterparts and competitors, but there is more......

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...Clark Paints – Course Project B, Question 2 The production department determined that 1,100,000 cans would need to be manufactured each year for the next 5 years if Clark Paints were to begin making their paints cans instead of purchasing them. Considering all relevant costs it was determine that it would cost the company $422,460 annually to make the paints can versus $495,000 they are currently paying to buy the paint can. Clark Paints should accept the proposal to manufacture paint cans because it would result in an annual, before tax, cash savings of $72,540, as well as $32,000 in tax savings as a result of equipment depreciation. The total annual cash flow generated, after tax, is $ 58,351. The initial investment of $200,000, for equipment, would paid back in 3.43 years due to a 13.18% annual rate of return and overall decreased costs. The Net Present Value was calculated at $33,035. The Positive NPV indicates positive future cash flows and present equity. Annual cash flow generated within the 5 years would result in an Internal Rate of Return of 18%. The high Internal Rate of Return indicates an increased rate of future growth as a result of the reduced cost and increased cash flow. Again, this proposal should be accepted because of the significant reduction in cost, the attractive positive Net Present Value and the high Internal Rate of Return. Calculations Summary: Annual cash flows over the expected life of the equipment = $ 58,351 Payback period = 3.4 Years ...

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...Project Part 3 Betsy Whitfield ITT Tech Online MG514 – Managerial Economics Professor Michael Thirtle April 27, 2013 | | Project Part 3 The fundamental concepts of managerial economics are to decide if a production plant is needed. What will be produced, where the plant will be located, and how many employees do we need. I chose a plastic plant. One can either buy an existing manufacturing plant (Starting a Plastic Manufacturing Plant, 2013). There are a lot of plastic manufacturing plants that are fully furnished and offered for sale in various parts of the world (Starting a Plastic Manufacturing Plant, 2013). They cater to a wide variety of plastic products (Starting a Plastic Manufacturing Plant, 2013). Once you are knowledgeable of your desired product, the scientific and technical way to manufacture it and the type of business venture you are planning, as well as the location of your plant, you can start looking into the license and permit requirements of the government (Starting a Plastic Manufacturing Plant, 2013). Any transaction has two dimensions, namely a demand and a supply (http://www.buzzle.com/, 2013). In any market, the number of units of a commodity that a consumer has the will and ability to pay for becomes the demand for that particular commodity in the market and any producer that has the will and ability to produce those units, becomes the supply (http://www.buzzle.com/, 2013). It is not......

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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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...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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...probability that an event will occur, when another event is known to occur or to have occurred. If the events are A and B respectively, this is said to be "the probability of A given B". It is commonly denoted by P(A|B), or sometimes PB(A). P(A|B) may or may not be equal to P(A), the probability of A. If they are equal, A and B are said to be independent. For example, if a coin is flipped twice, "the outcome of the second flip" is independent of "the outcome of the first flip". Given two events A and B with P(B) > 0, the conditional probability of A given B is defined as the quotient of the joint probability of A and B, and the probability of B: P(A|B) = \frac{P(A \cap B)}{P(B)} Given two events A and B with P(B) > 0, the conditional probability of A given B is defined as the quotient of the joint probability of A and B, and the probability of B: P(A|B) = \frac{P(A \cap B)}{P(B)} 3) Binomial Probability: The binomial distribution describes the behavior of a count variable X if the following conditions apply: 1: The number of observations n is fixed. 2: Each observation is independent. 3: Each observation represents one of two outcomes ("success" or "failure"). 4: The probability of "success" p is the same for each outcome. If these conditions are met, then X has a binomial distribution with parameters n and p, abbreviated B(n,p). 4) 5) 6) 7)...

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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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...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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...Funches Math 533 Introduction The department store AJ Davis wants to study the customers that use credit with them. They collected a sample group of 50 people and broke them down to 5 different variables. The 5 different variables are broken down by location, income, household size, years in current location, and credit balance. I plan on creating different graphs and tables to make things easier to study to see the difference in groups. 1st individual variable The pie chart above clearly shows that most/max of the customers to buy from AJ Davis live in the urban location. Even though the rural location is the min of the three, rural and suburban is very close to each other that they both make-up for the rest of the sales. 2nd individual variable The histogram above shows how many people fall into the different income ranges. It seems that the middle class has the lowest amount of people in that category where the lower and upper class mirror each other in the chart above. 3rd individual variable The dotplot above shows how many families fall into the category of how many people are in the household size. It looks as if the families with two people is the largest group out of everyone by a long shot, and households with one, five, six leaving there are equal. 1st pairing of variables The boxplot above shows the connection between the three different locations and the credit balance with AJ Davis. The Rural location seems to be......

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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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...includes the following: - Using derogatory sexual slurs to signify to a coworker -Making repeated unwelcome physical contact with a worker, whether sexual or not -leaving pornographic photos or sexual paraphernalia on a person’s desk -Teasing a coworker for not partaking in telling sexually derogatory jest or stories In the U.S., whether an environment is 'hostile' or 'abusive' can be determined only by looking at all the situations. These may contain the regularity of the discriminatory behavior; its severity; whether it is physically intimidating or embarrassing, or a mere unpleasant expression; and whether it unreasonably intrudes with an employee's work performance. The consequence on the employee's psychological comfort is, of course, applicable to determining whether the accuser essentially found the environment abusive. But while psychological mischief, like any other relevant aspect, may be taken into account, no single factor is necessitated. Sexual Harassment is considered severe or pervasive if evidence establishing that you were given compliments regarding your body, that you were touched, that you were told you were being observed and even trailed home may be ample to make a determination that the harassment was "severe or pervasive." The courts will normally take into deliberation the regularity of the harassing behavior when determining if such conduct was "severe or pervasive." The superior the frequency of such unsolicited conduct, the grander the......

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