...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 determining...
Words: 973 - Pages: 4
...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 ...
Words: 591 - Pages: 3
...To: Board of Directors From: Date: 8/20/10 Subject: Analysis of Pricing Data As you asked, I have analyzed the data and concluded the selling price model of houses in Eastville. Here are my results. Data: Data was provided of a sample of 108 chosen houses in Eastville, Oregon. From these files, we obtained variables including: total square footage of the house, numbers of beds and baths, style of the house, presence of gas forced or electric heat, garage, basement, age of house, fire place, and school district. In order to examine what the selling price model of houses in Eastville, Oregon is, we also need to figure out the factors drive the selling price. Hypothesis: • The higher the total square footage of the house is, the higher the selling price will be. • The more bedrooms there are, the higher the selling price will be. • The more bathrooms there are, the higher the selling price will be. • The style of the design accommodates for the selling price of the house. • The presence of electric heat will result in a higher selling price. • The presence of a 2 car garage will result in a higher selling price. • The presence of a fireplace will result in a higher selling price. • Since Apple Valley School district is the more preferable, houses in this district will result in a higher selling price. • The older the house is, the lower the selling price will be. Results: Using Price as the independent variable...
Words: 1503 - Pages: 7
...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...
Words: 1184 - Pages: 5
...The following report presents a detailed statistical analysis of AJ DAVIS department store customers. Data was collected from a sample of 50 AJ DAVIS credit customers for the purpose of learning more about the customers of AJ DAVIS. The first variable considered is Location, a categorical variable. The three subcategories are Urban, Suburban and Rural. The frequency distribution and pie chart are included. Measures of central tendency and descriptive statistics are not calculated due to the categorical nature of the variable. Frequency Distribution: LOCATION | FREQUENCY | Urban | 22 | Suburban | 15 | Rural | 13 | The largest number of customers belong to the Urban Location category (44%), followed by those in the Suburban Location category (30%). The least number of customers belong in the Rural Location category (26%). The next individual variable considered is Household Size, meaning the number of people living in the household. Size is a quantitative variable. The measures of central tendency and variation along with other descriptive statistics have been calculated for this variable. Descriptive Statistics: AJ DAVIS Customer Data - Household Size Total Variable Count N N* CumN Percent CumPct Mean SE Mean TrMean StDev C1 50 50 0 50 100 100 3.420 0.246 3.341 1.739 Sum of Variable Variance CoefVar Sum Squares Minimum Q1 Median ...
Words: 702 - Pages: 3
...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/ ...
Words: 560 - Pages: 3
...HCS 533 Week 1 Individual Assignment Definition Worksheet (2 Answer) FOR MORE CLASSES VISIT www.hcs533study.com This Tutorial contains 2 Answers for each Question HCS 533 Week 1 Definition Worksheet Definition of Terms The health care environment is constantly changing, new systems arise every day with terminology of their own to reflect the changes. As a health care professional, it is important for you to stay up-to-date with the terminology and its proper use. Define each term in the table below. There’s only one definition for each terminology. -------------------------------------------------------------------- HCS 533 Week 2 Individual Assignment Database Worksheet (2 Set) FOR MORE CLASSES VISIT www.hcs533study.com This Tutorial contains 2 Set of Answers (2 Paper) HCS 533 Week 2 Individual Assignment Database Worksheet Databases Worksheet Write a 50- to 150-word response to the following question. Be clear and concise, use complete sentences, and explain your answers using specific examples. Cite any outside sources. For additional information on how to properly cite your sources, check out the Reference and Citation Generator resource in the Center for Writing Excellence. 1. What is the difference between database types and capacities? 2. How do data inaccuracies affect patient care and reimbursement? 3. Review the databases below and explain the relationship between each of the databases and their impact on the medical records...
Words: 850 - Pages: 4
...9-913-533 JANUARY 29, 2013 ROBERT SIMONS CRAIG CHAPMAN Luotang Power: Variances Explained Introduction As soon as Tan Min Yi received the 2011 Report of Operations for the Luotang Power Company, he called company Controller Fiona Zhu and Sales Manager Ricky Wang into his office to discuss the results. Tan was general manager of the Luotang Power Company, a 600 Mega Watt (“MW”)1 coalfired power plant, located in Hubei Province, China. He was scheduled to make a presentation to the Board of Directors of his parent company, China Hua Tong Power (“HT Power”), the following week about the most recent results and was concerned about their reaction to the disappointing results.2 Tan knew his company had performed well during the year. Both plant availability3 and fuel economy had improved over the previous year. Additionally the plant’s primary customer, the Hubei Provincial Power Company (“HPPC”), had met its contractual electricity purchase obligations for the year. However, there had been limited opportunity to sell energy above the contractual minimum, either to HPPC or others. Still, Tan felt that these factors were outside his control. His team had performed well—it just didn’t show up in the financial results. The scheduled presentation to the Board was important for two reasons. First, HT Power was considering a 2,000 MW expansion at Luotang. However, on a more personal note for Tan, he had been general manager of Luotang since 2002, and he hoped it would be time for...
Words: 3495 - Pages: 14
...9-913-533 JANUARY 29, 2013 ROBERT SIMONS CRAIG CHAPMAN Luotang Power: Variances Explained Introduction As soon as Tan Min Yi received the 2011 Report of Operations for the Luotang Power Company, he called company Controller Fiona Zhu and Sales Manager Ricky Wang into his office to discuss the results. Tan was general manager of the Luotang Power Company, a 600 Mega Watt (“MW”)1 coalfired power plant, located in Hubei Province, China. He was scheduled to make a presentation to the Board of Directors of his parent company, China Hua Tong Power (“HT Power”), the following week about the most recent results and was concerned about their reaction to the disappointing results.2 Tan knew his company had performed well during the year. Both plant availability3 and fuel economy had improved over the previous year. Additionally the plant’s primary customer, the Hubei Provincial Power Company (“HPPC”), had met its contractual electricity purchase obligations for the year. However, there had been limited opportunity to sell energy above the contractual minimum, either to HPPC or others. Still, Tan felt that these factors were outside his control. His team had performed well—it just didn’t show up in the financial results. The scheduled presentation to the Board was important for two reasons. First, HT Power was considering a 2,000 MW expansion at Luotang. However, on a more personal note for Tan, he had been general manager of Luotang since 2002, and he hoped it would...
Words: 3675 - Pages: 15
...Analysis and Management II: Fixed Income | (2) | | IMB 533 Portfolio Analysis and Management III: Derivatives and Financial Risk Management | (2) | | IMB 536 Global Macroeconomic Trends and Financial Institutions or CSB Elective | (1) | | IMB 534 International Real Estate Investment | (1) | | IMB 539 Financial Management | (2) | | IMB --- CSB Elective | (2) | | Or | | | | | | IMB 595 Special Topics in International Business (to be completed at an IBSA partner school) | | C. Thesis or Extensive Written Case Analysis (12 semester hours): Term 3 (summer) | | | IMB 599 Thesis | (6) | | IMB 594 Capstone Project / Practicum | (6) | IMBA Course Descriptions (UNCW) The course descriptions shown below are only for courses offered by UNCW and the Cameron School of Business. See each alliance school for their course offerings. IMB 531. Cases in International Finance (1-2) This course will focus on international financial management cases. Cases will build on topics of international monetary systems, international investment decisions, portfolio diversifications, multinational capital structure, and foreign exchange risk and management. IMB 532. Portfolio Analysis and Management I (2) This course is designed to focus on tool and techniques of modern portfolio theory in a global context. Students will convert theory to practice through active management of a real dollar portfolio. IMB 533. Portfolio analysis and Management II (2) This course...
Words: 797 - Pages: 4
...| Memo To: Northwind Traders From: Samantha Winn CC: Instructor Date: 8/17/2014 Re: Revenue Assessment Thank you for the opportunity to assess your sales data in order to provide recommendations for increasing your sales. The analysis and recommendations below are based on the data you provided, which covers a period from May 2004 through June 2006. The analysis below is based on this data alone. Therefore, our recommendations should be tempered by your knowledge of business realities and your market. Please let us know if we can answer any questions concerning the analysis or the recommendations provided. ANALYSIS 1: Change of Unit Purchase Price Effecting Sales Analysis When sorting through the data provided by date then by product, you can notice that over time the unit purchase price per unit increases; this is easier to see if you select the product tab on the table and select one product at a time to view. I then continued to sort by product, date and then unit price. You can then easily see the cut off for when the price changes and see how it affects the quantity of the product purchased. If you sum up the quantity purchased for the 8 months Alice is $31.20, for example, and compare it to the quantity sum of 8 months the price is increased to $39.00, you can see that more of the product was purchased while the price was lowered by 29 units, but with the price of $39.00 per unit in later months, there was a higher profit of around $1,500 making the $39...
Words: 1106 - Pages: 5
...CIVIL AND SOCIAL USES OF DRONES. March 21, 2016 Drones, otherwise known as Unmanned Aerial Vehicles or Remotely Piloted Vehicles, are pilotless aerial vehicles which can be guided either through remote control from its control base or can navigate autonomously based on a preprogrammed software. Drones are often classified into military and civilian drones. Both with individual characteristics which distinguish them. The most outright difference is their functions, whereby military drones are used in the military setting whereas civilian drones are used in less ‘violent' functions and are unarmed, unlike their military counterparts. Eddie Codel, a digital photographer behind the first ever Flying Robot International Film Festival, stated that “Drones are now being used to help humanity tackle global issues”. This is especially true for the civilian drones which so far scientists are trying to get their relevance in almost every aspect of life. “The applications of drones are as limitless as the imagination” added Codel. As per what can be seen today, this is especially true in the fields where drones have been applied and have brought forth untold success. Areas like; search and rescue operations, Monitoring and inspecting infrastructure ,Aerial photography, filming of events ,Property inspections and appraisals ,Agricultural surveys, inspections, and maintenance ,Private surveillance and investigation ,Commercial aerial photography ,Entertainment, video feeds, filming...
Words: 988 - Pages: 4
...Managing Workforce Flow (38:533:533) SYLLABUS Spring 2013 (Subject to Change as Needed) Instructor: Jean Phillips Office: 212 Janice H. Levin Building E-mail: jeanp@rci.rutgers.edu (this is the best way to reach me) Phone: 445-5825 Office Hours: By appointment Overview This course focuses on the effective management of the flow of talent into and through organizations. I give particular attention to the impact of business strategy, internal and external labor markets, recruiting, selection, and person-job and person-organization match on staffing practices. We will cover human resource planning, layoffs, career transitions, and other workforce movement. An important goal of the class will be to provide opportunities to develop hands-on skills that are relevant to effectively managing talent flow. Thus, experiences focusing on the transfer of course material to real-world situations will be an integral part of the class. Required Materials Phillips, J.M. & Gully, S.M. (2011). Strategic Staffing (2nd ed.). Pearson/Prentice Hall. ISBN-10: 0136109748 ISBN-13: 978-0136109747 We will also be using Sakai for the course. Instructions are below. Please check Sakai for the most current syllabus and weekly assignments. Using Sakai 1. 2. 3. 4. 5. Go to: http://sakai.rutgers.edu Login using your Rutgers netid and password Click on the TAB “Managing Workforce Flow” If you do not see the TAB “Managing Workforce Flow”, contact me at jeanp@rci.rutgers.edu as you may not be on the roster. Click...
Words: 1137 - Pages: 5
...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...
Words: 1274 - Pages: 6
...Cover Letters and other career correspondence Your letter is your introduction —it continues or starts a conversation about work or education Just as you start a conversation by introducing yourself, a résumé should always be sent with an accompanying letter. Picture yourself sitting face-to-face with a person doing work that is interesting. What would you say? What do you want to ensure they know about you before you leave the room? Your letter is a chance to make a great first impression or continue a conversation that has already been started. Your letter is something employers expect —it shows your professionalism and helps them get to know you Every time you submit a résumé, you should attach an accompanying letter. Even if they don’t specifically request it, employers expect documentation from you that shows your professionalism and potential contributions. What better way to start than by using a cover letter? Your letter should clearly show the match —it illustrates the connections between you and the work Some employers scan your résumé first, while others start with the cover letter. To increase your chances of being invited to an interview, ensure that both documents clearly show the match between what the employer needs and what you can contribute. Once you have made the match, remember that the letter is your chance to stand out as an individual from the many other qualified applicants. Include information that supports and points to your résumé, without...
Words: 1716 - Pages: 7
