Help With a MEMORANDUM and fill out a matrix form – SAMPLES have been attached

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Help With  a MEMORANDUM and fill out a matrix form – SAMPLES have been attached  – Thanks

Help With a MEMORANDUM and fill out a matrix form – SAMPLES have been attached
Descriptive Statistics – Matrix Sample Sample – What can we learn from the data? Item to be Addressed Data Source Example – Airline Arrival Study What is the name of the data? See the title. Airline Arrival Study In general, what is the data set See the first sentence of the file BUGN 280 Excel Project Date File Information document. A random sample of 444 flights from LAX to JKF in New York. What data is included in the sample? See Variables of Interest worksheet in your data set. In your own words… There are five continuous variables: arrival delay and departure delay in minutes, taxi time from gate to take-off and from landing to gate, and air time. The two categorical variables are the airline carrier and the time of departure – am or pm. Is any of the data coded? How? See Variables of Interest worksheet in your data set. The name of the carrier: 1 is American, 2 is United. Note: Values for arrival mean early arrivals, 0 = on time, and + values mean later than scheduled. Where did the data come from? See your data set worksheet tabs. Bureau of Transportation Statistics, US Government How do you think the data might have been collected? Guess or see source. Flight recorder or flight log Why do you think the data might have been collected? Guess or see source. Provide information to traveling public on arrival/departure performance against the schedule. Who is the suggested target of this information? See description file, or look up the information. COO United Airlines What are the questions the target would like to address or information he/she would like to have? Think about what questions you might be able to answer. What is the range of times or the delays? What is the average delay? Do the delays vary by time of departure? Which airline has the better record?
Help With a MEMORANDUM and fill out a matrix form – SAMPLES have been attached
Descriptive Statistics Memo: Now that you’re familiar with looking at data sets and extrapolating the “story” that the data reveals (your regression memo assignment), it’s time to take a more encompassing look at descriptive statistics, in particular, what statistics can tell us about different variables and what the larger picture is from that “story.” Task: Each of you will review your new assigned data set. You are to: Review your data set and all of the statistics, charts, graphs, and related data that your Excel file reveals. Fill out the Descriptive Statistics Matrix for your data set. Prepare a memo to me, your manager at Quanta Communications. The scenario: I’m your manager. At our last meeting, I gave you a data set and asked you to summarize it for me. Tell me what it entails, who might find it useful, and why. Your memo should summarize the overall findings of your data. What’s the story that the data tells you? Why is that important? Upload your memo and your completed matrix to Canvas by the due date discussed. How should you organize your memo to give me the best information? It’s your turn to come up with the sections (section titles) that you think are important in order for your reader to best understand your data set. Include in an Appendix to your memo at least 2 charts or graphs that you think best illustrate what you’re summarizing. Make reference to these images. Don’t just cut and paste them; do some editing so they don’t just look cut and pasted. (You will lose points if you don’t follow this step.) Requirements: The first part of the memo will introduce why you’re writing the memo. The next part will summarize what your data set is about, the scope of the data, and the descriptive statistics used in the data. In essence, you’re summarizing the information from your matrix. Keep your memo to two pages max. Interpret how this data can be useful. Your closing paragraph should tell me to reach out to you if I have any additional questions. DO NOT discuss any regression statistics in this memo. Other considerations: Your narrative must be clear enough to evaluate your interpretation of the data and your conclusions. Anything merely copied/pasted from your downloaded Excel file may lead to loss of points because many of these need to be edited for clarity and design.  In sum: You are describing the data you have by showing that you understand the descriptive statistics presented in your data set. You are clearly articulating how your data can be used to help form an opinion or make business decisions.
Help With a MEMORANDUM and fill out a matrix form – SAMPLES have been attached
Memorandum To: Gail Yosh, President, Quanta Communications From: Jane Smith Date: May 2, 2016 Subject: Business Valuation Analysis During our April 29th meeting, you asked me to review the compiled data on the pharmaceutical industry and its main competitors. I conducted an analysis of the data, and I believe it will be very beneficial as we work with our newest client, ABC Pharmaceuticals. Entitled ‘Business Valuation Study,’ the data set contains information pertaining to 71 companies in the pharmaceutical industry. Each company is categorically organized by its name, type, and stock exchange reference. Information pertaining to Debt/EBITA Ratio and Ratio Level is additionally provided. The data set also shows the continuous variables of each company with Price/Book Value Ratios, PE Ratios, Assets, Return on Equity, and Growth Percentages. Although it is not indicated where the data came from, it can be inferred that it was compiled with the use of company’s public financial records and through other financial information databases. Chart 1, attached in the Appendix on the following page, displays the Debt/EBITDA Ratio of the companies by type, either Pharmaceutical Preparation or Biological Products. This ratio measures a company’s ability to pay off incurred debts, and is highly valuable in providing investors an estimated time needed for a particular company to pay off such debts. Table 1 displays statistical data for the ROE of both types of companies. This reveals a higher mean ROE for BioProducts (29.2 to 20.7), as well as a higher maximum ROE (133 to 91.5). This indicates to investors the chance of higher returns with BioProducts companies. However, the standard deviation of this company type, at roughly 28.9, is higher than PharmPrep’s 16.28 standard deviation. This indicates to investors that BioProducts companies have greater variation, and perhaps greater unpredictability, opposed to the more steady patterns with the latter. ABC Pharmaceuticals has contracted Quanta Communications to aid in both internal and external public relations. This information is excellent for understanding the competitive landscape of the overall pharmaceutical industry, as well as for understanding how potential investors may view our client as opposed to other pharmaceutical investment options. With this data, particularly the Debt/EDBITDA and ROE information, we will have a stronger foundation as we improve communications between ABC Pharmaceuticals and its current or potential investors. Additionally, this in depth look at the financial makeup of the industry will aid us in accurately conveying information to employees and the general public. Please do not hesitate to contact me with any comments or questions regarding the above information and analysis. I would also be happy to conduct additional research upon your request. Appendix Chart 1 – Type Co. and Debt/EBITDA Ratio Table 1 – ROE and Type Co.   PharmPrep BioProducts Mean 20.70533333 29.11851724 Median 15.8325 19.433 Minimum 1.583 1.45 Maximum 91.52 133.042 Range 89.937 131.592 Variance 265.1270 833.7809 Standard Deviation 16.2827 28.8753 Coeff. of Variation 78.64% 99.16% Skewness 2.7318 2.2668 Kurtosis 9.4610 5.6607 Count 42 29 Standard Error 2.5125 5.3620
Help With a MEMORANDUM and fill out a matrix form – SAMPLES have been attached
Used Autos Study – Descriptive Statistics – Matrix What can we learn from the data? Item to be Addressed Data Source What is the name of the data? See the title. In general, what is the data set See the first sentence of the file BUGN 280 Excel Project Date File Information document. What data is included in the sample? See Variables of Interest worksheet in your data set. Is any of the data coded? How? See Variables of Interest worksheet in your data set. Where did the data come from? See your data set worksheet tabs. How do you think the data might have been collected? Guess or see source. Why do you think the data might have been collected? Guess or see source. Who is the suggested target of this information? See description file, or look up the information. What are the questions the target would like to address or information he/she would like to have? Think about what questions you might be able to answer.
Help With a MEMORANDUM and fill out a matrix form – SAMPLES have been attached
32-Used Autos Study (A random sample of 743 recently sold used cars.) Scenario You are employed as a research assistant at the American Automobile Association (AAA) and your supervisor, Salvador Apari, asks you to study the characteristics of recently sold used cars. For this purpose you take a sample of 743 automobiles that were recently resold. For each automobile you collect data on several variables (see the Variable INFO tab) but for the reports you will be preparing you decide to focus on one key numerical variable, “Price” (i.e., the price paid in thousands of dollars when purchasing the used vehicle), and one key categorical variable, “Type” of vehicle – Coupe, Minivan Muscle Cr, PickupT, Sedan, SedanWg, Sports Cr, SUV, Van, or Wagon. You also decide to use a grouping categorical variable “Origin” since each vehicle is manufactured in either Asia or Europe or in the USA. This will enable you to make comparisons of both the “Price” of the vehicle and the “Type” of vehicle based on its origin – manufactured in either Asia or Europe or manufactured in the USA. In addition, you have also selected the numerical variable “Mileage” (i.e., the mileage, in thousands, recorded on the odometer of the used vehicle at time of resale) to develop a simple linear regression model to predict the “Price.”

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