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MEQA Assignment 1

Issues and Scope: worth 5 points out of 30

Writing: worth 5 points out of 30

· Table Of Content (TOC)

· Typo free

· Presentation of the summaries of various tests.

· Proper formatting, sentence structure, and English

· Nice easy to read writing style

· References

1. Marks are deducted for, (i) too many typos, (ii) poorly written passages, (iii) poor organization, and (iv) poor documentation – for example, omitting the Regression Output, Supporting calculations, etc.

2. Formatting should be according to APA guidelines.

3. Executive Summary is not part of word count, hence, if included, it is not read.

4. You are allowed two appendices, where you should deposit your calculations, computer output, and original data, etc. Do keep in mind that anything that justifies your conclusion(s) in the main body of the report should be in the main body -- for example, your regression equation with the explanation of its notation should be in the main body. Similarly, if you use p-value to justify a statistical significance, you should provide p-value (in a table for all regression coefficients), in the main body.

5. Exceeding the word count limit -- 1000 words

Pl. pay attention to English, including sentence structure.

We'll use the familiar 5% as significance level for the Assignment I. Also, for the purposes of Assignment 1, we'll not discard any variable from the regressed model, though it may be statistically not significant.

· You need not perform the t-test for significance, pl. use the p-value and its interpretation; it will save you time. (It may help, if you did a quick review of Ch. 4 (T&M), to refresh your understanding of Regression output.)

You are asked to run two regression -- Linear model, and the Log Linear model. You'll see that the explanatory power of both is nearly equal, therefore, state in your Report that you are choosing, Linear model, as this will help you answer optimal price question.

· For computing elasticities, pl. use averages as typical values

· For optimal price, keep in mind that Harry has no control over values of M, N, and P_h, but pl. do not drop these variables from the equation, rather use the average values of these as typical values, and, using the average value for A, you can get an equation with only two variables -- own-price and quantity.

· Once you have found the optimal quantity, Q*, and corresponding price, P*, to find the corresponding advertising spend, say A*, you should go back to your original regression equation, and keep A as the only variable. Since you know the values for all other variables, you can compute A*.

· For all computational work, pl. let EXCEL perform the computations; it would also reduce your headache. (Rounding too early will cause erroneous results, which can be avoided by letting EXCEL do the computations.)

· In your Assignment 1 response pl. ensure that you have:

· clearly answered the questions raised by the CEO and that your answers can be easily found.

· provided all the supporting computations. As you know, most of the tables, figures, and computations should be in Appendices, so that CEO's Assistant can consult them. (Organize your Appendices; you can have up to two Appendices!)

· adopted a writing style that is reader friendly – easy to read and well structured, organized into appropriate sections, and carefully checked for spellings and typos. Pl. note that marks are specifically allocated for writing.

The relevant computer output information should be included in an Appendix and , your EXCEL file containing the regression output should be attached. Thus, there will be two file attachments -- a WORD file (your Report to Harry), and an EXCEL file (your Regression output).

You accepted an offer from MagnaMed, and today is your first day working there as a business analyst. MagnaMed does business by producing and selling a range of medical diagnostic products. Its main product is the TrickKit™, an easy-to-use medical diagnostic tool for Hepatitis B. Trained personnel in physicians’ offices, medical facilities, clinical laboratories, and emergency care situations could use this as a screening assay to produce results in less than a minute. SusaMed is the main competitor of MagnaMed, and it sells a similar product, HepaTest, the only alternative to TrickKit™ in the market.

Last week, MagnaMed’s other business analyst, Sally Litch, didn’t show up for work. Rumour has it that Sally went to Vegas for the weekend and ended up joining Cirque du Soleil, but nobody really knows. The police have been informed.

Before she disappeared, Sally was working on an important and time-sensitive project for MagnaMed’s CEO, Garry Smith. The project involves four interrelated tasks:

1. Estimate the linear and log-linear empirical demand functions for TrickKits™.

2. Interpret the estimated demand functions for TrickKits™.

3. Make pertinent recommendations to senior management based on the empirical demand

functions.

4. Write a short report summarizing the results of the analysis and any recommendations.

Garry asks you to complete the project. He tells you that he’s particularly interested in knowing if the current price of the TrickKit™ is optimal and, if not, whether it should be increased or decreased. He tells you that the current price of a TrickKit™ is $30.00 and that since the production process has been set up, the marginal cost of producing additional kits is essentially zero. Garry is also interested in knowing whether or not the firm’s advertising expenditures

over the last three years have been a good investment and anything else relevant that your analysis uncovers.

You only have a week to complete the analysis, interpret the results, and summarize your findings and recommendations in a brief report. Garry tells you that the main body of the report must be short (a 1000 words at most excluding the title page and any appendices), to the point, and not overly technical. He tells you that his formal knowledge of mathematics, economics, and statistics is somewhat limited, and asks you to keep that in mind as you write the main body of the report. Nevertheless, your conclusions and recommendations must be based on a rigorous analysis of the available data and you should provide a concise summary of any technical details in an appendix. Garry also tells you to be explicit about any important limitations that your analysis might have.

Finally, Garry tells you that he likes his reports to be broken up into sections with sensible and self-explanatory headings because it makes them much easier to read and understand. Garry gives you a copy of an Excel spreadsheet that Sally sent him. It contains data that Sally collected on the variables she thought might have a significant influence on the demand for TrickKits™. It also contains some notes Sally made about the data and how to proceed with the

analysis. The spreadsheet is the only record of Sally’s work on the project.

Garry has complete confidence in Sally’s technical abilities and professional judgment. As such, he tells you to take the notes in Sally’s spreadsheet at face value and to use them as a starting point for your analysis. As Garry puts it, “You’ve only got a week, so there’s no point trying to reinvent the wheel. If Sally thought the data should be treated in a particular way, then that’s what you should do.”

Data.xlsx

Data

Observation Date Q P M N A P_h ln(Q) ln(P) ln(M) ln(N) ln(A) ln(P_h)
1 Jan-13 9,528 $ 33.99 $ 75,969 32,981,562 $ 30,000 $ 11.35 9.162039043 3.5260663637 11.2380785824 17.3114592461 10.3089526606 2.4292177439
2 Feb-13 9,496 $ 33.99 $ 76,564 33,008,011 $ 30,000 $ 11.75 9.1586130748 3.5260663637 11.2458874645 17.3122608576 10.3089526606 2.4638532406
3 Mar-13 11,760 $ 30.00 $ 77,160 33,034,460 $ 30,000 $ 12.00 9.3724579399 3.4011973817 11.2536358401 17.3130618271 10.3089526606 2.4849066498
4 Apr-13 11,917 $ 30.00 $ 77,756 33,060,909 $ 30,000 $ 11.95 9.3857434768 3.4011973817 11.2613246397 17.3138621555 10.3089526606 2.4807312784
5 May-13 13,189 $ 24.50 $ 63,933 32,240,990 $ 20,000 $ 12.05 9.4871388087 3.1986731176 11.0655840458 17.2887491927 9.9034875525 2.4890646599
6 Jun-13 13,653 $ 24.50 $ 63,391 32,267,439 $ 20,000 $ 12.00 9.5217309572 3.1986731176 11.0570766473 17.2895692096 9.9034875525 2.4849066498
7 Jul-13 12,292 $ 27.00 $ 63,391 32,293,888 $ 20,000 $ 12.00 9.4167161724 3.295836866 11.0570766473 17.2903885546 9.9034875525 2.4849066498
8 Aug-13 11,930 $ 27.00 $ 63,883 32,320,337 $ 25,000 $ 11.90 9.3867707917 3.295836866 11.0648136298 17.2912072289 10.1266311039 2.4765384001
9 Sep-13 11,348 $ 27.00 $ 63,883 32,346,786 $ 25,000 $ 11.75 9.3368334953 3.295836866 11.0648136298 17.2920252334 10.1266311039 2.4638532406
10 Oct-13 12,514 $ 27.00 $ 64,302 32,373,235 $ 25,000 $ 11.75 9.4345845385 3.295836866 11.0713433246 17.2928425694 10.1266311039 2.4638532406
11 Nov-13 11,805 $ 27.00 $ 64,302 32,399,684 $ 25,000 $ 12.00 9.3762786617 3.295836866 11.0713433246 17.2936592379 10.1266311039 2.4849066498
12 Dec-13 10,781 $ 27.00 $ 64,302 32,426,133 $ 15,000 $ 12.00 9.2855100164 3.295836866 11.0713433246 17.29447524 9.6158054801 2.4849066498
13 Jan-14 10,818 $ 29.00 $ 64,499 32,452,582 $ 15,000 $ 11.50 9.2889708494 3.36729583 11.0744014309 17.2952905768 9.6158054801 2.4423470354
14 Feb-14 10,071 $ 29.00 $ 64,868 32,479,031 $ 15,000 $ 11.50 9.2174580075 3.36729583 11.0801102952 17.2961052493 9.6158054801 2.4423470354
15 Mar-14 10,393 $ 29.00 $ 65,114 32,505,480 $ 15,000 $ 11.40 9.2488517616 3.36729583 11.0838981785 17.2969192587 9.6158054801 2.4336133554
16 Apr-14 10,567 $ 29.00 $ 64,868 32,531,929 $ 20,000 $ 11.35 9.2655328119 3.36729583 11.0801102952 17.297732606 9.9034875525 2.4292177439
17 May-14 10,927 $ 30.00 $ 66,099 32,558,378 $ 20,000 $ 11.75 9.298966518 3.4011973817 11.0989078411 17.2985452924 9.9034875525 2.4638532406
18 Jun-14 10,730 $ 30.00 $ 67,330 32,584,827 $ 20,000 $ 12.00 9.2807746918 3.4011973817 11.117358549 17.2993573188 9.9034875525 2.4849066498
19 Jul-14 10,937 $ 30.00 $ 67,330 32,611,276 $ 25,000 $ 11.95 9.2999122593 3.4011973817 11.117358549 17.3001686863 10.1266311039 2.4807312784
20 Aug-14 11,408 $ 30.00 $ 68,807 32,637,725 $ 25,000 $ 11.75 9.3420896087 3.4011973817 11.1390592198 17.3009793961 10.1266311039 2.4638532406
21 Sep-14 10,822 $ 30.00 $ 69,321 32,664,174 $ 25,000 $ 11.85 9.2893505548 3.4011973817 11.1465090395 17.3017894491 10.1266311039 2.4723278676
22 Oct-14 11,607 $ 28.00 $ 69,580 32,690,623 $ 25,000 $ 11.85 9.359331405 3.3322045102 11.1502309302 17.3025988465 10.1266311039 2.4723278676
23 Nov-14 11,580 $ 28.00 $ 70,023 32,717,072 $ 25,000 $ 11.85 9.3570646008 3.3322045102 11.1565792622 17.3034075893 10.1266311039 2.4723278676
24 Dec-14 13,265 $ 28.00 $ 70,161 32,743,521 $ 30,000 $ 12.00 9.4929150617 3.3322045102 11.1585461074 17.3042156786 10.3089526606 2.4849066498
25 Jan-15 9,981 $ 31.99 $ 71,204 32,769,970 $ 30,000 $ 12.00 9.208434163 3.465423354 11.1733100536 17.3050231154 10.3089526606 2.4849066498
26 Feb-15 10,732 $ 31.99 $ 71,800 32,796,419 $ 30,000 $ 11.95 9.2809618516 3.465423354 11.1816392737 17.3058299007 10.3089526606 2.4807312784
27 Mar-15 10,728 $ 31.99 $ 72,396 32,822,868 $ 35,000 $ 11.75 9.280627505 3.465423354 11.1898996906 17.3066360357 10.4631033405 2.4638532406
28 Apr-15 11,008 $ 31.99 $ 72,991 32,849,317 $ 35,000 $ 11.85 9.3063936146 3.465423354 11.1980924316 17.3074415213 10.4631033405 2.4723278676
29 May-15 9,082 $ 33.99 $ 73,587 32,875,766 $ 20,000 $ 11.85 9.114059918 3.5260663637 11.2062185967 17.3082463587 9.9034875525 2.4723278676
30 Jun-15 8,887 $ 33.99 $ 74,182 32,902,215 $ 20,000 $ 11.85 9.092319578 3.5260663637 11.2142792591 17.3090505488 9.9034875525 2.4723278676
31 Jul-15 9,784 $ 33.99 $ 74,778 32,928,664 $ 20,000 $ 12.00 9.188455869 3.5260663637 11.2222754665 17.3098540927 9.9034875525 2.4849066498
32 Aug-15 9,535 $ 33.99 $ 75,373 32,955,113 $ 20,000 $ 11.40 9.162731174 3.5260663637 11.2302082414 17.3106569915 9.9034875525 2.4336133554
33 Sep-15 13,006 $ 24.50 $ 62,776 32,134,746 $ 15,000 $ 12.30 9.4731770482 3.1986731176 11.0473204723 17.2854484326 9.6158054801 2.5095992624
34 Oct-15 13,642 $ 24.50 $ 63,022 32,162,540 $ 15,000 $ 12.20 9.5209426616 3.1986731176 11.0512343717 17.2863129793 9.6158054801 2.5014359517
35 Nov-15 12,660 $ 24.50 $ 63,268 32,187,644 $ 15,000 $ 12.10 9.4461708131 3.1986731176 11.0551330121 17.2870932102 9.6158054801 2.4932054526
36 Dec-15 12,903 $ 24.50 $ 63,933 32,214,541 $ 15,000 $ 12.05 9.4651912167 3.1986731176 11.0655840458 17.2879285028 9.6158054801 2.4890646599
Average: 11,258 $ 29.19 $ 68,504 32,598,052 $ 22,917 $ 11.85

variable definitions

Variable Definitions
Q = number of TrickKits sold
P = price of TRickKits (in real 2015 $ per unit)
M = average after-tax household income (in real 2015 $)
N = national population
A = advertising expenditures (in real 2015 $)
P_h = price of HepaTest (in real 2015 $)

Notes

Notes on estimating empirical demand function
1: I have monthly data for the Jan 2013 to Dec 2015 period, 36 data points in all. That should be enough to obtain reasonable estimates of the various demand elasticities.
2: I have converted all prices, household incomes, and advertizing expenditues from nominal dollars into real 2015 dollars using Canadian consumer price index using the standard method. Page 372 of Thomas and & Maurice (11th edition) provides details of the method used.
3: Fortunately, TrickKits have only one real competitor in the market. That means we can treat MagnaMed as a price-setting firm when estimating the demand equation.
4: I'll intend to use the regression component of Excel's built-in data analysis tools to estimate the regression equations