'Marketing Research – Final Exam Review Essay\r'

'wholes 1-2\r\nOne question depart be drawn from the following. This is the only tangible you need to know from the first both units except for material that has carried over into Unit 3. For instance, things like response types, population, sample, sampling distribution, and so forth were covered in Unit 2. These concepts atomic number 18 important to understanding the Unit 3 material, so you need to know them.\r\nstudying accredited organizations is some times the most effectual direction to understand some merchandising research concepts. In this course, class material has been illustrated through over fifty examples of real organizations.\r\nMost of the examples and matters have been covered in the first twain exams. These possible wide dissolver questions address examples and cases that have not been coveredâ€there aren’t that umteen of that haven’t been covered!\r\n1. In the Diageo Captain Morgan Gold case, what did management learn to do and wh erefore? (4 pts) What was the outcome, and wherefore did it glide by? (4 pts) What is the main lesson to take away from the case? (2 pts)\r\n2. In the cloth vs. disposable diapers case, cover the background and results of the two studies. (8 pts) What lesson does this illustrate nearly using secondary data for trade research? (2 pts)\r\n3. In the Whirlpool case, what did market research studies show, and what did management decide to do? (6 pts) While management make a mistake in hindsight, their reasoning made sense from the production sideâ€why? (2 pts) in that location are several takeaway lessons from this case. Name one and only(a). (2 pts)\r\nUnit 3 †There is only one possible long answer question, and here it is:\r\nPart 1\r\nDo people in in the altogether York, moolah, Los Angeles, and Houston spend the comparable average mensuration on furniture from each one year, or are there differences between the cities? To answer this, a furniture company self-po ssessed data from people in the iv cities. The supervisor proposes that they compare each fit of cities. So they would compare NYC vs. Chicago, NYC vs. LA, NYC vs. Houston, Chicago vs. LA, Chicago vs. Houston, and LA vs. Houston. If any of those pairs reveals a hearty difference with 95% confidence (i.e., you potful be 95% confident that the two groups are different), then they can conclude that the cities are not all the same.\r\na. Briefly, why isn’t this a good way to analyze the data? (5 pts) The problem with data track 6 pair tests is that there is unagitated a 5% lot that the z- apprise we calculate will be a fluke that leads to a treat conclusion. For each calculation done, there is an increased knock of error, thus we are six times more likely to get the wrong conclusion. This gives you a total of 1-(95/100) ^6 = 0.265 = 26.5% chance of improperly rejecting at least one of your six calculations.\r\nb. What is a better regularity? You only need to give the relate of the method. (2 pts)\r\nThe better method to use is called compend of variance aka ANOVA\r\nPart 2\r\nWhen conducting a chi-square test, the expected frequencies are able to\r\n(Row total x Column total) ÷ kB total\r\nHow is this locution derived from mathematical and chance rules? Be detailed. If it helps to explain it by referring to an literal table, you can use the table below. (10 pts) | This formula is derived by each individual get being assigned to each other individual amount. The probability of being in run-in A is A/E = 150/253 = .5929 = 59.29% The probability of being in column C is C/E = 135/253 = .5336 = 53.36% Thus when mathematically combining the probability of being in row A and column C is A/E x C/E = 150/253 x 135/253 = (150Ã135)/253 = 80.04 which is the same as\r\nB\r\nD\r\nC\r\nA\r\nE\r\n.5929 x .5336 = .3164 x 253 = 80.04\r\n'