QRB 501 Week 3 DQs Ver 1

In this archive file of QRB 501 Week 3 Discussion Questions Ver 1 you will find the next info:

DQ 1: If someone had to create a data base, there are several descriptive statistics that should be included in the data base. They include a stem-and-leaf plot, box plot, mean, medium, mode, range, variance, standard deviation, frequency distribution, and a line, bar or pie chart. Given the following data collected from a final exam in statistics, create a data base. 15, 22, 27, 28, 34, 46, 55, 59, 65, 67, 69, 70, 72, 77, 78, 80, 82, 82, 84, 86, 88, 89, 90, 91, 92, 93, 94, 95, 96, 100. Report your numbers and cut and paste graphs and other important information into your post.
Stem-and-leaf plot.
DQ 2: When we have categorical or nominal data, we can assess if we have a significant deviation from the expected pattern. The following monthly data was obtained from a car dealer, model 1 sold 33, model 2 sold 30, model 3 sold 16, model 4 sold 64, model 5 sold 50 and model 6 sold 47 cars. If the dealer expected to sell 40 of each, is there a significant change from the expected pattern? Run a chi-square test using megastat and interpret the result.
DQ 3: Create a simple question about demand for a product you dream up. For example, your product might be a do-walking service. While we are not focused on how to write a good survey question in this discussion, you learn that writing and testing the question is really a science itself. For now, write the question in such a way that you can determine which of four price ranges customers would be willing to pay. After asking a few friends and family (which is not the best way to sample, but that is for a later class) to answer the question, create a table in Excel showing the possible answers and how many people selected each answer. Graph the information using a histogram and a scatter plot in Excel as well. How does seeing the information in a graphic form provide more information?
DQ 4: Create a table showing the size of a college in students and how many pizzas the local pizzeria sells in a year. Run a regression analysis looking for a linear trend. Show your data, your r, and your regression line by copying and pasting it into this forum. Interpret the correlation and the regression analysis.

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