Problem 1: A study was conducted at a major corporation to examine the attitudes of its employees with regard to two items. The first item of interest was whether there was a relationship between an employee's job type and their opinions on developing self-managed work teams. The company was considering a move toward team tasks rather than individualized tasks. The second item of interest was to determine if there was a relationship between an employee's job type and their opinion on adding one additional vacation day per month, but without pay. The results of the study are presented below. The first contingency table contains the data regarding work teams, and the second deals with vacation days.

Your task as a statistical analyst is to decide whether there was a significant relationship between the variables. You will need to carry out two separate tests-one for each table.

1. Is there a significant relationship between job type and opinions on self-managed work teams? Begin by reporting your findings using plain, non-statistical language. Explain as though you're speaking to a member of management who knows nothing of statistical analyses.
2. Is there a significant relationship between job type and opinions on adding a vacation day? Begin by reporting your findings using plain, non-statistical language. Explain as though you're speaking to a member of management who knows nothing of statistical analyses.
3. After examining the Megastat results and the data above, make an informed suggestion to the decision-making management. For example, you might think it wise to use self-managed work teams for one group but not another, You will probably want to consider conditional probabilities to make these determinations.
4. Next, discuss how you came to your decision (in #1 and #2) statistically. In this section, it is advisable to discuss hypotheses, P-values, etc.
5. Next, include all relevant statistical output (from Megastat). It should be included for anyone who may want to review the information you discussed in section #4.

PROBLEM #2: Your next analysis deals with examining relationships between various variables describing nations from around the world. You will need to refer to the CD Excel formatted dataset called "International2003.xls". (The dataset is also available on blackboard). You are specifically interested if there is a relationship between a nation's GDP per capita and the literacy rate of the country. The thinking is that we may be able to predict a country's literacy rate from its GDP.

1. First, obtain the correlation between the two variables of interest, and report this value. If there is a significant correlation at the 0.05 level of significance, you may want to obtain a scatterplot and regression fit. Carry out this procedure and include all relevant computer output.
2. Interpret the slope of the regression line IN TERMS OF THIS PARTICULAR PROBLEM. Explain it so that it makes sense to someone who knows now statistics.
3. Use your regression equation to predict the literacy rate of a country whose GDP per capita is $14,000.
4. What percent of the variability in literacy can be explained by the relationship with GDP? Based on this, do you think your line makes good predictions?
5. Now choose 2 other variables you think might be linearly related. Explain why you think they might be related in this way: Then, carry out a correlation and regression analysis on these 2 variables. Include all relevant statistical output from your analysis.
6. Were your suspicions correct? In other words, is there a significant linear relationship between the variables you chose?

PROBLEM #3: A carpet manufacturer is studying differences between 2 of its major outlet stores. The company is particularly interested in the time it takes customers to receive carpeting that was ordered from the plant. Data concerning a sample of delivery times for the most popular type of carpet are summarized as follows:

You would like to formally test to see if there is a significant difference in the average delivery times for the two stores. Using the 0.05 level of significance, test to see if there is in fact a significant difference in average delivery times for stores \(\mathrm{A}\) and \(\mathrm{B}\).

1. State what your findings mean. Explain it to the owner of the company and safely assume he knows no statistics. If there is a significant difference, which store seems to be doing better?
2. Report all relevant statistical output from Megastat.

PROBLEM #4: An experiment was conducted to examine how effective three different trial drugs would be in reducing cholesterol levels. 18 people participated in the experiment that involved 6 people being placed on drug \(X\), 6 people on drug \(Y\), and 6 people on drug \(Z\). After 2 months on the drug, each person's reduction in cholesterol was recorded. The table below records these values (For example, the 22 in the first cell represents a 22 point reduction in total cholesterol for the first individual to take drug X). Your job is to see if a significant difference exists between the three groups' average reduction levels. If a significant difference exists, explain which drug (or drugs) stand out as being most effective.

Price: $29.63

Solution: The downloadable solution consists of 14 pages, 1563 words and 3 charts.
Deliverable: Word Document