1. ANOVA:

 

UMUC wants to use a new tutorial to teach the students about business ethics.  As an experiment the administrator randomly selected 15 students and randomly assigned them to one of three groups which include either a PowerPoint presentation created by the faculty, AuthorGen Presentation created by the faculty, or a well known tutorial by the ABC company.  After completing their assigned tutorial, the students are given a Business Ethics test.  At the .01 significance level, can UMUC conclude that there is a difference between how well the different tutorials work for the students?

Note: (Here is a good website where you can find the f table for significance level .01 http://www.socr.ucla.edu/Applets.dir/F_Table.html#FTable0.01)

 

PowerPoint Tutorial (X1)	AuthorGen Tutorial (X2)	ABC Tutorial (X3)
68	79	75
85	86	83
91	82	78
87	92	86
88	91	81

 

 

2. Pivot Table

 

ASPCA wanted to find out if there was a relationship between the type of animals preferred and the gender of the owner.  They gathered data from 50 randomly chosen pet owners who owned either one cat or one dog:

 

gender	type	gender	type
female	cat	male	cat
male	dog	female	cat
male	dog	male	cat
male	dog	female	dog
female	cat	female	cat
female	cat	female	dog
female	dog	male	cat
female	cat	male	dog
male	dog	male	dog
female	dog	male	dog
female	dog	male	cat
male	dog	female	dog
male	cat	female	cat
male	cat	male	dog
female	dog	female	cat
female	cat	female	cat
female	cat	male	cat
female	cat	female	cat
female	dog	male	dog
male	cat	female	dog
male	dog	male	cat
male	cat	female	dog
male	dog	male	cat
male	dog	male	cat
female	dog	male	dog

 

Use Excel’s capabilities to create a pivot table that shows the number of pets owned by each gender.

 

 

3. Chi Square:

 

Use the pivot table from problem 1 to find the Chi Square value and give the interpretation based on a .01 significance level.  Is there are relationship between the sex of the owner and the type of pet they prefer?

Here is a website with a chi square table:

http://sites.stat.psu.edu/~mga/401/tables/Chi-square-table.pdf

 

4. Chi Square:

 

A human resources director wants to find out if there is a relationship with the day of the week and the number of times employees were sick on that day.  She tallied the number of times 60 employees were absent on these days:

 

Monday: 12

Tuesday: 9

Wednesday: 11

Thursday: 10

Friday: 9

Saturday: 9

 

Using a significance level of .05 and the Excel function, =chiinv(), find the critical value.

 

1. What is the chi square value for this scenario?
2. Use the answer to b and the =chidist() equation in Excel to find the p value.
3. Is there a relationship between the days of the week and the number of employees who are sick on that day?

 

 

5. Regression analysis:

 

A CEO of a large pharmaceutical company would like to determine if he should be placing more money allotted in the budget next year for television advertising of a new drug marketed for controlling asthma. He wonders whether there is a strong relationship between the amount of money spent on television advertising for this new drug called XBC and the number of orders received. The manufacturing process of this drug is very difficult and requires stability so the CEO would prefer to generate a stable number of orders. The cost of advertising is always an important consideration in the phase I roll-out of a new drug. Data that have been collected over the past 20 months indicate the amount of money spent of television advertising and the number of orders received.

 

The use of linear regression is a critical tool for a manager’s decision-making ability. Please carefully read the example below and try to answer the questions in terms of the problem context. Here are the results of the data collection.

 

Month	Advertising cost (in thousands)	# of orders
1	$68.93	4,902,000
2	72.62	3,893,000
3	79.58	5,299,000
4	58.67	4,130,000
5	69.18	4,367,000
6	70.14	5,111,000
7	83.37	3,923,000
8	68.88	4,935,000
9	82.99	5,276,000
10	75.23	4,654,000
11	81.38	4,598,000
12	52.9	2,967,000
13	61.27	3,999,000
14	79.19	4,345,000
15	80.03	4,934,000
16	78.21	4,653,000
17	83.77	5,625,000
18	62.53	3,978,000
19	88.76	4,999,000
20	72.64	5,834,000

 

 

1. Set up a scatter diagram and calculate the associated correlation coefficient. Discuss how strong you think the relationship is between the amount of money spent on television advertising and the number of orders received. Please use the Scatterplot and Correlation procedures within Excel under Tools > Data Analysis
2. What is the regression equation?
3. Interpret the meaning of the slope, b1, in the regression.
4. Predict the monthly advertising cost when the # of orders is 5,100,000.
5. Compute the coefficient of determination, r2, and interpret its meaning.
6. Compute the standard error of estimate, and interpret its meaning.