Chi Square and Regression
This assignment includes conceptual and computations problems regarding regression as well as SPSS. Note that each problem has several parts. For full credit, please respond to each part completely and show all your work. Take a picture and insert your work for all hand computation which are noted with the following icon Insert them in the place noted. Do not included them only at the end and do not submit them in a separate file. Submissions must be in PDF or word format – no other formats will be accepted for credit.
DO NOT change the formatting or the numbering of the exam. Keep all questions and point values on this document. Simply respond in the places noted.
Note that this assignment should be completed individually. Think of this assignment as a “take-home quiz.” Therefore, Do not discuss this assignment with anyone other than your professor. When complete submit an electronic copy on Bb.
TYPE all of your answers in addition to the pictures of your work.
- A store owner is trying to decide whether he should order equal amounts of four types of milk (skim, 1%, 2%, and whole). To help determine if certain kinds of milk are more popular, he records how many gallons of each kind of milk he sells over the course of a week. His data shows that he sold: 31 gallons of skim milk, 23 gallons of 1% milk, 37 gallons of 2% milk, and 25 gallons of whole milk. The owner is interested in whether the store sells equal amounts of the four types of milk.
Compute a Goodness of Fit Chi Square. Report and interpret your results including the value of chi square, degrees of freedom and statistical and practical significance.
df = k – 1
Where = chi square, fo = frequency observed, fe = frequency expected, ϕ = phi (measure of practical significance)
Show your work here (for full credit you must show your work for all steps – type in below or attach a picture). Also show your work for the computation of phi. 10 points :
|
fo |
fe |
|
| Skim milk |
|
|
|
| 1% milk |
|
|
|
| 2% milk |
|
|
|
| Whole milk |
|
|
|
| Sum |
|
|
|
Write out your interpretation including frequencies for all groups and use the following format for the reporting of the results, χ² () = , p .05, Φ =) to report your results. 10 points
- Santos et al. (1994) described the pique technique. They claimed that people are more likely to comply with strange requests than with “typical” ones, even if the strange request is larger.
To test this hypothesis, a researcher asked 160 strangers for money. He asked some people if they could spare a quarter, and asked others if they could spare 37 cents. He then recorded how many people gave him the amount requested and how many people did not.
Were people more likely to give the researcher 37 cents than they were to give him 25 cents?
Compute a Chi Square Test of Independence. Report and interpret your results including the value of chi square, degrees of freedom and statistical and practical significance.
Observed Frequencies
Show your work here (type in row totals, column totals and grand total here):
(for full credit you must show your work for all steps – type in below or attach a picture)
|
YES – Did give the $ |
NO – Did not give the $ |
Row Total |
| Asked for 25 cents |
18 |
42 |
|
| Asked for 37 cents |
54 |
46 |
|
| Column Total |
|
|
Grand Total = |
Show your work here (for full credit you must show your work for all steps – type in below or attach a picture). Also show your work for the computation of phi. 10 points :
Expected Frequencies (For each cell compute the following: row total x column total/grand total)
|
YES – Did give the $ |
NO – Did not give the $ |
| Asked for 25 cents |
|
|
| Asked for 37 cents |
|
|
df = (ka – 1)(kb – 1)
(for full credit you must show your work for all steps – type in below or attach a picture)
|
YES – Did give the $ |
NO – Did not give the $ |
| Asked for 25 cents |
|
|
| Asked for 37 cents |
|
|
Write out your interpretation including frequencies for all groups and use the following format for the reporting of the results, χ² () = , p .05, Φ =) to report your results. 10 points
- A researcher wants to assess whether students’ level of prejudice predict attitudes toward racial profiling. As part of a larger survey, students complete two scales pertaining to those variables. Higher scores on the prejudice measure indicate greater prejudice and higher scores on the profiling scale indicate greater support for racial profiling. Scores on both measures are obtained from each of 20 students.
|
Prejudice |
Profiling |
|
|
|
X |
Y |
XY |
X2 |
|
7 |
5 |
35 |
49 |
|
4 |
4 |
16 |
16 |
|
5 |
3 |
15 |
25 |
|
6 |
7 |
42 |
36 |
|
2 |
2 |
4 |
4 |
|
3 |
4 |
12 |
9 |
|
6 |
7 |
42 |
36 |
|
4 |
5 |
20 |
16 |
|
8 |
6 |
48 |
64 |
|
9 |
8 |
72 |
81 |
|
6 |
6 |
36 |
36 |
|
5 |
3 |
15 |
25 |
|
4 |
4 |
16 |
16 |
|
6 |
5 |
30 |
36 |
|
2 |
4 |
8 |
4 |
|
6 |
8 |
48 |
36 |
|
5 |
4 |
20 |
25 |
|
7 |
8 |
56 |
49 |
|
8 |
4 |
32 |
64 |
|
7 |
9 |
63 |
49 |
| Sum |
110 |
106 |
630 |
676 |
| Mean |
5.5 |
5.3 |
|
|
Insert one picture for work for both c and d here and Type your answers below
- Identify the predictor. 2 points
- Identify the criterion. 2 points
- Compute the regression line. 20 points
- Using the regression line – identify the predicted profiling score for a person with a score of 8 for prejudice. 4 points
- Peterson is interested in assessing whether self-esteem predicts reading ability. The data is as follows:
| Self Esteem |
Reading Ability |
| 4 |
13 |
| 6 |
10 |
| 7 |
16 |
| 8 |
13 |
| 10 |
17 |
| 11 |
12 |
| 13 |
14 |
| 13 |
17 |
- Identify the predictor variable (IV) 1 point
- Identify the criterion variable (DV) 1 point
- Compute the Regression Analysis in SPSS using the data provided on self esteem and reading ability. Paste your output here (you will need to use a snipping tool to copy from SPSS, it will not allow you to copy and paste). Output has several parts – it must include: variables entered/removed, model summary, ANOVA, ad coefficients tables. 5 points
- Report results in sentence format – see handout instructions on what to include and how to format it. 5 points
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