Assignment:
1. A panel of admissions counselors for a college are interested in determining which predictors are the best in predicting college success (College_GPA.sav). They have narrowed it down the three variables which they believe are most informative to study (high school gpa, sat total, and quality of letters of recommendation). The corresponding data set is from a group of 100 students.
Run a Correlation analysis with all 4 of the variables in the data set: College GPA, High School GPA, SAT total, and Quality of Letters of Recommendation.
Run 2 simple linear regression analyses: One with High School GPA as the Independent Variable, the other with Quality of Letters of Recommendation as the Independent Variable. Both should have College GPA as the Dependent Measure.
Run a t-test to determine if the Mean SAT Score is significantly different from 1000. Additionally, compute a 95% Confidence Interval for the mean.
Run a dependent samples t-test that would determine if there is a significant change in GPA between High School and College.
2. My Niece, Kaela, is really interested in Science. As a "project", we decide to put ice in the microwave at different temperature settings to see how long it takes to melt. We use 6 settings: High, Medium High, Medium, Medium Low, Low and Off. The data are uploaded onto elearning under the filename Ice.sav.
Run a one-way ANOVA on the data file that would test for the differences between the 6 settings. Run a post-hoc Tukey test for the analysis also.
3. An educator conducted an experiment to test whether new directed reading activities in the classroom will help elementary school pupils improve some aspects of their reading ability. She arranged for a third grade class of 21 students to follow these activities for an 8-week period. A control classroom of 23 third graders followed the same curriculum without the activities. At the end of the 8 weeks, all students took a Degree of Reading Power (DRP) test, which measures the aspects of reading ability that the treatment is designed to improve. The data is included in the data file DRP_Scores.sav . Run an independent samples t-test that will test for differences between the treatment and control groups. Also include a 90% Confidence Interval for the mean difference.
4. Remember back to Tess Tastypop. She tasted Dr. Pepper 3 times and each time she had a probability of liking Dr. Pepper, which was 0.75. Each taste was independent, and we said that the count of the number of likes followed a Binomial Distribution. I observed Tess drinking the Dr. Pepper 3 times over 100 trials and recorded the number of likes for each trial. I ended up with the frequencies shown below. Run a chi-square analysis that will test the goodness of fit to the binomial distribution. The expected frequencies under the null hypothesis are a binomial (That is, the expected counts should use the probabilities of the number of likes that are calculated using the binomial formula). Therefore, in order to get the expected probabilities, you must calculate, P(X=0), P(X=1), P(X=2), and P(X=3) using a binomial with n=3 and p=.75. The data are given in Tess.sav
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# of Likes
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0
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1
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2
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3
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Count
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5
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15
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25
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55
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5. Do Males have Females have different voting tendencies? In a recent study, 1000 Men and Women were asked their Voting Preferences. The results are provided in the two-way contingency table below. Run a Chi-Square analysis that would determine if Gender and Voting Preferences are Independent. Data are provided in the file Vote.sav.
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Voting Preferences
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Gender
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Republican
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Democrat
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Independent
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Male
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200
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150
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50
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Female
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250
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300
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50
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