Soc 201 - Second Exam

 Person Favorite Food Weight Exercise Speed Ann Pizza 120 Fast Kay Cake 160 Slow Bill Pizza 140 Very Fast Joe Pizza 140 Slow Dan Pizza 160 Fast Dave Cake 120 Fast Sue Pizza 120 Very Fast Mel Cake 140 Slow

Use the above data to answer all problems on this exam which require data to answer them (except 12 - 15). You may assume this is a random sample from a larger population. Show all computations on the last 5 problems; just giving the answer without showing how will not count. Be sure to answer all 15 problems.

Name_______________________________

1. Why or why not use Gamma to answer this question: "What is the relationship between weight and exercise speed in this sample?".
a. Yes you can; this is the ideal statistic.
b. Yes you can except it is wasteful to treat weight as ordinal.
c. No you cannot because Gamma is not inferential
d. No you cannot because the variables are not dichotomous and discrete
e. No you cannot because you do not know which variable is independent and which is dependent.

2. Why or why not use Chi Square to answer this question: "Can you generalize the association between food and weight to the whole population?"
a. No you cannot because Chi Square does not measure association.

b. Yes you can except it is wasteful to treat weight as nominal.
c. Yes you can, it is the ideal statistic.
d. No you cannot because the fe must be 5 or larger.
e. No you cannot because weight is not nominal.

3. Why or why not use the two sample T-test of means to answer this question, "Can you generalize the difference in average weights of males and females to the whole population from which the sample was drawn?'
a. Yes, it is ideal.
b. Yes, except it is wasteful to treat weight as nominal.
c. No, because the sample totals are not 100 or more.
d. No, because the two sample T-test of means does not measure the significant differences of average weight.
e. None of the above

4. Why or why not use the Phi Coefficient to answer this question: "What is the relationship between exercise speed and weight.
a. No you cannot because the variables are not dichotomous and discrete.
b. Yes you can except it is wasteful to treat these data as nominal.
c. Yes, you can this is the ideal statistic.
d. No you cannot because you need an Fe of 5 or more.
e. None of the above.

5. Why or why not asymmetrical lambda to answer this question: "What is the relationship between favorite food and weight when you are trying to predict weight from a knowledge of favorite food.
a. Yes you can; it is the ideal statistic.
b. No you cannot because you do not know which variable is dependent and which is independent.
c. No you cannot because the variables are not discrete.
d. Yes you can except it is wasteful to treat weight as nominal.
e. No you cannot because you do not have two nominal variables.

6. Interpret a Phi coefficient of -.17 for this table:

 Vote Sex Demo Rep F M

a. Small positive association between being female and voting Democrat.
b. Small negative association between sex and vote.
c. 17% more disagreement than agreement if the rank order of sex and vote.
d. p is greater than .05.
e. Small negative association between being female and voting Democrat.

7. How would you interpret a Chi Square of 26.22 for a table with 3 rows and 8 columns?
a. The chances of being wrong if you generalize from the sample to the whole population are less than 1 in 100.
b. p = .05
c. p = .01
d. p is less than 5 in 100.
e. You cannot generalize from the sample to the whole population.

8. How would you interpret a Gamma of -.82 for weight and exercise speed?
a. 82% more agreement than disagreement in the rank order of weight and exercise.
b. p is greater than .05.
c. Large positive relationship between weight and exercise speed.
d. 82% more disagreement than agreement in the rank order of exercise speed and weight.
e. 82% improvement when predicting both variables simultaneously from a knowledge of each other.

9. How would you interpret an asymmetrical lambda of .35 when weight is dependent and exercise speed is independent?
a. Moderately small negative relationship between weight and exercise.
b. 35% improvement when you are trying to predict both variables simultaneously from a knowledge of each other.
c. 35% more agreement than disagreement in the rank order of weight and exercise.
d. 35% improvement when predicting weight from a knowledge of exercise.
e. 35% improvement when predicting exercise speed from a knowledge of weight.

10. How would you interpret the result of 2.63 from a 2 sample t-test of means for the hypothesis, "Males are heavier than females among the whole population from which this sample was drawn."
a. You cannot generalize from the sample to the population.
b. The chance of being wrong if you generalize is 5%.
c. The chance of being wrong if you generalize is less than 5%.

d. The chance of being wrong if you generalize is 1%.

e. The chance of being wrong if you generalize is less than 1%.

11. Arrange the data in the proper form and compute gamma for the relationship between weight and exercise speed.

12 Compute Chi square for the following table:

 Exercise Speed Food Very Fast Fast Slow row totals Pizza 11 15 9 35 Cake 21 32 12 65 column totals 32 47 21 100

13. Combine categories and collapse the following table in an effort to make it meet the assumptions for Chi Square. Do not collapse it anymore than is absolutely necessary. Do not compute Chi Square just collapse.

 Income Party 10 7 6 5 4 row total Demo 16 Rep 40 Peace 4 Comm 35 Ind 5 col. totals 32 10 20 18 20 100

14 & 15. Using the table below compute the mean and standard deviation. Then, using the one sample T-test of means, see if it is legitimate to generalize to the whole SDSU population. is 22.3 (the mean of the whole population)

 Age to nearest birthday f 19-20 28 21-22 35 23-24 22 25-26 15

extra credit: do a 95% Confidence Interval on the above data's mean.