Suppose I want to know something about the study habits of undergraduate college students. I collect a random sample of 200 students and find that they spend ...
Write the null and alternative hypotheses. Ho: μx = μy Note that we need to specify here that the X population is fathers and the Y. H1: μx < μy population is sons.
Suppose that Starbuck's wanted to conduct a study to determine whether men and women differ in the ... researcher to collect these data from the first 100 customers to visit one of their stores in ... Please answer the following questions based on th
Calculate a Cohen's d statistic and interpret it. Would this be considered a small, moderate, or medium effect size? First, we need to convert the standard error ...
randomly selected samples of this size would be expected to produce a t value of ... Larger sample sizes produce smaller standard errors of the mean, assuming.
and has being doing after the Fukushima. Nuclear Disaster. He used his personal. Twitter account as an effective medium to distribute the data he collected from.
Answers To Chapter 14. â« Review Questions. 1. Answer a. 15. 0.10. 15 135 ... 5. Answer d. Labor markets are inherently dynamic, resulting in continual flows ... Answer b. The supply and demand model suggests that unemployment (a surplus ...Missing:
year assuming there is no change in the size of the adult non-institutionalized population. 2. In the small community of Highville, the aggregate labor supply ...
cartoon of the processes occurring, the student has a greater chance of remembering ... They then should draw an appropriate cartoon showing the mediating ...
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Work Problems Chapter 14 Suppose I want to know whether there are differences in the likelihood of being diagnosed with depression for people who live in different types of communities (urban, suburban, rural). I collect data from people from these three different types of communities and get the data summarized in Table 14.11. Use this data to answer the following set of questions.
Table 14.11. Raw data of depressed and not depressed people living in urban, rural, and suburban areas.
1. Calculate the expected values for each of the 6 cells in the table.
2. Calculate the sum of squared differences between the observed and expected values to find the observed chi-square value.
2.50 + .60 + 2.96 + .71 + .14 + .03 = 6.94. This is the chi-square value (χ2 = 6.94.) 3. Report the degrees of freedom (df) for this problem. R = 2 and C = 3, so df = (2 – 1)(3 – 1) = 2. 4. Using the df you just calculated, and an alpha level of .05, find the critical value for the chi-square statistic in Appendix E. The critical χ2 = 5.99 with df = 2 and alpha level of .05. 5. Compare the critical value from Appendix E with the observed chi-square value that you calculated in question #2 and decide whether your observed value is statistically significant. The observed χ2 = 6.94 and the critical χ2 = 5.99. Because the observed value is larger than the critical value, our chi-square statistic is statistically significant. 6. What does the chi-square statistic that you calculated tell you? What doesn’t it tell you? Because our observed chi-square statistic is statistically significant, we know that some of our observed frequencies differ from the expected frequencies. In suburban areas, it appears that the proportion of depressed and non-depressed people is about what would be expected by chance. In urban areas, it appears that there are more depressed and fewer non-depressed people than we would expect by chance. This pattern is reversed in the rural areas. Here are two more questions that are not based on the data presented above: 7. Explain when you would use a non-parametric test rather than a parametric test. Non-parametric tests are better than parametric tests when the data do not form a normal distribution and, sometimes, when the scales of measurement on the variables are nominal and/or ordinal rather than interval/ratio. 8. Suppose that in a large company, there is an allegation of gender bias in who
receives promotions and who does not. Explain how the chi-square test of independence compares observed and expected frequencies to determine whether this allegation is true. In this example, there would be four groups, or cells of a table: Women who received promotions, women who did not, men who received promotions, and men who did not. Using the actual data, we would first determine the observed frequencies for each of these four groups. Then, using the total number of each gender and the total number of promoted vs. non-promoted, we could calculate the expected frequencies for each cell. For example, if half of all employees were men and half of all employees received a promotion, we would expect, by chance alone, that one half of all women received promotions and the other half did not. By comparing the observed frequencies with the expected frequencies, and using the differences between the two to calculate a chisquare statistic, we can determine whether one gender is more or less likely than chance to have received a promotion.