Are job applicants with easy to pronounce last names just as likely to get called for an interview than applicants with difficult to pronounce last names. 574 job applications were sent out with last names that are easy to pronounce and 780 identical job applications were sent out with names that were difficult to pronounce. 359 of the "applicants" with easy to pronounce names were called for an interview while 501 of the "applicants" with difficult to pronounce names were called for an interview. What can be concluded at the 0.01 level of significance? For this study, we should use Select an answer z-test for a population proportion t-test for a population mean t-test for the difference between two dependent population means t-test for the difference between two independent population means z-test for the difference between two population proportions The null and alternative hypotheses would be: H0:H0: Select an answer p1 μ1 Select an answer ≠ = < > Select an answer p2 μ2 (please enter a decimal) H1:H1: Select an answer p1 μ1 Select an answer < ≠ > = Select an answer μ2 p2 (Please enter a decimal) The test statistic ? t z = (please show your answer to 3 decimal places.) The p-value = (Please show your answer to 4 decimal places.) The p-value is ? ≤ > αα Based on this, we should Select an answer accept reject fail to reject the null hypothesis. Thus, the final conclusion is that ... The results are statistically significant at αα = 0.01, so there is sufficient evidence to conclude that among all possible applicants, there is a differnece in the population proportion of callbacks for applicants with easy to pronounce last names and applicants with difficult to pronounce names. The results are statistically insignificant at αα = 0.01, so we can conclude that the population proportion of people with easy to pronounce names who get called for an interview is equal to the population proportion of people with difficult to pronounce names who get called for an interview. The results are statistically significant at αα = 0.01, so there is sufficient evidence to conclude that the proportion of the 574 applicants with easy to pronounce names who got called for an interview is not the same as the proportion of the 780 applicants with difficult to pronounce names who got called for an interview. The results are statistically insignificant at αα = 0.01, so there is insufficient evidence to conclude that among all possible applicants, there is a differnece in the population proportion of callbacks for applicants with easy to pronounce last names and applicants with difficult to pronounce names. Interpret the p-value in the context of the study. There is a 52.4% chance that percent of callbacks for applicants with easy to pronounce names and those with difficult to pronounce names differ by at least 1.7%. If the population proportion of callbacks for applicants with easy to pronounce last names is the same as the population proportion of callbacks for applicants with difficult to pronounce last names and if another 574 applications with easy to pronounce names and 780 applications with difficult to pronounce names are submitted then there would be a 52.4% chance that the percent of callbacks for the sample of applicants with easy to pronounce names and the percent of callbacks for the sample of applicants with difficult to pronounce names would differ by at least 1.7%. There is a 52.4% chance of a Type I error. If the sample proportion of applicants with easy to pronounce names who receive a callback is the same as the sample proportion of applicants with difficult to pronounce names who receive a callback and if another another 574 applications with easy to pronounce names and 780 applications with difficult to pronounce names are submitted then there would be a 52.4% chance of concluding that the percent callbacks for applicants with easy to pronounce names and applicants with difficult to pronounce names differ by at least 1.7% .

Are job applicants with easy to pronounce last names just as likely to get called for an interview than applicants with difficult to pronounce last names. 574 job applications were sent out with last names that are easy to pronounce and 780 identical job applications were sent out with names that were difficult to pronounce. 359 of the "applicants" with easy to pronounce names were called for an interview while 501 of the "applicants" with difficult to pronounce names were called for an interview. What can be concluded at the 0.01 level of significance? For this study, we should use Select an answer z-test for a population proportion t-test for a population mean t-test for the difference between two dependent population means t-test for the difference between two independent population means z-test for the difference between two population proportions The null and alternative hypotheses would be: H0:H0: Select an answer p1 μ1 Select an answer ≠ = < > Select an answer p2 μ2 (please enter a decimal) H1:H1: Select an answer p1 μ1 Select an answer < ≠ > = Select an answer μ2 p2 (Please enter a decimal) The test statistic ? t z = (please show your answer to 3 decimal places.) The p-value = (Please show your answer to 4 decimal places.) The p-value is ? ≤ > αα Based on this, we should Select an answer accept reject fail to reject the null hypothesis. Thus, the final conclusion is that ... The results are statistically significant at αα = 0.01, so there is sufficient evidence to conclude that among all possible applicants, there is a differnece in the population proportion of callbacks for applicants with easy to pronounce last names and applicants with difficult to pronounce names. The results are statistically insignificant at αα = 0.01, so we can conclude that the population proportion of people with easy to pronounce names who get called for an interview is equal to the population proportion of people with difficult to pronounce names who get called for an interview. The results are statistically significant at αα = 0.01, so there is sufficient evidence to conclude that the proportion of the 574 applicants with easy to pronounce names who got called for an interview is not the same as the proportion of the 780 applicants with difficult to pronounce names who got called for an interview. The results are statistically insignificant at αα = 0.01, so there is insufficient evidence to conclude that among all possible applicants, there is a differnece in the population proportion of callbacks for applicants with easy to pronounce last names and applicants with difficult to pronounce names. Interpret the p-value in the context of the study. There is a 52.4% chance that percent of callbacks for applicants with easy to pronounce names and those with difficult to pronounce names differ by at least 1.7%. If the population proportion of callbacks for applicants with easy to pronounce last names is the same as the population proportion of callbacks for applicants with difficult to pronounce last names and if another 574 applications with easy to pronounce names and 780 applications with difficult to pronounce names are submitted then there would be a 52.4% chance that the percent of callbacks for the sample of applicants with easy to pronounce names and the percent of callbacks for the sample of applicants with difficult to pronounce names would differ by at least 1.7%. There is a 52.4% chance of a Type I error. If the sample proportion of applicants with easy to pronounce names who receive a callback is the same as the sample proportion of applicants with difficult to pronounce names who receive a callback and if another another 574 applications with easy to pronounce names and 780 applications with difficult to pronounce names are submitted then there would be a 52.4% chance of concluding that the percent callbacks for applicants with easy to pronounce names and applicants with difficult to pronounce names differ by at least 1.7% .

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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For this study, we should use Select an answer z-test for a population proportion t-test for a population mean t-test for the difference between two dependent population means t-test for the difference between two independent population means z-test for the difference between two population proportions

The null and alternative hypotheses would be:

H0:H0: Select an answer p1 μ1 Select an answer ≠ = < > Select an answer p2 μ2 (please enter a decimal)

H1:H1: Select an answer p1 μ1 Select an answer < ≠ > = Select an answer μ2 p2 (Please enter a decimal)

The test statistic ? t z = (please show your answer to 3 decimal places.)
The p-value = (Please show your answer to 4 decimal places.)
The p-value is ? ≤ > αα
Based on this, we should Select an answer accept reject fail to reject the null hypothesis.
Thus, the final conclusion is that ...
- The results are statistically significant at αα = 0.01, so there is sufficient evidence to conclude that among all possible applicants, there is a differnece in the population proportion of callbacks for applicants with easy to pronounce last names and applicants with difficult to pronounce names.
- The results are statistically insignificant at αα = 0.01, so we can conclude that the population proportion of people with easy to pronounce names who get called for an interview is equal to the population proportion of people with difficult to pronounce names who get called for an interview.
- The results are statistically significant at αα = 0.01, so there is sufficient evidence to conclude that the proportion of the 574 applicants with easy to pronounce names who got called for an interview is not the same as the proportion of the 780 applicants with difficult to pronounce names who got called for an interview.
- The results are statistically insignificant at αα = 0.01, so there is insufficient evidence to conclude that among all possible applicants, there is a differnece in the population proportion of callbacks for applicants with easy to pronounce last names and applicants with difficult to pronounce names.
Interpret the p-value in the context of the study.
- There is a 52.4% chance that percent of callbacks for applicants with easy to pronounce names and those with difficult to pronounce names differ by at least 1.7%.
- If the population proportion of callbacks for applicants with easy to pronounce last names is the same as the population proportion of callbacks for applicants with difficult to pronounce last names and if another 574 applications with easy to pronounce names and 780 applications with difficult to pronounce names are submitted then there would be a 52.4% chance that the percent of callbacks for the sample of applicants with easy to pronounce names and the percent of callbacks for the sample of applicants with difficult to pronounce names would differ by at least 1.7%.
- There is a 52.4% chance of a Type I error.
- If the sample proportion of applicants with easy to pronounce names who receive a callback is the same as the sample proportion of applicants with difficult to pronounce names who receive a callback and if another another 574 applications with easy to pronounce names and 780 applications with difficult to pronounce names are submitted then there would be a 52.4% chance of concluding that the percent callbacks for applicants with easy to pronounce names and applicants with difficult to pronounce names differ by at least 1.7% .

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