Gaucher, Friesen, and Kay (2010) found that masculine-themed words (such ascompetitive, independent, analyze, strong) are commonly used in job recruitmentmaterials, especially for job advertisements in male-dominated areas. In a similarstudy, a researcher counted the number of masculine-themed words in jobadvertisements for job areas, and obtained the following data.Area Number of Masculine WordsPlumber 14Electrician 12Security guard 17Bookkeeper 9Nurse 6Early-childhood educator 7Determine what kind of graph would be appropriate for showing this distribution andsketch the frequency distribution graph.

Gaucher, Friesen, and Kay (2010) found that masculine-themed words (such ascompetitive, independent, analyze, strong) are commonly used in job recruitmentmaterials, especially for job advertisements in male-dominated areas. In a similarstudy, a researcher counted the number of masculine-themed words in jobadvertisements for job areas, and obtained the following data.Area Number of Masculine WordsPlumber 14Electrician 12Security guard 17Bookkeeper 9Nurse 6Early-childhood educator 7Determine what kind of graph would be appropriate for showing this distribution andsketch the frequency distribution graph.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section: Chapter Questions

Problem 13PT

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A Pew Research study conducted in 2017 found that approximately 75% of Americans believe that robots and computers might one day do many of the jobs currently done by people.† Suppose we have the following data collected from nurses, tax auditors, and fast-food workers in which a higher score means the person feels his or her job is more likely to be automated. Nurse TaxAuditor Fast-FoodWorker 3 4 5 4 5 8 6 4 6 2 4 8 3 6 5 3 3 6 4 5 6 5 3 4 (a) Use ? = 0.05 to test for differences in the belief that a person's job is likely to be automated for the three professions. State the null and alternative hypotheses. H0: μNurse = μTax auditor = μFast-food worker Ha: μNurse ≠ μTax auditor ≠ μFast-food worker H0: At least two of the population means are equal. Ha: At least two of the population means are different. H0: Not all the population means are equal. Ha: μNurse = μTax auditor = μFast-food worker H0: μNurse = μTax auditor = μFast-food worker Ha:…
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