/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Q102E Workplace bullying and intention... [FREE SOLUTION] | 91影视

91影视

Log out

Learning Materials

Features

Discover

Chapter 12: Q102E (page 772)

Workplace bullying and intention to leave. Refer to the Human Resource Management Journal (October 2008) study of workplace bullying, Exercise 12.91 (p. 765). Recall that multiple regression was used to model an employee鈥檚 intention to leave (y) as a function of bullying (x1, measured on a quantitative scale) and perceived organizational support (measured qualitatively as 鈥渓ow POS,鈥� 鈥渘eutral POS,鈥� or 鈥渉igh POS鈥�). In Exercise 12.91b, you wrote a model for E(y) as a function of bullying and POS that hypothesizes three parallel straight lines, one for each level of POS. In Exercise 12.91c, you wrote a model for E(y) as a function of bullying and POS that hypothesizes three nonparallel straight lines, one for each level of POS.

a) Explain why the two models are nested. Which is the complete model? Which is the reduced model?

b) Give the null hypothesis for comparing the two models.

c) If you reject H0 in part b, which model do you prefer? Why?

d) If you fail to reject H0 in part b, which model do you prefer? Why?

Short Answer

Expert verified

a) The two models are nested models because the first model is a reduced model because this model represents three parallel lines indicating no interaction. While the other model is a complete model representing three nonparallel lines denoting model with interaction hence added variables representing interaction amongst the variables.

b) The null and alternate hypothesis for comparing the two models can be written as H0: 尾4 = 尾5 = 0 while Ha: At least one of 尾 parameters are nonzero.

c) If H0 is rejected in part b, then model with interaction is preferred since the hypothesis testing indicates that the added variables from the interaction model is statistically useful for predicting E(y).

d) If H0 is not rejected in part b, then model without any interaction is preferred since the hypothesis testing indicates that the added variables from the interaction model is not statistically useful for predicting E(y).

Step by step solution

01

Nested and complete model

The two models are nested models because the first model is a reduced model because this model (Ey=尾0+尾1x1+尾2x2+尾3x3)represents three parallel lines indicating no interaction. While the other model (Ey=尾0+尾1x1+尾2x2+尾3x3+尾4x1x3+尾5x2x3

)is a complete model representing three nonparallel lines denoting model with interaction hence added variables representing interaction amongst the variables.

02

Hypotheses

The null and alternate hypothesis for comparing the two models can be written as

H0: 尾4 = 尾5 = 0while Ha: At least one of 尾 parameters are nonzero.

03

Interpretation of thesis testing

If H0 is rejected in part b, then model with interaction is preferred since the hypothesis testing indicates that the added variables from the interaction model is statistically useful for predicting E(y).

04

Clarification of theorem testing

If H0 is not rejected in part b, then model without any interaction is preferred since the hypothesis testing indicates that the added variables from the interaction model is not statistically useful for predicting E(y).

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91影视!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Consider relating E(y) to two quantitative independent variables x1 and x2.

  1. Write a first-order model for E(y).

  2. Write a complete second-order model for E(y).

Reality TV and cosmetic surgery. Refer to the Body Image: An International Journal of Research (March 2010) study of the impact of reality TV shows on a college student鈥檚 decision to undergo cosmetic surgery, Exercise 12.17 (p. 725). Recall that the data for the study (simulated based on statistics reported in the journal article) are saved in the file. Consider the interaction model, , where y = desire to have cosmetic surgery (25-point scale), = {1 if male, 0 if female}, and = impression of reality TV (7-point scale). The model was fit to the data and the resulting SPSS printout appears below.

a.Give the least squares prediction equation.

b.Find the predicted level of desire (y) for a male college student with an impression-of-reality-TV-scale score of 5.

c.Conduct a test of overall model adequacy. Use a= 0.10.

d.Give a practical interpretation of R2a.

e.Give a practical interpretation of s.

f.Conduct a test (at a = 0.10) to determine if gender (x1) and impression of reality TV show (x4) interact in the prediction of level of desire for cosmetic surgery (y).

Suppose you used Minitab to fit the model y=尾0+尾1x1+尾2x2+蔚

to n = 15 data points and obtained the printout shown below.

  1. What is the least squares prediction equation?

  2. Find R2and interpret its value.

  3. Is there sufficient evidence to indicate that the model is useful for predicting y? Conduct an F-test using 伪 = .05.

  4. Test the null hypothesis H0: 尾1= 0 against the alternative hypothesis Ha: 尾1鈮� 0. Test using 伪 = .05. Draw the appropriate conclusions.

  5. Find the standard deviation of the regression model and interpret it.

Question: Shared leadership in airplane crews. Refer to the Human Factors (March 2014) study of shared leadership by the cockpit and cabin crews of a commercial airplane, Exercise 8.14 (p. 466). Recall that simulated flights were taken by 84 six-person crews, where each crew consisted of a 2-person cockpit (captain and first officer) and a 4-person cabin team (three flight attendants and a purser.) During the simulation, smoke appeared in the cabin and the reactions of the crew were monitored for teamwork. One key variable in the study was the team goal attainment score, measured on a 0 to 60-point scale. Multiple regression analysis was used to model team goal attainment (y) as a function of the independent variables job experience of purser (x1), job experience of head flight attendant (x2), gender of purser (x3), gender of head flight attendant (x4), leadership score of purser (x5), and leadership score of head flight attendant (x6).

a. Write a complete, first-order model for E(y) as a function of the six independent variables.

b. Consider a test of whether the leadership score of either the purser or the head flight attendant (or both) is statistically useful for predicting team goal attainment. Give the null and alternative hypotheses as well as the reduced model for this test.

c. The two models were fit to the data for the n = 60 successful cabin crews with the following results: R2 = .02 for reduced model, R2 = .25 for complete model. On the basis of this information only, give your opinion regarding the null hypothesis for successful cabin crews.

d. The p-value of the subset F-test for comparing the two models for successful cabin crews was reported in the article as p 6 .05. Formally test the null hypothesis using 伪 = .05. What do you conclude?

e. The two models were also fit to the data for the n = 24 unsuccessful cabin crews with the following results: R2 = .14 for reduced model, R2 = .15 for complete model. On the basis of this information only, give your opinion regarding the null hypothesis for unsuccessful cabin crews.

f. The p-value of the subset F-test for comparing the two models for unsuccessful cabin crews was reported in the article as p < .10. Formally test the null hypothesis using 伪 = .05. What do you conclude?

Question: Suppose the mean value E(y) of a response y is related to the quantitative independent variables x1and x2

E(y)=2+x1-3x2-x1x2

a. Identify and interpret the slope forx2.

b. Plot the linear relationship between E(y) andx2forx1=0,1,2, where.

c. How would you interpret the estimated slopes?

d. Use the lines you plotted in part b to determine the changes in E(y) for each x1=0,1,2.

e. Use your graph from part b to determine how much E(y) changes when3猢�x1猢�5and1猢�x2猢�3.
See all solutions

Recommended explanations on Math Textbooks

Logic and Functions

Read Explanation

Calculus

Read Explanation

Discrete Mathematics

Read Explanation

Pure Maths

Read Explanation

Probability and Statistics

Read Explanation

Geometry

Read Explanation
View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.