/*! 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} 122E Emotional intelligence and team ... [FREE SOLUTION] | 91影视

91影视

Log out

Learning Materials

Features

Discover

Chapter 12: 122E (page 802)

Emotional intelligence and team performance. Refer to the Engineering Project Organizational Journal (Vol. 3, 2013) study of the relationship between emotional intelligence of individual team members and their performance during an engineering project, Exercise 12.33 (p. 733). Using data on n = 23 teams, you fit a first-order model for mean project score (y) as a function of range of interpersonal scores (x1), range of stress management scores (x2), and range of mood scores (x3). The regression results, as well as a correlation matrix for the independent variables, are displayed in the accompanying Minitab printout. Do you detect any signs of multicollinearity in the data? [Note: The researchers expect the linear relationship between project score and each independent variable to be negative for range of interpersonal scores and positive for both range of stress management scores and mood scores.]

Short Answer

Expert verified

Since the r coefficient values for all three parameters are less than 0.2, it can be concluded that there is a low level of multicollinearity amongst the variables. And the signs of 尾 values for all three independent variables are as expected by the researchers

Step by step solution

01

Multicollinearity check

Multicollinearity is checked by checking the correlation amongst the independent variables. If there is a high correlation amongst any two independent variables, it is said that the problem of multicollinearity exists in the model.

02

Application of multicollinearity check

In this question, stress management and the interpersonal score have a correlation of 0.083, mood score and interpersonal score have a correlation of 0.201, and stress management and mood score have a correlation of -0.194. Since the r coefficient values for all three parameters are less than 0.2, it can be concluded that there is a low level of multicollinearity amongst the variables. And the signs of 尾 values for all three independent variables are as expected by the researchers (negative for interpersonal scores and positive for both ranges of stress management scores and mood scores).

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

Buy-side vs. sell-side analysts鈥� earnings forecasts. Refer to the Financial Analysts Journal (July/August 2008) comparison of earnings forecasts of buy-side and sell-side analysts, Exercise 2.86 (p. 112). The Harvard Business School professors used regression to model the relative optimism (y) of the analysts鈥� 3-month horizon forecasts. One of the independent variables used to model forecast optimism was the dummy variable x = {1 if the analyst worked for a buy-side firm, 0 if the analyst worked for a sell-side firm}.

a) Write the equation of the model for E(y) as a function of type of firm.

b) Interpret the value of尾0in the model, part a.

c) The professors write that the value of尾1in the model, part a, 鈥渞epresents the mean difference in relative forecast optimism between buy-side and sell-side analysts.鈥� Do you agree?

d) The professors also argue that 鈥渋f buy-side analysts make less optimistic forecasts than their sell-side counterparts, the [estimated value of尾1] will be negative.鈥� Do you agree?

The Minitab printout below was obtained from fitting the modely=尾0+尾1x1+尾2x2+尾3x1x2+蔚to n = 15 data points.

a) What is the prediction equation?

b) Give an estimate of the slope of the line relating y to x1 when x2 =10 .

c) Plot the prediction equation for the case when x2 =1 . Do this twice more on the same graph for the cases when x2 =3 and x2 =5 .

d) Explain what it means to say that x1and x2interact. Explain why your graph of part c suggests that x1and x2interact.

e) Specify the null and alternative hypotheses you would use to test whetherx1andx2interact.

f)Conduct the hypothesis test of part e using 伪=0.01.

State casket sales restrictions. Some states permit only licensed firms to sell funeral goods (e.g., caskets, urns) to the consumer, while other states have no restrictions. States with casket sales restrictions are being challenged in court to lift these monopolistic restrictions. A paper in the Journal of Law and Economics (February 2008) used multiple regression to investigate the impact of lifting casket sales restrictions on the cost of a funeral. Data collected for a sample of 1,437 funerals were used to fit the model. A simpler version of the model estimated by the researchers is E(y)=尾0+尾1x1+尾2x2+尾3x1x2, where y is the price (in dollars) of a direct burial, x1 = {1 if funeral home is in a restricted state, 0 if not}, and x2 = {1 if price includes a basic wooden casket, 0 if no casket}. The estimated equation (with standard errors in parentheses) is:

y^=1432 + 793x1- 252x2+ 261x1x2, R2= 0.78

(70) (134) (109)

  1. Calculate the predicted price of a direct burial with a basic wooden casket at a funeral home in a restricted state.

  2. The data include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,200. Assuming the standard deviation of the model is \)50, is this data value an outlier?

  3. The data also include a direct burial funeral with a basic wooden casket at a funeral home in a restricted state that costs \(2,500. Again, assume that the standard deviation of the model is \)50. Is this data value an outlier?

Question: Accuracy of software effort estimates. Periodically, software engineers must provide estimates of their effort in developing new software. In the Journal of Empirical Software Engineering (Vol. 9, 2004), multiple regression was used to predict the accuracy of these effort estimates. The dependent variable, defined as the relative error in estimating effort, y = (Actual effort - Estimated effort)/ (Actual effort) was determined for each in a sample of n = 49 software development tasks. Eight independent variables were evaluated as potential predictors of relative error using stepwise regression. Each of these was formulated as a dummy variable, as shown in the table.

Company role of estimator: x1 = 1 if developer, 0 if project leader

Task complexity: x2 = 1 if low, 0 if medium/high

Contract type: x3 = 1 if fixed price, 0 if hourly rate

Customer importance: x4 = 1 if high, 0 if low/medium

Customer priority: x5 = 1 if time of delivery, 0 if cost or quality

Level of knowledge: x6 = 1 if high, 0 if low/medium

Participation: x7 = 1 if estimator participates in work, 0 if not

Previous accuracy: x8 = 1 if more than 20% accurate, 0 if less than 20% accurate

a. In step 1 of the stepwise regression, how many different one-variable models are fit to the data?

b. In step 1, the variable x1 is selected as the best one- variable predictor. How is this determined?

c. In step 2 of the stepwise regression, how many different two-variable models (where x1 is one of the variables) are fit to the data?

d. The only two variables selected for entry into the stepwise regression model were x1 and x8. The stepwise regression yielded the following prediction equation:

Give a practical interpretation of the 尾 estimates multiplied by x1 and x8.

e) Why should a researcher be wary of using the model, part d, as the final model for predicting effort (y)?

Question: Adverse effects of hot-water runoff. The Environmental Protection Agency (EPA) wants to determine whether the hot-water runoff from a particular power plant located near a large gulf is having an adverse effect on the marine life in the area. The goal is to acquire a prediction equation for the number of marine animals located at certain designated areas, or stations, in the gulf. Based on past experience, the EPA considered the following environmental factors as predictors for the number of animals at a particular station:

X1 = Temperature of water (TEMP)

X2 = Salinity of water (SAL)

X3 = Dissolved oxygen content of water (DO)

X4 = Turbidity index, a measure of the turbidity of the water (TI)

x5 = Depth of the water at the station (ST_DEPTH)

x6 = Total weight of sea grasses in sampled area (TGRSWT)

As a preliminary step in the construction of this model, the EPA used a stepwise regression procedure to identify the most important of these six variables. A total of 716 samples were taken at different stations in the gulf, producing the SPSS printout shown below. (The response measured was y, the logarithm of the number of marine animals found in the sampled area.)

a. According to the SPSS printout, which of the six independent variables should be used in the model? (Use 伪 = .10.)

b. Are we able to assume that the EPA has identified all the important independent variables for the prediction of y? Why?

c. Using the variables identified in part a, write the first-order model with interaction that may be used to predict y.

d. How would the EPA determine whether the model specified in part c is better than the first-order model?

e.Note the small value of R2. What action might the EPA take to improve the model?
See all solutions

Recommended explanations on Math Textbooks

Decision Maths

Read Explanation

Discrete Mathematics

Read Explanation

Probability and Statistics

Read Explanation

Applied Mathematics

Read Explanation

Mechanics Maths

Read Explanation

Logic and Functions

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.