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Accuracy of software effort estimates. Refer to the Journal of Empirical Software Engineering (Vol. 9, 2004) study of the accuracy of new software effort estimates, Exercise 12.114 (p. 781). Recall that stepwise regression was used to develop a model for the relative error in estimating effort (y) as a function of company role of estimator (x1 = 1 if developer, 0 if project leader) and previous accuracy (x8 = 1 if more than 20% accurate, 0 if less than 20% accurate). The stepwise regression yielded the prediction equationy^=0.12-0.28x1+0.27x8. The researcher is concerned that the sign of the estimated β multiplied by x1 is the opposite from what is expected. (The researcher expects a project leader to have a smaller relative error of estimation than a developer.) Give at least one reason why this phenomenon occurred.

Short Answer

Expert verified

The estimated sign for β for x1 is positive (the developer has a larger relative error of estimation than a project leader) but the prediction equation estimated using step-wise regression is negative sign of x1. A possible reason for the same could be the existence of multicollinearity in the model.

Step by step solution

01

Reason for opposite sign

The estimated sign for β for x1 is positive (the developer has a larger relative error of estimation than a project leader) but the prediction equation estimated using step-wise regression is negative sign of x1. A possible reason for the same could be the existence of multicollinearity in the model.

02

Rationale behind opposite sign

Since there is high degree of correlation amongst relative error in estimating effort (y) and company role of estimator (x1) the β estimates will not be true parameter indicators and will be biased.

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Most popular questions from this chapter

Question: 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 one’s desire to undergo cosmetic surgery, Exercise 12.17 (p. 725). Recall that psychologists used multiple regression to model desire to have cosmetic surgery (y) as a function of gender(x1) , self-esteem(x2) , body satisfaction(x3) , and impression of reality TV (x4). The SPSS printout below shows a confidence interval for E(y) for each of the first five students in the study.

  1. Interpret the confidence interval for E(y) for student 1.
  2. Interpret the confidence interval for E(y) for student 4

Question:Consider the first-order model equation in three quantitative independent variables E(Y)=2-3x1+5x2-x3

  1. Graph the relationship between Y and x3for x1=2 and x2=1
  2. Repeat part a for x1=1and x2=-2
  3. How do the graphed lines in parts a and b relate to each other? What is the slope of each line?
  4. If a linear model is first-order in three independent variables, what type of geometric relationship will you obtain when is graphed as a function of one of the independent variables for various combinations of the other independent variables?

Cooling method for gas turbines. Refer to the Journal of Engineering for Gas Turbines and Power (January 2005) study of a high-pressure inlet fogging method for a gas turbine engine, Exercise 12.19 (p. 726). Recall that you fit a first-order model for heat rate (y) as a function of speed (x1) , inlet temperature (x2) , exhaust temperature (x3) , cycle pressure ratio (x4) , and airflow rate (x5) . A Minitab printout with both a 95% confidence interval for E(y) and prediction interval for y for selected values of the x’s is shown below.

a. Interpret the 95% prediction interval for y in the words of the problem.

b. Interpret the 95% confidence interval forE(y)in the words of the problem.

c. Will the confidence interval for E(y) always be narrower than the prediction interval for y? Explain.

Question: Cooling method for gas turbines. Refer to the Journal of Engineering for Gas Turbines and Power (January 2005) study of a high-pressure inlet fogging method for a gas turbine engine, Exercise 12.19 (p. 726). Consider a model for heat rate (kilojoules per kilowatt per hour) of a gas turbine as a function of cycle speed (revolutions per minute) and cycle pressure ratio. The data are saved in the file.

a. Write a complete second-order model for heat rate (y).

b. Give the null and alternative hypotheses for determining whether the curvature terms in the complete second-order model are statistically useful for predicting heat rate (y).

c. For the test in part b, identify the complete and reduced model.

d. The complete and reduced models were fit and compared using SPSS. A summary of the results are shown in the accompanying SPSS printout. Locate the value of the test statistic on the printout.

e. Find the rejection region for α = .10 and locate the p-value of the test on the printout.

f. State the conclusion in the words of the problem.


Question: Consider the model:

y=β0+β1x1+β2x2+β3x3+ε

where x1 is a quantitative variable and x2 and x3 are dummy variables describing a qualitative variable at three levels using the coding scheme

role="math" localid="1649846492724" x2=1iflevel20otherwisex3=1iflevel30otherwise

The resulting least squares prediction equation is y^=44.8+2.2x1+9.4x2+15.6x3

a. What is the response line (equation) for E(y) when x2 = x3 = 0? When x2 = 1 and x3 = 0? When x2 = 0 and x3 = 1?

b. What is the least squares prediction equation associated with level 1? Level 2? Level 3? Plot these on the same graph.

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