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 β 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)?
