2. (Nine parts; 30 marks in total) The Psychology of Waiting in Line: A study wa
ID: 3053290 • Letter: 2
Question
2. (Nine parts; 30 marks in total) The Psychology of Waiting in Line: A study was conducted to determine the level of customers' annoyance while waiting in line for service, as affected by the number of people ahead of them. Also, the study tried to determine whether the number of people waiting behind them has a psychological effect of reducing the annoyance level. A random sample of 16 people who had already been waited in line for 10 minutes at a certain bank were asked to rate their level of negative feelings (annoyance, anxiety, etc.) in order to calculate an index. The number of people ahead of them and the number behind them were recorded and an interaction term was calculated. Based on the following partial computer output from multiple linear regression analysis, answer parts (a) - (i) below. Model Summary Adjusted R R Square Square Std. Error of the Estimate Model .947a a. Predictors: (Constant), Interaction, Number_ahead, Number_behind b. Dependent Variable: Annoyance ANOVA Sig. Sum of Model Squares Mean Square Regression 25.529 Residual 2.909 Total 28.438 a. Dependent Variable: Annoyance b. Predictors: (Constant), Interaction, Number_ahead, Number_behind Standardized Coefficients Beta Sig. Coefficients Unstandardized Coefficients Model Std. Error (Constant) 1.286 .705 Number_ahead .275 .058 Number_behind -.196 Interaction .003 .006 a. Dependent Variable: Annoyance .093 .000 .081 .032Explanation / Answer
a) Completeing the ANOVA table we get
df : degrees of freedom for regression is 3 , total df = 15(16 observations) , MSE is calculated by SS/df .
F value = MSE Regression/MSE Residual = 35
From table at 5% signifiacance level F value (3,12) = 3.49
Hence F-calculated is > Ftable so Regression is usefule in explaining the relationship
b)Unadjusted R-Square = Sqr(0.947)=0.8968 Hence 89.68% of variance in annoyance level is explained by Regression model.
c) Adjusted R Square = 1- (1-Rsquare)(N-1)/(N-k-1) where n no of samples, k no of independent variable,
hence by claclculation : Adj R Square = 1- ((1-Rsquare)(N-1)/(N-k-1))==1-((1-0.897)*(16-1)/(16-3-1))=0.8712
Hence 87.12% of variance in annoyance level is explained by Regression model.
Sum of Square df MSE F Regression 25.529 3 8.509667 35.10347 Residual 2.909 12 0.242417 Total 28.438 15Related Questions
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.