Results Assessment Workbook

Use this workbook after running the HBAT-style case in SmartPLS.

Part 1: Linear Regression Model

Model:

X19 <- X6 + X7 + X9 + X11 + X12

Overall Fit

Item SmartPLS Result Interpretation
F-test or ANOVA significance
R-square
Adjusted R-square
Durbin-Watson

Collinearity

Predictor VIF Condition Index Note
X6
X7
X9
X11
X12

Coefficients

Predictor b beta t p 95% CI Interpretation
X6
X7
X9
X11
X12

Assumptions

Assumption SmartPLS Evidence Decision
Linearity Predicted vs residual; predicted vs actual
Homoscedasticity Predicted vs residual; Breusch-Pagan
Independence Residual autocorrelation; Durbin-Watson
Normality QQ plot; residual histogram
Collinearity VIF; condition index

Part 2: Logistic Regression Model

Model:

X4 <- X6 + X7 + X8 + X9 + X10 + X11 + X12 + X13 + X14 + X15 + X16 + X17 + X18

Fit Summary

Item Null Model Estimated Model Interpretation
Log-likelihood
Deviance
AIC
BIC

Pseudo R-Square

Measure Value Interpretation
McFadden's R-square
Cox and Snell's R-square
Nagelkerke's R-square

Classification

Classification Item Value Interpretation
Correctly classified group 0
Correctly classified group 1
Overall classification accuracy

Logistic Coefficients

Predictor Coefficient Wald p Odds Ratio Interpretation
X6
X7
X8
X9
X10
X11
X12
X13
X14
X15
X16
X17
X18

Final Interpretation Prompts

  1. Which predictors matter most in the linear model?
  2. Does the linear model explain a meaningful amount of satisfaction variance?
  3. Which logistic predictors significantly separate region groups?
  4. Are odds ratios interpreted correctly?
  5. What limitations should be stated?