Practice Quizzes
Work through these practice quizzes to review key concepts from each section of the book. The questions are a mixture of multiple choice, multiple response, true/false, and calculation.
-
Q1.1: Identifying and summarizing data
-
Q1.2: Population distributions
-
Q1.3: Selecting individuals at random – probability
-
Q1.4: Random sampling
-
Q1.5: Interval estimation
-
Q1.6: Hypothesis testing
-
Q1.7: Random errors and prediction
-
Q2.1: SLR probability model for X and Y
-
Q2.2: SLR least squares criterion
-
Q2.3.1: SLR model evaluation – regression standard error
-
Q2.3.2: SLR model evaluation – coefficient of determination, R-squared
-
Q2.3.3: SLR model evaluation – slope parameter
-
Q2.4: SLR model assumptions
-
Q2.5: SLR model interpretation
-
Q2.6: SLR estimation and prediction
-
Q3.1: MLR probability model for (X1, X2, …) and Y
-
Q3.2: MLR least squares criterion
-
Q3.3.1: MLR model evaluation – regression standard error
-
Q3.3.2: MLR model evaluation – coefficient of determination, R-squared
-
Q3.3.3: MLR model evaluation – global usefulness test
-
Q3.3.4: MLR model evaluation – nested model test
-
Q3.3.5: MLR model evaluation – individual parameter tests
-
Q3.4: MLR model assumptions
-
Q3.5: MLR model interpretation
-
Q3.6: MLR estimation and prediction
-
Q4.1.1: Natural logarithm transformation for predictors
-
Q4.1.2: Polynomial transformation for predictors
-
Q4.1.3: Reciprocal transformation for predictors
-
Q4.1.4: Natural logarithm transformation for the response
-
Q4.1.5: Transformations for the response and predictors
-
Q4.2: Interactions
-
Q4.3.1: Qualitative predictors with two levels
-
Q4.3.2: Qualitative predictors with three or more levels
-
Q5.1.1: Influential points – outliers
-
Q5.1.2: Influential points – leverage
-
Q5.1.3: Influential points – Cook’s distance
-
Q5.2.1: Regression pitfalls – nonconstant variance
-
Q5.2.2: Regression pitfalls – autocorrelation
-
Q5.2.3: Regression pitfalls – multicollinearity
-
Q5.2.4: Regression pitfalls – excluding important predictor variables
-
Q5.2.5: Regression pitfalls – overfitting
-
Q5.2.6-8: Regression pitfalls – extrapolation, missing data, power and sample size
-
Q5.4: Model selection
-
Q5.5: Model interpretation using graphics