SAS Statistical Business Analysis Regression and Modeling (A00-240) Certification Exam Sample Questions

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SAS A00-240 Sample Questions:

01. A hospital analyst fits a multiple regression predicting length of stay (days) from patient age and number of comorbidities using PROC REG:
Parameter Estimates Variable DF Estimate StdErr t Value Pr > |t| Intercept 1 1.20 0.55 2.18 0.0312 Age 1 0.018 0.012 1.50 0.1361 Comorbidities 1 0.95 0.21 4.52 <.0001
Using alpha = 0.05, which predictor is NOT a statistically significant predictor of length of stay?
a) Comorbidities
b) Age
c) The Intercept
d) Both Age and Comorbidities are non-significant.
 
02. A quality engineer compares average defect counts per shift across three production lines using PROC GLM. The ANOVA table is:
Source DF SS MS F Pr > F Line 2 240.0 120.0 ? 0.0072 Error 27 540.0 20.0 Corrected Total 29 780.0
What is the correct F value for the Line effect?
a) 0.17
b) 20.0
c) 6.00
d) 120.0
 
03. A telecom analyst is choosing among SELECTION=FORWARD, SELECTION=BACKWARD, and SELECTION=STEPWISE in PROC LOGISTIC to build a churn model from 20 candidate predictors.
Which of these correctly describes a limitation of forward selection relative to stepwise selection?
a) Forward selection can add a variable but never re-evaluates and removes it later, even if it becomes redundant once other variables are added, whereas stepwise selection re-tests previously entered variables at every step.
b) Forward selection requires the model to start with all candidate variables, making it infeasible when the number of predictors exceeds the sample size, attributing backward elimination's full-model starting point to forward selection instead.
c) Forward selection cannot be used with a binary response variable and is restricted to continuous outcomes modeled in PROC REG.
d) Forward selection always produces a model identical to backward elimination because both use the same default significance level.
 
04. PROC LOGISTIC fits a cumulative logit model to an ordinal customer-satisfaction response (Low < Medium < High) under the proportional odds assumption.
What does this assumption specifically require?
a) The response categories must be numerically equally spaced.
b) All odds ratios in the model must equal exactly 1.
c) Each response category has its own separate, independently estimated set of predictor slopes, the defining feature of a generalized (nominal) logit model, more suited to an unordered set of categories than the ordinal scale used here.
d) The effect (slope) of each predictor on the log-odds of being at or below a given category is the same across all category cutpoints, so only the intercepts differ.
 
05. After fitting a logistic regression to predict defective manufacturing units, an engineer reports the model's misclassification rate computed on the same data used to fit the model, and concludes the model is ready for production.
Which statement correctly critiques this approach?
a) The engineer's approach is valid as long as the training sample size is large enough, since sample size alone eliminates the need for a validation partition.
b) Computing performance on the training data is preferred, because it uses the maximum amount of information available and therefore gives the most reliable estimate, treating a larger evaluation sample size as though it directly offsets the optimism built into in-sample resubstitution error.
c) Reporting performance on the training data, in-sample or resubstitution performance, tends to overstate how well the model will perform on new units; an honest assessment requires evaluating performance on a held-out validation or test partition not used to fit the model.
d) Misclassification rate is not a valid metric for any logistic regression model, regardless of which data partition it is computed on.
 
06. A credit default model uses CLASS Region with either PARAM=EFFECT (effect coding) or PARAM=REF (reference coding) in PROC LOGISTIC.
How does effect coding differ from reference coding in the interpretation of the resulting parameter estimates?
a) Reference coding cannot be used with the CLASS statement; only effect coding can.
b) Under effect coding, each level's estimate reflects its deviation from the overall (unweighted) mean effect across all levels, whereas reference coding expresses each level's deviation from one specified reference level.
c) Effect coding requires the response variable to be continuous rather than binary.
d) Effect coding and reference coding always produce identical parameter estimates and odds ratios for every level, an equivalence assumed to hold because both encoding schemes are thought to merely relabel the same underlying contrasts without changing any numeric values.
 
07. An airline analyst fits a regression predicting flight delay (minutes) from a weather severity index and a day-of-week score:
Parameter Estimates Variable DF Estimate StdErr t Value Pr > |t| WeatherSeverity 1 4.20 0.47 8.94 <.0001 DayOfWeekScore 1 0.15 0.30 0.50 0.6190
Using alpha = 0.05, what action is best supported by this evidence?
a) Keep both predictors, since larger coefficient estimates always indicate stronger real-world relationships.
b) Remove WeatherSeverity, since its estimate (4.20) is much larger and therefore more likely to be biased upward, treating coefficient magnitude alone as a proxy for the presence of measurement or specification bias.
c) Remove both predictors, since a multiple regression containing any non-significant term is invalid overall.
d) Consider removing DayOfWeekScore from the model, since its coefficient is not statistically distinguishable from zero, while retaining WeatherSeverity.
 
08. Two candidate disease-screening models are compared. Model A shows a higher likelihood-based fit statistic on the training data than Model B, but Model B has a lower (better) misclassification rate on the validation partition for detecting disease-positive patients.
Which model should be selected as the champion?
a) Model A, since its stronger training fit will generalize equally well
b) Model A, since a higher in-sample fit statistic is definitionally the better model, treating training-data fit as the final word on model quality even when a validation-partition comparison is available.
c) Model B, because champion selection should be based on honest, validation-partition performance rather than in-sample training fit
d) Whichever model was fit first, to preserve a stable analysis baseline
 
09. A marketing analyst fits a multiple regression predicting campaign response rate (%) from ad spend and email frequency (emails sent per week). The estimate for EmailFrequency is -0.45 (Pr > |t| = 0.02).
Holding ad spend constant, how should the analyst interpret the EmailFrequency coefficient of -0.45?
a) Each additional email sent per week is associated with a predicted 0.45-percentage-point decrease in response rate, holding ad spend constant.
b) Email frequency has no meaningful relationship with response rate since its coefficient is negative, treating a negative sign as equivalent to a Pr > |t| result well above the 0.05 threshold.
c) The coefficient indicates email frequency and response rate are not linearly related.
d) A 0.45-unit increase in email frequency decreases response rate by one percentage point.
 
10. In a hospital readmission model, a particular lab result is missing for 20% of patients, and the analyst suspects patients are more likely to be missing this lab result specifically because they were considered lower-risk and it was never ordered.
Which approach best preserves this information rather than discarding it?
a) Combine the lab-result variable with an unrelated demographic variable into a single composite score to avoid handling the missing values directly.
b) Replace every missing lab result with a value of zero, since zero is always a statistically neutral placeholder that carries no information about missingness, treating zero as though it always fell outside the lab result's normal clinical range regardless of the specific test being recorded.
c) Discard the lab-result variable entirely from the model, since any variable with missing values must be excluded no matter how informative it may be.
d) Create a binary missingness-indicator variable flagging whether the lab result was recorded, alongside an imputed value for the lab result itself, so the model can capture information carried by the missingness pattern.

Answers:

Question: 1 Answer: b Question: 2 Answer: c
Question: 3 Answer: a Question: 4 Answer: d
Question: 5 Answer: c Question: 6 Answer: b
Question: 7 Answer: d Question: 8 Answer: c
Question: 9 Answer: a Question: 10 Answer: d

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