What to know
Identify the rule, formula, or decision criterion before reading the answer choices. CFA Level I distractors often use the right vocabulary with the wrong condition.
Quantitative Methods
Simple Linear Regression practice questions
Simple Linear Regression is part of CFA Level I Quantitative Methods. Quantitative Methods questions emphasize time value of money, probability, sampling, hypothesis testing, regression, and return statistics. Use this page to review the controlling ideas, then work through 9 questions with answer explanations and common traps.
Review the worked explanations before moving into adaptive practice. The app version can mix this topic with due reviews and weak related concepts.
Practice this topicHow to practice
Work each item under time pressure, then compare your reasoning with the step-by-step explanation and key takeaway.
Review signal
Missed questions should become scheduled reviews when the error comes from a concept gap, formula setup, or answer-choice trap.
Quantitative Methods
Simple Linear Regression
In the regression equation Return = 1.5 + 0.8 x MarketReturn, the slope coefficient is best interpreted as:
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Simple Linear Regression
If actual Y is 12 and predicted Y is 9, the residual is:
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Simple Linear Regression
An R-squared of 0.64 in simple linear regression indicates that:
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Simple Linear Regression
A regression slope estimate is 0.65 with standard error 0.20. The t-statistic for testing whether the slope equals zero is closest to:
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Simple Linear Regression
A simple regression has SSE = 180 and n = 12 observations. The standard error of estimate is closest to:
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Simple Linear Regression
A simple regression has SSR = 75, SSE = 25, and n = 12. The F-statistic for overall fit is closest to:
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Simple Linear Regression
A prediction interval for a new dependent variable observation is wider than a confidence interval for the mean dependent variable at the same X value primarily because the prediction interval includes:
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Simple Linear Regression
A residual plot shows residuals with increasing spread as the independent variable rises. The regression assumption most likely violated is:
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Simple Linear Regression
In a log-log regression ln(Sales) = 2.0 + 1.2 ln(Advertising), the slope coefficient is best interpreted as:
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