This post lesson quiz is to help anchor what you have just learnt and to give you some practise. The questions may not be structured like the kind you are likely to get in the exam.
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Which of the following statements best describe the line of best fit for a simple linear regression?
Trisha wishes to determine the relationship between the quarterly sales surprise of a company (X) and the company’s quarterly stock return (Y). After fitting a simple linear regression, it is observed that the standard deviation of the error term increases with higher levels of sales surprise. The data is said to exhibit:
Minsu is an engineer who ran a simple linear regression on a sample size of 33 to explain the variation in battery life against ambient temperature. The sample standard deviation of battery life was 1.625 hours, and the explained variation was 30.2.
(a) The coefficient of determination for the model is closest to:
Minsu is an engineer who ran a simple linear regression on a sample size of 33 to explain the variation in battery life against ambient temperature. The sample standard deviation of battery life was 1.625 hours, and the explained variation was 30.2.
(b) The standard error of estimate for the model is closest to:
Minsu is an engineer who ran a simple linear regression on a sample size of 33 to explain the variation in battery life against ambient temperature. The sample standard deviation of battery life was 1.625 hours, and the explained variation was 30.2.
(c) The F-statistic of the regression is closest to:
Trinh Nguyen, CFA believes that there is an exponential relation between long-term stock return (R) and gross profit margin (GPM) in the pharmaceutical industry, fitting in the following model: lnR = b0 + b1 x GPM. He collected a sample of long-term returns for 12 pharma companies and their average gross margins for the corresponding periods, and obtained the following regression results:
Source of Variation | DF | Sum of Sq | Mean Sum of Squares |
---|---|---|---|
Regression | 1 | 95.5 | 95.5 |
Residual | 10 | 28.9 | 2.89 |
Coefficient | Std Error | t-statistic | p-value | |
---|---|---|---|---|
Intercept | 1.85 | 1.047 | 1.767 | ~0.06 |
GPM | 0.0082 | 0.0029 | 2.828 | ~0.02 |
(a) Based on t-statistic, at 5% significance, Nguyen should conclude that:
Trinh Nguyen, CFA believes that there is an exponential relation between long-term stock return (R) and gross profit margin (GPM) in the pharmaceutical industry, fitting in the following model: lnR = b0 + b1 x GPM. He collected a sample of long-term returns for 12 pharma companies and their average gross margins for the corresponding periods, and obtained the following regression results:
Source of Variation | DF | Sum of Sq | Mean Sum of Squares |
---|---|---|---|
Regression | 1 | 95.5 | 95.5 |
Residual | 10 | 28.9 | 2.89 |
Coefficient | Std Error | t-statistic | p-value | |
---|---|---|---|---|
Intercept | 1.85 | 1.047 | 1.767 | ~0.06 |
GPM | 0.0082 | 0.0029 | 2.828 | ~0.02 |
(Critical value at 5% level: F=4.96)
(b) Based on F-statistic, at 5% significance, Nguyen should conclude that:
Trinh Nguyen, CFA believes that there is an exponential relation between long-term stock return (R) and gross profit margin (GPM) in the pharmaceutical industry, fitting in the following model: lnR = b0 + b1 x GPM. He collected a sample of long-term returns for 12 pharma companies and their average gross margins for the corresponding periods, and obtained the following regression results:
Source of Variation | DF | Sum of Sq | Mean Sum of Squares |
---|---|---|---|
Regression | 1 | 95.5 | 95.5 |
Residual | 10 | 28.9 | 2.89 |
Coefficient | Std Error | t-statistic | p-value | |
---|---|---|---|---|
Intercept | 1.85 | 1.047 | 1.767 | ~0.06 |
GPM | 0.0082 | 0.0029 | 2.828 | ~0.02 |
(c) Based on the model, the predicted long term return for a company with an average gross profit margin of 40% is closest to:
Many years ago, I was exactly where you are today—a CFA Level I candidate juggling a demanding full-time career with the daunting CFA curriculum. Coming from a Computer Engineering background, finance was entirely new territory for me. And yes, it was tough!
I struggled with dense textbooks, late-night cramming, and the frustration of concepts that seemed impossible after a long workday. But after passing Level I (barely), I realized something had to change.
Using the Pareto Principle (80/20 rule), I distilled the vast CFA syllabus into essential, easy-to-understand nuggets. I leaned into visual summaries and bite-sized learning sessions that worked around my busy schedule. This smarter approach helped me clear Levels II and III on my first attempts with significantly less stress.
I founded PrepNuggets to share the streamlined strategies and innovative learning methods that transformed my CFA journey. Our mission is simple: leverage technology to make CFA prep more effective, accessible, and enjoyable.
Join the PrepNuggets community today—sign up for your free account, and let our thoughtfully crafted materials propel you toward CFA success without unnecessary overwhelm.
Here’s to your CFA journey!
Keith Tan, CFA
Founder & Chief Instructor, PrepNuggets
Keith is the founder and chief instructor of PrepNuggets. He has a wide range of interests in all things related to tech, from web development to e-learning, gadgets to apps. Keith loves exploring different cultures and the untouched gems around the world. He currently lives in Singapore but frequently travels to share his knowledge and expertise with others.
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