Intro to Linear Regression
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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Question 1 of 8
1. Question
Which of the following statements best describe the line of best fit for a simple linear regression?
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Question 2 of 8
2. Question
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:
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Question 3 of 8
3. Question
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:
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Question 4 of 8
4. Question
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:
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Question 5 of 8
5. Question
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:
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Question 6 of 8
6. Question
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:
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Question 7 of 8
7. Question
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:CorrectIncorrect -
Question 8 of 8
8. Question
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:
CorrectIncorrect
