1|Page
**MGT 6203 MIDTERM EXAM – PART 1 & PART
2**DATA ANALYTICS FOR BUSINESS | GEORGIA
TECH | ONLINE MS IN ANALYTICS (OMSA)**200+
PRACTICE QUESTIONS WITH VERIFIED ANSWERS &
DETAILED RATIONALES**HIGH-YIELD CONTENT •
FIRST-TIME PASS • 2026–2027**
# PART 1
## SECTION 1: SIMPLE & MULTIPLE LINEAR REGRESSION
(Questions 1–30)
**1. In a simple linear regression model \( Y = \beta_0 + \beta_1 X +
\epsilon \), what does \( \beta_1 \) represent?**
A) The value of Y when X = 0
B) The change in Y for a one-unit change in X
C) The error term
D) The correlation coefficient
**Answer: B**
*Rationale:* \( \beta_1 \) is the slope coefficient. It represents the
average change in Y for a one-unit increase in X, holding all else
constant.
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**2. Which of the following assumptions is NOT required for ordinary
least squares (OLS) regression to yield unbiased coefficients?**
A) Linearity
B) Normality of errors
C) No perfect multicollinearity
D) Zero conditional mean (errors have mean zero given X)
**Answer: B**
*Rationale:* Normality of errors is required for hypothesis testing (t-
tests, F-tests) and confidence intervals, but not for unbiasedness. The
Gauss-Markov theorem requires linearity, random sampling, no perfect
multicollinearity, and zero conditional mean.
**3. A regression model has an R² = 0.85. This means:**
A) 85% of the variation in Y is explained by the model
B) 15% of the variation in Y is explained by the model
C) The model is not a good fit
D) The correlation between Y and predicted Y is 0.85
**Answer: A**
*Rationale:* R² is the proportion of variance in the dependent variable
that is explained by the independent variables. R² = 0.85 means 85% of
the variation is explained, 15% is unexplained.
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**4. What is the interpretation of the adjusted R²?**
A) It is always higher than R²
B) It penalizes the addition of irrelevant predictors
C) It measures the correlation between X and Y
D) It is unaffected by sample size
**Answer: B**
*Rationale:* Adjusted R² includes a penalty for the number of
predictors, increasing only if the new predictor improves the model more
than expected by chance. It is always less than or equal to R².
**5. In a multiple regression with three predictors, the F-test tests the
null hypothesis that:**
A) All individual coefficients are zero
B) At least one coefficient is non-zero
C) All coefficients (except intercept) are jointly zero
D) R² = 1
**Answer: C**
*Rationale:* The F-test tests \( H_0: \beta_1 = \beta_2 = \beta_3 = 0 \)
against \( H_a \): at least one \( \beta_j \neq 0 \). It tests the overall
significance of the model.
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**6. A 95% confidence interval for \( \beta_1 \) from a regression is [0.5,
1.5]. This means:**
A) There is a 95% probability that the true \( \beta_1 \) lies between 0.5
and 1.5
B) We are 95% confident that the true \( \beta_1 \) lies between 0.5 and
1.5
C) 95% of the data points fall between 0.5 and 1.5
D) The p-value for \( H_0: \beta_1 = 0 \) is less than 0.05
**Answer: B**
*Rationale:* Confidence intervals provide a range of plausible values
for the population parameter. The correct interpretation is: "We are 95%
confident that the true parameter lies within this interval." The interval
does not contain 0, so we reject \( H_0: \beta_1 = 0 \) at α = 0.05.
**7. What is the consequence of omitting a relevant variable from a
regression model?**
A) Unbiased coefficients but larger standard errors
B) Biased coefficients (omitted variable bias)
C) No effect on coefficients
D) Increased R²
**Answer: B**
**MGT 6203 MIDTERM EXAM – PART 1 & PART
2**DATA ANALYTICS FOR BUSINESS | GEORGIA
TECH | ONLINE MS IN ANALYTICS (OMSA)**200+
PRACTICE QUESTIONS WITH VERIFIED ANSWERS &
DETAILED RATIONALES**HIGH-YIELD CONTENT •
FIRST-TIME PASS • 2026–2027**
# PART 1
## SECTION 1: SIMPLE & MULTIPLE LINEAR REGRESSION
(Questions 1–30)
**1. In a simple linear regression model \( Y = \beta_0 + \beta_1 X +
\epsilon \), what does \( \beta_1 \) represent?**
A) The value of Y when X = 0
B) The change in Y for a one-unit change in X
C) The error term
D) The correlation coefficient
**Answer: B**
*Rationale:* \( \beta_1 \) is the slope coefficient. It represents the
average change in Y for a one-unit increase in X, holding all else
constant.
,2|Page
**2. Which of the following assumptions is NOT required for ordinary
least squares (OLS) regression to yield unbiased coefficients?**
A) Linearity
B) Normality of errors
C) No perfect multicollinearity
D) Zero conditional mean (errors have mean zero given X)
**Answer: B**
*Rationale:* Normality of errors is required for hypothesis testing (t-
tests, F-tests) and confidence intervals, but not for unbiasedness. The
Gauss-Markov theorem requires linearity, random sampling, no perfect
multicollinearity, and zero conditional mean.
**3. A regression model has an R² = 0.85. This means:**
A) 85% of the variation in Y is explained by the model
B) 15% of the variation in Y is explained by the model
C) The model is not a good fit
D) The correlation between Y and predicted Y is 0.85
**Answer: A**
*Rationale:* R² is the proportion of variance in the dependent variable
that is explained by the independent variables. R² = 0.85 means 85% of
the variation is explained, 15% is unexplained.
,3|Page
**4. What is the interpretation of the adjusted R²?**
A) It is always higher than R²
B) It penalizes the addition of irrelevant predictors
C) It measures the correlation between X and Y
D) It is unaffected by sample size
**Answer: B**
*Rationale:* Adjusted R² includes a penalty for the number of
predictors, increasing only if the new predictor improves the model more
than expected by chance. It is always less than or equal to R².
**5. In a multiple regression with three predictors, the F-test tests the
null hypothesis that:**
A) All individual coefficients are zero
B) At least one coefficient is non-zero
C) All coefficients (except intercept) are jointly zero
D) R² = 1
**Answer: C**
*Rationale:* The F-test tests \( H_0: \beta_1 = \beta_2 = \beta_3 = 0 \)
against \( H_a \): at least one \( \beta_j \neq 0 \). It tests the overall
significance of the model.
, 4|Page
**6. A 95% confidence interval for \( \beta_1 \) from a regression is [0.5,
1.5]. This means:**
A) There is a 95% probability that the true \( \beta_1 \) lies between 0.5
and 1.5
B) We are 95% confident that the true \( \beta_1 \) lies between 0.5 and
1.5
C) 95% of the data points fall between 0.5 and 1.5
D) The p-value for \( H_0: \beta_1 = 0 \) is less than 0.05
**Answer: B**
*Rationale:* Confidence intervals provide a range of plausible values
for the population parameter. The correct interpretation is: "We are 95%
confident that the true parameter lies within this interval." The interval
does not contain 0, so we reject \( H_0: \beta_1 = 0 \) at α = 0.05.
**7. What is the consequence of omitting a relevant variable from a
regression model?**
A) Unbiased coefficients but larger standard errors
B) Biased coefficients (omitted variable bias)
C) No effect on coefficients
D) Increased R²
**Answer: B**
