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Unit 2 - Practice Quiz

INT234

1 What is the primary goal of Simple Linear Regression?

A. To classify data into discrete categories
B. To find the relationship between two continuous variables using a straight line
C. To group similar data points together
D. To reduce the dimensionality of the dataset

2 In the linear regression equation Y = mx + c, what does 'c' represent?

A. The slope of the line
B. The dependent variable
C. The y-intercept
D. The error term

3 Which method is commonly used to estimate the coefficients in linear regression?

A. Maximum Likelihood Estimation
B. Ordinary Least Squares (OLS)
C. K-Means Clustering
D. Principal Component Analysis

4 What is a 'residual' in the context of regression?

A. The value of the independent variable
B. The difference between the observed value and the predicted value
C. The slope of the regression line
D. The square of the correlation coefficient

5 Which of the following values indicates a perfect negative correlation?

A.
B. 1
C. -1
D. 0.5

6 Multiple Linear Regression differs from Simple Linear Regression because it involves:

A. Multiple dependent variables
B. Multiple independent variables
C. Polynomial terms
D. Categorical outputs

7 In a regression model, if the R-squared value is 0.85, what does this mean?

A. 85% of the errors are zero
B. The model is 85% accurate in classification
C. 85% of the variance in the dependent variable is explained by the independent variables
D. The correlation coefficient is 0.85

8 Which evaluation metric is calculated by taking the average of the squared differences between predicted and actual values?

A. MAE
B. RMSE
C. MSE
D. R-squared

9 Which regression algorithm is specifically designed to predict the probability of a categorical outcome (e.g., Yes/No)?

A. Simple Linear Regression
B. Polynomial Regression
C. Logistic Regression
D. Ridge Regression

10 What function is used in Logistic Regression to map predicted values to probabilities between 0 and 1?

A. Linear function
B. Sigmoid (Logistic) function
C. ReLU function
D. Polynomial function

11 Polynomial Regression is best used when:

A. The relationship between variables is linear
B. The relationship between variables is non-linear
C. The dependent variable is categorical
D. There are too many independent variables

12 What is a potential risk of using a high-degree polynomial in Polynomial Regression?

A. Underfitting
B. Overfitting
C. Linearity
D. High bias

13 Root Mean Squared Error (RMSE) is preferred over MSE when:

A. You want the error metric to be in the same units as the target variable
B. You want to penalize outliers less
C. You have a classification problem
D. You want a value between 0 and 1

14 Which metric is less sensitive to outliers?

A. MSE
B. RMSE
C. MAE
D. R-squared

15 In OLS estimation, the assumption of 'Homoscedasticity' implies that:

A. The residuals have constant variance across all levels of the independent variable
B. The residuals follow a normal distribution
C. There is no correlation between independent variables
D. The relationship is non-linear

16 Multicollinearity in Multiple Linear Regression refers to:

A. A high correlation between the dependent and independent variables
B. A high correlation between two or more independent variables
C. The lack of a linear relationship
D. Measurement errors in the target variable

17 The range of the R-squared (R²) score is typically:

A. -1 to 1
B. 0 to infinity
C. 0 to 1
D. -infinity to 1

18 In the equation Y = b0 + b1x1 + b2x2 + ... + bn*xn, what are b1, b2, ... bn called?

A. Intercepts
B. Residuals
C. Regression coefficients
D. Dependent variables

19 Which of the following is NOT an assumption of Linear Regression?

A. Linearity
B. Independence of errors
C. Multicollinearity usually present
D. Normality of residuals

20 What is the primary advantage of Adjusted R-squared over R-squared?

A. It is easier to calculate
B. It penalizes the addition of irrelevant independent variables
C. It can handle non-linear data
D. It is always higher than R-squared

21 In Logistic Regression, the 'Logit' is defined as:

A. The sum of squared errors
B. The natural logarithm of the odds ratio
C. The square root of the variance
D. The probability of success

22 If the Pearson correlation coefficient between X and Y is 0, it implies:

A. X and Y are identical
B. There is no linear relationship between X and Y
C. X causes Y
D. There is a strong non-linear relationship

23 Which cost function is primarily used for Logistic Regression?

A. Mean Squared Error
B. Mean Absolute Error
C. Log Loss (Cross-Entropy)
D. Hinge Loss

24 When evaluating a regression model, a lower MAE indicates:

A. Worse performance
B. Better performance
C. High variance
D. Overfitting

25 To handle categorical independent variables in regression, one should usually:

A. Ignore them
B. Convert them using One-Hot Encoding (Dummy variables)
C. Assign random numbers
D. Use the text directly

26 The 'Dummy Variable Trap' occurs when:

A. Categorical variables are missing
B. One dummy variable can be predicted perfectly from the others (perfect multicollinearity)
C. There are too many categories
D. The variables are not scaled

27 Which of the following plots is best for visualizing a Simple Linear Regression?

A. Pie Chart
B. Scatter Plot with a line of best fit
C. Histogram
D. Box Plot

28 If a polynomial regression model has degree 1, it behaves like:

A. A Simple Linear Regression
B. A Logistic Regression
C. A Quadratic Regression
D. An Exponential Regression

29 In the context of MSE, what is the effect of squaring the errors?

A. It cancels out positive and negative errors
B. It penalizes larger errors more severely than smaller errors
C. It makes the calculation faster
D. It reduces the impact of outliers

30 Which algorithm minimizes the sum of squared residuals?

A. Gradient Boosting
B. Ordinary Least Squares
C. Logistic Regression
D. Decision Trees

31 What is the typical threshold used in Logistic Regression to classify a probability as '1' (or Positive)?

A. 0.1
B. 1.0
C. 0.5
D. 0.0

32 Predicting the price of a house based on size, location, and age is a problem of:

A. Clustering
B. Classification
C. Regression
D. Dimensionality Reduction

33 Predicting whether an email is 'Spam' or 'Not Spam' is a problem of:

A. Linear Regression
B. Polynomial Regression
C. Classification (e.g., Logistic Regression)
D. K-Means

34 In the equation Y = b0 + b1*X + e, what does 'e' represent?

A. The predicted value
B. The error term (noise)
C. The correlation
D. The intercept

35 If the correlation coefficient (r) is 0.9, the Coefficient of Determination (R²) is:

A. 0.9
B. 0.18
C. 0.81
D. 0.45

36 Which of the following indicates the strongest relationship?

A. Correlation = 0.5
B. Correlation = -0.8
C. Correlation = 0.1
D. Correlation = 0.7

37 Why might one perform a log transformation on the dependent variable in regression?

A. To make the distribution more normal or linearize a relationship
B. To turn it into a categorical variable
C. To increase the number of outliers
D. To reduce the sample size

38 If a model has an RMSE of 10 and an MAE of 2, what does this suggest?

A. The model is perfect
B. There are likely large outliers in the errors
C. The model is underfitting
D. RMSE is calculated incorrectly

39 In polynomial regression, if the curve passes through every single data point perfectly, the model is likely:

A. Ideally fitted
B. Underfitted
C. Overfitted
D. Linear

40 The decision boundary in Logistic Regression is:

A. Curved
B. Linear (in the feature space)
C. Circular
D. Undefined

41 What is the slope of the line y = 3x + 5?

A. 5
B. 3
C.
D. 8

42 When comparing two regression models on the same dataset, the one with the ___ is generally preferred.

A. Higher RMSE
B. Lower R-squared
C. Higher R-squared and Lower RMSE
D. Higher MAE

43 Which technique helps checks for 'Linearity' in a regression model?

A. Residuals vs Predicted Values plot
B. Histogram of the target
C. Correlation matrix
D. Bar chart

44 The 'Bias-Variance Tradeoff' implies that:

A. We want high bias and high variance
B. As we increase model complexity, bias decreases but variance increases
C. Simple models have high variance
D. Complex models have high bias

45 Feature scaling (normalization/standardization) is particularly important for:

A. Simple Linear Regression with one variable
B. Decision Trees
C. Regression using Gradient Descent optimization
D. Ordinary Least Squares solution

46 If R-squared is 1.0:

A. The model predicts the mean for every observation
B. The model explains 100% of the variability in the data
C. The model is completely wrong
D. The correlation is 0

47 Which of the following is a dependent variable in a study of how study time affects exam scores?

A. Study time
B. Exam scores
C. Student ID
D. Subject

48 In the context of regression, 'extrapolation' refers to:

A. Predicting values outside the range of the training data
B. Predicting values within the range of training data
C. Removing outliers
D. Calculating the mean

49 The correlation coefficient ranges between:

A. 0 and 1
B. -1 and 1
C. -infinity and infinity
D. 0 and 100

50 Which of the following is NOT a metric for Regression?

A. Accuracy
B. MAE
C. MSE
D. R-squared