Week 8 Answers
1) Regression is used in:
a) Predictive data mining
b) Exploratory data mining
c) Descriptive data mining
d) Explanative data mining
Answer: A
2) The output of a regression algorithm is usually a:
a) Real variable
b) Integer variable
c) Character variable
d) String variable
Answer: A
3) Regression finds out the model parameters which produce the least square error between:
a) Input value and output value
b) Input value and target value
c) Output value and target value
d) Model parameters and output value
Answer: C
4) Consider x₁, x₂ to be the independent variables and y the dependent variable, which of the following represents a linear regression model?
a) y = a₀ + a₁/x₁ + a₂/x₂
b) y = a₀ + a₁x₁ + a₂x₂
c) y = a₀ + a₁x₁ + a₂x₂²
d) y = a₀ + a₁x₁² + a₂x₂
Answer: B
5) The linear regression model y = a₀ + a₁x is applied to the data in the table shown below. What is the value of the sum squared error function S(a₀, a₁), when a₀ = 1, a₁ = 2?
a) 0.00
b) 0.25
c) 0.50
d) 0.51
Answer: D
6) The linear regression model y = a₀ + a₁x is to be fitted to the data in the table shown below. What is the optimal regression model obtained by minimizing sum squared error?
a) y = 1.01 – 2.10x
b) y = 1.01 + 2.10x
c) y = 1.01 – 0.98x
d) y = 1.01 + 0.98x
Answer: D
7) The linear regression model y = a₀ + a₁x₁ + a₂x₂ + … + aₚxₚ is to be fitted to a set of N training data points having p attributes each. Let X be an N × (p+1) vector of input values (augmented by 1’s), Y be an N × 1 vector of target values, and q be a (p+1) × 1 vector of parameter values (a₀, a₁, a₂, …, aₚ). If the sum squared error is minimized for obtaining the optimal regression model, which of the following equation holds?
a) XᵀX = Xy
b) Xq = Xᵀy
c) XᵀXq = y
d) XᵀXq = Xᵀy
Answer: D
8) Accuracy of a linear regression model usually has:
a) Low bias and low variance
b) Low bias but high variance
c) High bias but low variance
d) High bias and high variance
Answer: C
9) A time series prediction problem is often solved using:
a) Multivariate regression
b) Autoregression
c) Logistic regression
d) Sinusoidal regression
Answer: B
10) In principal component analysis, the projected lower-dimensional space corresponds to:
a) Subset of the original coordinate axis
b) Eigenvectors of the data covariance matrix
c) Eigenvectors of the data distance matrix
d) Orthogonal vectors to the original coordinate axis
Answer: B
