Multivariate Linear Regression MCQ

Test your knowledge with important Multivariate Linear Regression MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30+ more Multivariate Linear Regression MCQs on Bissoy. Bissoy App

• There is no upper bound on the number of the target variable(s).
  ক

  True

  খ

  False
• There is no upper bound on the number of the independent variable(s).
  ক

  True

  খ

  False
• What does (x1(4), x2(4), y(4)) represent or imply?
  ক

  There are 4 training examples

  খ

  The values of x1, x2, and y are 4

  গ

  The fourth training example and there are two independent variables

  ঘ

  The second training example and there are two independent variables
• What is the minimum number of variables required to represent a linear regression model?
  ক

  3

  খ

  2

  গ

  1

  ঘ

  4
• What does X(I) represent?
  ক

  A vector denoting all the values of the ith feature

  খ

  ith feature in the ith example

  গ

  A feature vector denoting the independent variables in the ith example

  ঘ

  Depends on the dataset
• Let there be n features. What is the dimension of the X vector in hypothesis h(X) = tTX?
  ক

  n x 1

  খ

  (n + 1) x 1

  গ

  n x n

  ঘ

  (n – 1) x 1
• How is the hypothesis represented in multivariate regression? Transpose of matrix a is represented as aT.
  ক

  h(X) = tTX

  খ

  h(X) = tX

  গ

  h(X) = tXT

  ঘ

  h(X) = tTXT
• What does xn(i) represent?
  ক

  Value of nth variable is i

  খ

  Value of ith variable in the nth training example

  গ

  Value of nth variable in the ith training example

  ঘ

  Value of ith variable is n
• The learner is trying to predict housing prices based on the size of each house and number of bedrooms. What type of regression is this?
  ক

  Multivariate Logistic Regression

  খ

  Logistic Regression

  গ

  Linear Regression

  ঘ

  Multivariate Linear Regression
• Multivariate linear regression belongs to which category?
  ক

  Neither supervised nor unsupervised learning

  খ

  Both supervised and unsupervised learning

  গ

  Supervised learning

  ঘ

  Unsupervised learning