Independent Variables MCQs Quiz Online PDF Book Download

Independent variables MCQs, independent variables quiz answers to learn online accounting courses. Learn cost function and behavior multiple choice questions (MCQs), independent variables quiz questions and answers. Career assessment test on data collection and adjustment issues, specification analysis : estimation assumptions, estimating cost function using quantitative analysis, independent variables test prep for business analyst certification.

Practice cost function and behavior test MCQs: an assumption, which states that there must be linear relationship between independent variable and dependent variable is, with choices irrelevant range of linearity, relevant range of linearity, significant range, and insignificant range for online degree in accounting and finance. Prepare assessment test for scholarships, online learning independent variables quiz questions for competitive assessment in business majors for CPA certification. Independent Variables Video

MCQ on Independent VariablesQuiz Book Download

MCQ: An assumption, which states that there must be linear relationship between independent variable and dependent variable is

  1. irrelevant range of linearity
  2. relevant range of linearity
  3. significant range
  4. insignificant range

B

MCQ: In multicollinearity, correlation coefficient between two independent variables must be greater than

  1. 0.7
  2. 0.6
  3. 0.5
  4. 0.4

A

MCQ: An estimated coefficient, which indicates degree by which estimated values are affected by random factors is known as

  1. standard error of estimated coefficient
  2. weighted error of estimated coefficient
  3. average of estimated coefficient
  4. variance of estimated coefficient

A

MCQ: For slope coefficient b, value of estimated coefficient is considered as

  1. d-value
  2. c-value
  3. t-value
  4. b-value

C

MCQ: Situation in which two or more independent variables are highly correlated is known as

  1. price linearity
  2. cost linearity
  3. division linearity
  4. multi-collinearity

D