2nd Year Statistics Chapter 14 Online MCQ Test for 12th Class Statistics Chapter 14 (Simple Linear Regression and Correlation)

The relationship that describes the dependence of the expected value of the dependent random variable for a given value of the independent non-random variable is called

• equation A
• relation B
• ratio C
• regression D

The variable that forms the basis of estimation is called

• regression A
• regressand B
• regressor C
• correlation D

• 37 A
• 132 B
• 32 C
• cannot be calculated D

When b_xy is positive, then b_yx will be _______.

• Negative A
• Positive B
• Zero C
• One D

The variable, whose resulting value depends upon the selected value of the independent variable is called______.

• Regression A
• Regressor B
• Regressand C
• Coefficient D

The value of the coefficient of correlation relies between__________.

• -1 and +1 A
• 0 and 1 B
• -1 and 0 C
• -0.5 and + 0.5 D

If r = -1, then there is

• negative correlation A
• perfect negative correlation B
• no correlation C
• average correlation D

In simple linear regression, the number of unknown constants are:

• Two A
• Three B
• Four C
• Five D

Regression line x on y is

• 
  A
• 
  B
• 
  C
• 
  D

The regression equation always passes throught_________.

• (X, Y) A
• (X, y̅) B
• (X̅, Y) C
• (X̅, y̅) D

If X and Y are independent, then Cov(x,y) = 0 which implies that

• b_yx = 0 A
• b_xy = 0 B
• ρ = 0 C
• a = 0 D

A process by which we estimate the value of dependent variable on the basis of one or more independent variable is called_________.

• Residual A
• Correlation B
• Regression C
• Slope D

• 
  A
• b_yx B
• b_xy C
• S_p D

If b_yx = 0.89 and b_xy = 0.75, then r=

• 0.89 A
• 0.28 B
• 0.98 C
• 0.82 D

The estimates of the parameters=α and=β are

• μ and=σ2 A
• a and b B
• μ andπ C
• χ2 and Z D

The _______ regression line always passes through (X̅, y̅).

• Opposite A
• Estimated B
• Estimates C
• Random D

The measures of strength of closeness of linear relationship between two variables is called

• simple linear regression A
• composite linear regression B
• simple linear correlation C
• composite linear correlation D