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

• S_xy A
• S_yx B
• b_xy C
• b_yx D

If constants are added to or subtracted from the values of the variables, the value of r 

• is negative A
• is positive B
• is zero C
• remains unchanged D

• y-intercept A
• x-intercept B
• slope C
• none of these D

A set of points in a rectangular coordinate system, where each point represents an observed pair of values is called

• least square regression A
• scatter diagram B
• pie graph C
• regression coefficient D

The straight line graph of the linear equation Y = a + bX, the slope will be upward it_______.

• b = 0 A
• b < 0 B
• b > 0 C
• b ≠ 0 D

A relationship where the flow of the data points is best represented by a curve is called_______.

• Linear positive A
• Linear negative B
• Linear relationship C
• Nonlinear relatiobship D

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

• Two A
• Three B
• Four C
• Five D

The variable, that forms the basis of estimation, is called_______.

• Regression A
• Regressor B
• Regressand C
• Estimated 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

The variable that forms the basis of estimation is called

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

When b_xy is positive, then b_yx will be _______.

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

r_xy -------------- r_yx

• = A
• < B
• > C
• ≠ 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

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

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

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