Multiple Regression Analysis

The percent of total variation of the dependent variable Y explained by the set of independent variables X is measured by

• Coefficient of Correlation

• Coefficient of Skewness

• Coefficient of Determination

• Standard Error of Estimate

If the Pearson correlation co-efficient R is equal to 1 then

• There is a perfect positive relationship between the two variables

• There is a negative relationship between the two variables

• There is no relationship between the two variables

• There is a positive relationship between the two variables

In regression analysis, the variable that is being predicted is the

• Dependent variable

• Independent variable

• Intervening variable

• None of the above

In the equation of a straight line, Y = mX + c , if m is equal to zero then when

• Y increases X increases

• X increases Y decreases

• X increases Y remains constant

• X increases Y increases

If the slope of the regression line is calculated to be 2.5 and the intercept 16 then the value of Y when X is 4 is

• 16

• 2.5

• 26

• 66.5

What test statistic is used for a global test of significance?

• Z test

• t test

• Chi-square test

• F-test

R-square is a mathematical notation for

• Spearman’s Co-efficient of Rank Correlation

• Pearson’s Co-efficient of Correlation

• The Co-efficient of Determination

• The Co-efficient of Variation

In the equation of a straight line, Y = mX + c the term, m is the

• Dependent variable

• Slope

• Intercept

• Independent Variable

In the equation of a straight line, Y = mX + c , if c is equal to zero then

• The line of best fit cuts the X axis to the left of the Y axis

• Does not cross the X axis

• The line of best fit cuts the X axis to the right of the Y axis

• The line of best fit passes through the origin

In the equation of a straight line, Y = mX + c , if m is equal to -2 then

• The relationship between the two variables is perfect

• There is no relationship between the two variables

• There is a positive relationship between the two variables

• There is a negative relationship between the two variables