Simple vs Multiple Linear Regression

The main purpose(s) of Linear Regression is/are (choose all that apply):

• Measuring the linear relationship between one variable and another

• Predicting one variable on the basis of another

• Measuring the effect of one or more variables on another variable

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

• Dependent variable

• Independent variable

• predictor variable

• None of the above

What is a scatter plot?

• A graph that shows the relationship of two data sets

• A graph that shows frequencies of a categorical data set

• A graph that shows the distribution of a continuous data set

• A graph that shows only the linear relationship between two data sets

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

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 m is equal to zero then when

• Y increases X increases

• X increases Y decreases

• X increases Y remains constant

• X increases Y increases

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

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

• Standard Error of Estimate

Linear regression analysis produces an equation of Y(hat) = 7 - 3.2X. This indicates that:

• An X value of 0 would increase Y by 7.

• A 1 unit decrease in X results in a 3.2 unit decrease in Y.

• A 1 unit increase in X results in a 3.2 unit increase in Y.

• A 1 unit increase in X results in a 3.2 unit decrease in Y.