Simple vs Multiple Linear Regression

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

Dependent variable

Independent variable

predictor variable

None of the above

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

Dependent variable

Slope

Intercept

Independent Variable

Y increases X increases

X increases Y decreases

X increases Y remains constant

X increases Y increases

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

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

Coefficient of Correlation

Coefficient of Skewness

Coefficient of Determination $$\left(R^2\right)\$$

Standard Error of Estimate

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.