Unit 2-2 Assessment Review: Linear Regression & Scatterplots

Front

What is the equation of a line of best fit?

Back

The equation of a line of best fit can be expressed in the form y = mx + b, where m is the slope and b is the y-intercept.

Front

What does a strong positive correlation indicate in a scatterplot?

Back

A strong positive correlation indicates that as one variable increases, the other variable also tends to increase.

Front

What does an r value of -0.3 indicate about the correlation?

Back

An r value of -0.3 indicates a weak negative correlation, meaning there is a slight tendency for one variable to decrease as the other increases.

Front

How do you calculate the linear regression equation from a data set?

Back

To calculate the linear regression equation, you can use statistical software or a calculator that provides regression analysis, which will give you the slope and y-intercept.

Front

What is the predicted distance traveled after 15 hours if the linear regression equation is y = 61.93x - 1.79?

Back

The predicted distance traveled after 15 hours is 927 miles.

Front

What does the slope in a linear regression equation represent?

Back

The slope represents the rate of change of the dependent variable (y) with respect to the independent variable (x).

Front

What is the significance of the y-intercept in a linear regression equation?

Back

The y-intercept is the value of y when x is 0, indicating the starting point of the line on the y-axis.

Front

How can you interpret the results of a linear regression analysis?

Back

The results can be interpreted by examining the slope, y-intercept, and the correlation coefficient (r) to understand the relationship between the variables.

Front

What is the formula for predicting values using a linear regression equation?

Back

The formula for predicting values is to substitute the value of x into the linear regression equation y = mx + b.

Front

What does a scatterplot visually represent?

Back

A scatterplot visually represents the relationship between two quantitative variables, showing how one variable may be affected by the other.