Reference no: EM132855889

Description Answer the following questions

Instructions

Answer the following questions:

If the variables do have a linear relationship, the next step is to create what you call the regression line. You have seen that when you have data for two quantitative variables, you can create scatterplot of the data. A regression line is simply a straight line that comes closest to the points in the scatterplot. To create the equation for the regression line, you just need to know how to get what is called the slope and the y-intercept.

 And solve problems involving the same car data that you used earlier.

• Given the following data where city MPG is the response variable and weight is the explanatory variable, explain why a regression line would be appropriate to analyze the relationship between these variables:

Model City MPG Weight

Mazda MX-5 Miata 25 2365

Mercedes/Benz SLK 22 3020

Mitsubishi Eclipse 23 3235

Pontiac Firebird 18 3545

Porsche Boxster 19 2905

Saturn SC 27 2420

• Construct the regression line for this data.
• Interpret the meaning of the y-intercept and the slope within this scenario.
• What would you predict the city MPG to be for a car that weighs 3000 pounds?
• If a car that weighs 3000 pounds actually gets 32 MPG, would this be unusual? Calculate the residual and talk about what that value represents.