1. Open the attached .csv file.
2. Select Regression from the data analysis tab
3. Weight will be our predictor variable and MPG the response variable. Select your x (predictor) and y (response) ranges accordingly. Check the labels button and and output cell.
4. Observe the ANOVA matrix. The F-statistic will tell you whether your model is better than simply using the mean. You will want the F-statistic to be as high as possible and the significance to be as low as possible.
5. The equation of the regression (or trend) line provides the relationship between x and y. The coefficient of x tells you how much y will change for each change in x by multiplying the coefficent times the change in x.
6. To see the linear model, insert a scatter chart. Right click on the chart to open a dialog window that will allow you to add data as you did before for x (predictor) and y (response).
7. Move your pointer over the chart and select the + symbol at the top right. Select Axis titles and input Weight and MPG where appropriate.Provide a meaningful name for the chart title.
8. When your mouse is over the + symbol scroll down, check the Trendline box and the More Options button.Select the Linear Model, display the equation on the chart, and show R squared.
9. You should see a scatterplot roughly bisected by a negative line revealing the weight and MPG are negatively correlated.

Create a linear model with a data set of your own choosing. Discuss the meaning of the F statistic the significance of the F statistic that is produced. Interpret the graph, discussing whether the predictor variable does a good job predicting the response. Submit a Word document with screenshots of your work and your discussion. Always include a slice of your desktop with any screenshot.