Create a linear model with a data set of your own choosing.

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.