Create a chart in Excel that is a scatterplot of mileage (x-axis) and price (y-axis). Add the regression line, the regression equation, and the r-squared (in Excel, R2) value to the chart. Copy it from Excel and paste it into Word. Write a brief summary (1 paragraph) of what the chart indicates about the relationship between the two variables. Identify any outliers that you see.
V. Calculate a 95% confidence interval (CI) for both of the two variables (price and mileage). In your report, state the 95% CI for both variables and provide a brief written summary (1 paragraph) interpreting the CI’s.
VI. Copy the results of the simple linear regression hypothesis test that you conducted in Excel and paste them into Word.
VII. Write a detailed summary (3 – 4 paragraphs) of the results explaining what they mean and how they answer the question listed in part I. State whether you rejected the null hypothesis in favor of the alternative hypothesis or whether you failed to reject the null hypothesis, including the F-value and p-value for your test and your interpretation of these values. Report the Pearson correlation coefficient (r) and the goodness of fit (r2) and interpret what they tell you about the relationship in terms of strength of the correlation between the variables and the fit of the line to the data, respectively. State the linear regression equation y = a + bx + e, using price as y and mileage as x (b is the regression coefficient for mileage, which comes from the Excel output).
VIII. Conclusion: Briefly summarize (1 paragraph) what you did for this project and what you determined about the relationship between mileage and price of a used car of a particular make, model, and year. Make sure that your summary clearly answers the question asked in part I.