Hi everyone! Read through the material below, watch the videos, work on the Excel lecture and follow up with your instructor if you have questions.
Learning Outcomes.
- Collect, organize and graph raw data.
- Compute statistical parameters (covariance, correlation coefficient).
- Compute a regression equation and use it to make predictions.
Topic. This lesson covers: Least Squares Method and Regression
- Openstax Introductory Statistics:
- Introductory Statistics by Sheldon Ross, 3rd edition: Sections 12.1-12.3
- Statistics with Microsoft Excel by Beverly J. Dretzke, 5th ed., SLOPE, INTERCEPT P. 205 – 210
Videos
The Applied View
Watch the video fitting lines to data.
- How is the snowpack during wintertime in the Colorado Mountains measured?
- What is a residual?
- How does the least-squares method decide which line best fits the points in a scatterplot?
- How can a particular year’s data on the snowpack be used to predict the amount of water running downstream in the spring?
- The video showed two examples of residual plots. What does a residual plot tell you if the dots in the plot appear to be randomly scattered? What if the dots appear to form a strong curved pattern instead?
Exit Ticket
Refer to your exit ticket example from Lecture #4. What is your regression equation? Use it to predict what your stock price might be 3 weeks from today. 3 weeks from today, refer back to your prediction. How accurate/inaccurate was your prediction? What variables might have effected the price? Here is an example.
