Loading in 5 sec....

Simple Linear RegressionPowerPoint Presentation

Simple Linear Regression

- 179 Views
- Uploaded on
- Presentation posted in: General

Simple Linear Regression. Statistics 700 Week of November 27. Example for Illustration.

Simple Linear Regression

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Simple Linear Regression

Statistics 700

Week of November 27

- The human body takes in more oxygen when exercising than when it is at rest. To deliver oxygen to the muscles, the heart must beat faster. Heart rate is easy to measure, but measuring oxygen uptake requires elaborate equipment. If oxygen uptake (VO2) can be accurately predicted from heart rate (HR), the predicted values may replace actually measured values for various research purposes. Unfortunately, not all human bodies are the same, so no single prediction equation works for all people. Researchers can, however, measure both HR and VO2 for one person under varying sets of exercise conditions and calculate a regression equation for predicting that person’s oxygen uptake from heart rate.

Simple Linear Regression

- Goals in this illustration:
- Scatterplot: linear relationship or not?
- Obtain the best-fitting line using least-squares.
- To test whether the model is significant or not.
- To obtain a confidence interval for the regression coefficient.
- To obtain predictions.

Simple Linear Regression

Simple Linear Regression

1. Conditional on X=x, the response variable Y has mean equal to m(x) = a + bx.

2. ais the y-intercept; whileb is the slope of the regression line, which could be interpreted as the change in the mean value per unit change in the independent variable.

3. For each X = x, the conditional distribution of Y is normal with mean m(x) and variance s2.

4. Y1, Y2, …, Yn are independent of each other.

Shorthand:

Yi = a + bxi + ei with ei IID N(0,s2)

Simple Linear Regression

Simple Linear Regression

Simple Linear Regression

Simple Linear Regression

Simple Linear Regression

Simple Linear Regression

Simple Linear Regression

Simple Linear Regression