Download presentation

Presentation is loading. Please wait.

Published byJesse Henry Modified over 3 years ago

1
Simple linear models Straight line is simplest case, but key is that parameters appear linearly in the model Needs estimates of the model parameters (slope and intercept)- usually by least squares Makes a number of assumptions, usually checked graphically using residuals

2
Examples for linear regression How is LOI related to moisture? How should we estimate merchantable volume of wood from the height of a living tree? How is pest infestation late in the season affected by the concentration of insecticide applied early in the season?

3
Scatterplot of tree volume vs height

4
Minitab commands

5
Regression Output

6
Interpreting the output Goodness of fit (R-squared) and ANOVA table p-value? Confidence intervals and tests for the parameters Assessing assumptions (outliers and influential observations Residual plots

7
t = distance between estimate and hypothesised value, in units of standard error vs Confidence intervals and t-tests

10
Regression output

11
Outliers

12
Residual plots

13
Confidence and prediction intervals

14
Low R-sq High R-sq Low p-value: significant High p-value: non-significant Four possible outcomes

15
Not because relationships are linear Transformations can often help linearise Good simple starting point – results are well understood Approximation to a smoothly varying curve Why linear?

Similar presentations

Presentation is loading. Please wait....

OK

Lecture 19: Tues., Nov. 11th R-squared (8.6.1) Review

Lecture 19: Tues., Nov. 11th R-squared (8.6.1) Review

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on fire extinguisher types for electronics Ppt on game theory in economics Ppt on tourism in karnataka Ppt on articles of association Ppt on bluetooth communication systems Fluency in reading ppt on ipad Ppt on english language in india Ppt on types of forests found in india Ppt on shell scripting tutorial Ppt on project management process