Download presentation

1
**Linear regression models**

2
**Simple Linear Regression**

3
History Developed by Sir Francis Galton ( ) in his article “Regression towards mediocrity in hereditary structure”

4
Purposes: To describe the linear relationship between two continuous variables, the response variable (y-axis) and a single predictor variable (x-axis) To determine how much of the variation in Y can be explained by the linear relationship with X and how much of this relationship remains unexplained To predict new values of Y from new values of X

5
**The linear regression model is:**

Xi and Yi are paired observations (i = 1 to n) β0 = population intercept (when Xi =0) β1 = population slope (measures the change in Yi per unit change in Xi) εi = the random or unexplained error associated with the i th observation. The εi are assumed to be independent and distributed as N(0, σ2).

6
Linear relationship Y ß1 1.0 ß0 X

7
**Linear models approximate non-linear functions over a limited domain**

extrapolation interpolation extrapolation

8
**For a given value of X, the sampled Y values are independent with normally distributed errors:**

Yi = βo + β1*Xi + εi ε ~ N(0,σ2) E(εi) = 0 E(Yi ) = βo + β1*Xi Y E(Y2) E(Y1) X X1 X2

9
**Fitting data to a linear model:**

Yi Yi – Ŷi = εi (residual) Ŷi Xi

10
**The residual sum of squares**

11
**Estimating Regression Parameters**

The “best fit” estimates for the regression population parameters (β0 and β1) are the values that minimize the residual sum of squares (SSresidual) between each observed value and the predicted value of the model:

12
Sum of squares Sum of cross products

13
**Least-squares parameter estimates**

where

14
Sample variance of X: Sample covariance:

15
**Solving for the intercept:**

Thus, our estimated regression equation is:

16
**Hypothesis Tests with Regression**

Null hypothesis is that there is no linear relationship between X and Y: H0: β1 = 0 Yi = β0 + εi HA: β1 ≠ 0 Yi = β0 + β1 Xi + εi We can use an F-ratio (i.e., the ratio of variances) to test these hypotheses

17
**Variance of the error of regression:**

NOTE: this is also referred to as residual variance, mean squared error (MSE) or residual mean square (MSresidual)

18
**Mean square of regression:**

The F-ratio is: (MSRegression)/(MSResidual) This ratio follows the F-distribution with (1, n-2) degrees of freedom

19
**Variance components and Coefficient of determination**

20
**Coefficient of determination**

21
**ANOVA table for regression**

Source Degrees of freedom Sum of squares Mean square Expected mean square F ratio Regression 1 Residual n-2 Total n-1

22
**Product-moment correlation coefficient**

23
**Parametric Confidence Intervals**

If we assume our parameter of interest has a particular sampling distribution and we have estimated its expected value and variance, we can construct a confidence interval for a given percentile. Example: if we assume Y is a normal random variable with unknown mean μ and variance σ2, then is distributed as a standard normal variable. But, since we don’t know σ, we must divide by the standard error instead: , giving us a t-distribution with (n-1) degrees of freedom. The 100(1-α)% confidence interval for μ is then given by: IMPORTANT: this does not mean “There is a 100(1-α)% chance that the true population mean μ occurs inside this interval.” It means that if we were to repeatedly sample the population in the same way, 100(1-α)% of the confidence intervals would contain the true population mean μ.

24
**Publication form of ANOVA table for regression**

Source Sum of Squares df Mean Square F Sig. Regression 11.479 1 21.044 Residual 8.182 15 .545 Total 19.661 16

25
**Variance of estimated intercept**

26
**Variance of the slope estimator**

27
**Variance of the fitted value**

28
**Variance of the predicted value (Ỹ):**

29
Regression

30
**Assumptions of regression**

The linear model correctly describes the functional relationship between X and Y The X variable is measured without error For a given value of X, the sampled Y values are independent with normally distributed errors Variances are constant along the regression line

31
**Residual plot for species-area relationship**

Similar presentations

OK

11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.

11-1 Empirical Models Many problems in engineering and science involve exploring the relationships between two or more variables. Regression analysis.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To ensure the functioning of the site, we use **cookies**. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy & Terms.
Your consent to our cookies if you continue to use this website.

Ads by Google

Ppt on formal education will make you a living Types of window display ppt online Ppt on edge detection software Seven segment display ppt online Ppt on power system security Ppt on human resource recruitment Maths ppt on surface area and volume Ppt on inventory turnover ratio Ppt on ganga action plan Ppt on child labour free download