1 / 33

Virtual COMSATS Inferential Statistics Lecture-26

This lecture focuses on the introduction of correlation and regression analysis, along with the discussion on regression models, assumptions, and estimation of population regression coefficients. The lecture also covers hypothesis testing and confidence interval estimation for correlation coefficients.

eregina
Télécharger la présentation

Virtual COMSATS Inferential Statistics Lecture-26

An Image/Link below is provided (as is) to download presentation 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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Virtual COMSATSInferential StatisticsLecture-26 Ossam Chohan Assistant Professor CIIT Abbottabad

  2. Recap of previous lectures • We are working on hypothesis testing. And we discussed following topics in our last lectures: • Simple Correlation and its significance in research. • Solution to problems. • Properties of Correlation. • Scatter Plot. • Coefficient of determination.

  3. Objective of lecture-26 • Introduction of Correlation and Regression. • Regression Analysis. • Simple Regression model. • Probable error and standard error. • Hypothesis testing for correlation coefficient.

  4. Regression Analysis • Predict the value of a dependent variable based on the value of at least one independent variable. • Study the impact of changes in dependent variable on the basis of changes in independent variable. • Independent Variable: Variable that have impact on dependent variable. • Dependent variable: The variable in which we are interested.

  5. Simplest form of Regression model • There must be one dependent variable in each model. • There should be single independent variable. • Relationship must be linear. • There must be logical relations between x and y, that is changes in y are because of x.

  6. Discussion on Regression model

  7. Discussion on Regression model

  8. Population Linear Regression Random Error term, or residual Population SlopeCoefficient Population y intercept Independent Variable Dependent Variable

  9. Linear Regression Assumptions • Error values are normally distributed for any given value of x • The probability distribution of the errors has constant variance • The underlying relationship between the x variable and the y variable is linear • Error values (ε) are statistically independent • The probability distribution of the errors is normal

  10. Estimated Regression Model Estimated (or predicted) y value Estimate of the regression intercept Estimate of the regression slope Independent variable The individual random error terms ei have a mean of zero

  11. How to calculate unknown bs

  12. Problem-28 • For the problem-27, fit a simple regression model. And interpret the results. • The test-Retest method is one way of establishing the reliability of a test. The test is administered and then, at a later date, the same test is re-administered to the same individuals. Fit a simple linear regression model for two sets of scores.

  13. Problem-28 Solution

  14. Problem-28 Solution

  15. Where is Inferential Statistics????

  16. Probable error and standard error • Probable error of correlation coefficient indicates extent to which its value depends on the conditions of random samples. • PEr = 0.6745 SEr • If r is the correlation coefficient t in a sample of n pairs of observations, then the standard error SEr of r is given by • SEr= (1-r2)/√n • The range within which population coefficient of correlation is expected to fall would be: • ρ= r ± PEr

  17. Problem-29 • If r=0.8 and n=25, then find the limits within which population correlation coefficient will fall:

  18. Hypothesis testing for population correlation coefficient (small samples) • IS there any significant correlation between x and y. • Problem-30 • A random sample of 27 pairs of observations from a normal population gives a correlation coefficinet of 0.42. is it likely that the variables in the population are uncorrelated?

  19. Problem-30 Solution

  20. Hypothesis testing for population correlation coefficient (large samples)

  21. Hypothesis testing for population correlation coefficient (large samples)

  22. Discussion Cont…

  23. Problem-30 • What is the probability that a correlation coefficient of 0.75 or less arises in a sample of 30 pairs of observations from a normal population in which the true correlation is 0.90. • Apply Fisher’s z-transformation

  24. Problem-30 Solution

  25. Problem-31 • Test the significance of the correlation r= 0.5 from a sample of size 18 against hypothesized population correlation 0.70. • We need to apply z-transformation

  26. Problem-31 Solution

  27. CI Estimation of Population Regression Co-efficient-β • Sample estimate of β is b. • The sampling distribution of b is normally distributed with a mean β, and standard deviation (standard error) δY.X /√Σ(X- )2 • That is, the variable • Z=(b- β)/ δY.X /√Σ(X- )2 is standard normal variable. What about population standard deviation

  28. CI Estimation of Population Regression Co-efficient-β

  29. Cont…

  30. Problem-32 • Use the following data to construct 95% confidence interval for the (a) value of α (b) the value of β.

  31. Problem-32 Solution

  32. Problem-32 Solution

  33. Assessment Problem-26 • Given the data • Assuming normality, calculate the 95% confidence interval for the (a) value of α (b) the value of β.

More Related