Lecture 4 Normal distribution? Sample data at :

Slides:



Advertisements
Similar presentations
Assumptions underlying regression analysis
Advertisements

Weighted Least Squares Regression Dose-Response Study for Rosuvastin in Japanese Patients with High Cholesterol "Randomized Dose-Response Study of Rosuvastin.
Kin 304 Regression Linear Regression Least Sum of Squares
Chapter 12 Simple Linear Regression
LINEAR REGRESSION: Evaluating Regression Models Overview Assumptions for Linear Regression Evaluating a Regression Model.
LINEAR REGRESSION: Evaluating Regression Models. Overview Assumptions for Linear Regression Evaluating a Regression Model.
K sample problems and non-parametric tests. Two-Sample T-test (unpaired)
Confidence intervals. Population mean Assumption: sample from normal distribution.
Class 5: Thurs., Sep. 23 Example of using regression to make predictions and understand the likely errors in the predictions: salaries of teachers and.
Lecture 4 Data editing. List of nice things Equal variance Normal distribution Linear relationship Independent variables Other requirements.
Simple Linear Regression Analysis
Regression Diagnostics Checking Assumptions and Data.
Quantitative Business Analysis for Decision Making Simple Linear Regression.
Chapter 12 Section 1 Inference for Linear Regression.
Simple Linear Regression Analysis
Lecture 5 Correlation and Regression
Correlation & Regression
Advantages of Multivariate Analysis Close resemblance to how the researcher thinks. Close resemblance to how the researcher thinks. Easy visualisation.
Regression and Correlation Methods Judy Zhong Ph.D.
Introduction to Linear Regression and Correlation Analysis
Regression Analysis Regression analysis is a statistical technique that is very useful for exploring the relationships between two or more variables (one.
Measures of Variability Objective: Students should know what a variance and standard deviation are and for what type of data they typically used.
Economics 173 Business Statistics Lecture 20 Fall, 2001© Professor J. Petry
Analysis of Residuals Data = Fit + Residual. Residual means left over Vertical distance of Y i from the regression hyper-plane An error of “prediction”
MBP1010H – Lecture 4: March 26, Multiple regression 2.Survival analysis Reading: Introduction to the Practice of Statistics: Chapters 2, 10 and 11.
Multiple Regression and Model Building Chapter 15 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.
Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.
AP Statistics Chapter 15 Notes. Inference for a Regression Line Goal: To determine if there is a relationship between two quantitative variables. –i.e.
Regression Analysis Week 8 DIAGNOSTIC AND REMEDIAL MEASURES Residuals The main purpose examining residuals Diagnostic for Residuals Test involving residuals.
Univariate Linear Regression Problem Model: Y=  0 +  1 X+  Test: H 0 : β 1 =0. Alternative: H 1 : β 1 >0. The distribution of Y is normal under both.
Simple Linear Regression ANOVA for regression (10.2)
1 Regression Analysis The contents in this chapter are from Chapters of the textbook. The cntry15.sav data will be used. The data collected 15 countries’
REGRESSION DIAGNOSTICS Fall 2013 Dec 12/13. WHY REGRESSION DIAGNOSTICS? The validity of a regression model is based on a set of assumptions. Violation.
Introduction to Biostatistics and Bioinformatics Regression and Correlation.
BASIC STATISTICAL CONCEPTS Chapter Three. CHAPTER OBJECTIVES Scales of Measurement Measures of central tendency (mean, median, mode) Frequency distribution.
I271B QUANTITATIVE METHODS Regression and Diagnostics.
KNN Ch. 3 Diagnostics and Remedial Measures Applied Regression Analysis BUSI 6220.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.
Real Estate Sales Forecasting Regression Model of Pueblo neighborhood North Elizabeth Data sources from Pueblo County Website.
Section 4.1 Transforming Relationships AP Statistics.
Section 6.4 Inferences for Variances. Chi-square probability densities.
1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 
Variance Stabilizing Transformations. Variance is Related to Mean Usual Assumption in ANOVA and Regression is that the variance of each observation is.
Chi Square Test for Goodness of Fit Determining if our sample fits the way it should be.
Homogeneity of Variance Pooling the variances doesn’t make sense when we cannot assume all of the sample Variances are estimating the same value. For two.
Lecturer: Ing. Martina Hanová, PhD.. Regression analysis Regression analysis is a tool for analyzing relationships between financial variables:  Identify.
Statistics 350 Lecture 2. Today Last Day: Section Today: Section 1.6 Homework #1: Chapter 1 Problems (page 33-38): 2, 5, 6, 7, 22, 26, 33, 34,
Multiple Regression Analysis Bernhard Kittel Center for Social Science Methodology University of Oldenburg.
Statistical Hydrology 1 Dr. Muhammad Ajmal Lecturer, Agri. Engg. Dept. UET Peshawar DATA TRANSFORMATION.
Sampling and Sampling Distributions
Covariance/ Correlation
Regression Analysis AGEC 784.
Modeling in R Sanna Härkönen.
Chapter 12: Regression Diagnostics
Covariance/ Correlation
Covariance/ Correlation
BIVARIATE REGRESSION AND CORRELATION
Model diagnostics Tim Paine, modified from Zarah Pattison’s slides
Random Sampling Population Random sample: Statistics Point estimate
Statistical Process Control
Regression is the Most Used and Most Abused Technique in Statistics
Homogeneity of Variance
Adequacy of Linear Regression Models
Adequacy of Linear Regression Models
Covariance/ Correlation
Checking the data and assumptions before the final analysis.
Chapter 14 Inference for Regression
Adequacy of Linear Regression Models
Adequacy of Linear Regression Models
Presentation transcript:

Lecture 4 Normal distribution? Sample data at :

List of nice things Equal variance Normal distribution Independent variables Linear relationship

Example 1 Is there a relation between the number of apples given to a teacher and the grade given by a teacher? Normality? –Standadized residuals –P-P plots (Q-Q plots) –Does it look nice?

Kolmogorov-Smirnov test Tests if a sample of variables are normal. Tests if the observed distribution function S(x) is significantly different from a hypothetical distribution function F(x). Lilliefors modification because F(x) is estimated from S(x).

Example 2 Is there a relation between the number of muffins given to a teacher and the grade given by a teacher?

Transformations Most statistical models are linear. Equal variance. c = 1: No transformation c = -1: reciprocal c = ½ : square root c = 0: logarithmic

Outliers Tjeck the data Tjeck the labbook Trimmed mean Common sense and your vast experience