Department of Computer Science

Slides:



Advertisements
Similar presentations
Dummy Variables. Introduction Discuss the use of dummy variables in Financial Econometrics. Examine the issue of normality and the use of dummy variables.
Advertisements

Chapter 9: Simple Regression Continued
Coefficient of Determination- R²
Test of (µ 1 – µ 2 ),  1 =  2, Populations Normal Test Statistic and df = n 1 + n 2 – 2 2– )1– 2 ( 2 1 )1– 1 ( 2 where ] 2 – 1 [–
Kruskal Wallis and the Friedman Test.
Multilevel analysis with EQS. Castello2004 Data is datamlevel.xls, datamlevel.sav, datamlevel.ess.
x : Beam intensity y : Magnetic field z : Disruption length Number of Points31 Degrees of Freedom24 Reduced Chi-Sqr Residual Sum of Squares
1 Simple Linear Regression and Correlation The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES Assessing the model –T-tests –R-square.
Simple Linear Regression
Part 4 Chapter 13 Linear Regression
Professor William Greene Stern School of Business IOMS Department Department of Economics Statistical Inference and Regression Analysis: Stat-GB ,
Ignore parts with eye-ball estimation & computational formula
Useful Statistical Distributions for Econometrics Econometrics is usually concerned with the estimation of equations of the form: The normal distribution.
REGRESSION Predict future scores on Y based on measured scores on X Predictions are based on a correlation from a sample where both X and Y were measured.
Stat13-lecture 25 regression (continued, SE, t and chi-square) Simple linear regression model: Y=  0 +  1 X +  Assumption :  is normal with mean 0.
Stats for Engineers Lecture 9. Summary From Last Time Confidence Intervals for the mean t-tables Q Student t-distribution.
Managerial Economics Demand Estimation. Scatter Diagram Regression Analysis.
Econ 3790: Business and Economics Statistics Instructor: Yogesh Uppal
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 15 Multiple Regression n Multiple Regression Model n Least Squares Method n Multiple.
1 1 Slide Simple Linear Regression Coefficient of Determination Chapter 14 BA 303 – Spring 2011.
© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.
H1H1 H1H1 HoHo Z = 0 Two Tailed test. Z score where 2.5% of the distribution lies in the tail: Z = Critical value for a two tailed test.
Correlation and Regression Chapter 9. § 9.3 Measures of Regression and Prediction Intervals.
ES 240: Scientific and Engineering Computation. Chapter 13: Linear Regression 13. 1: Statistical Review Uchechukwu Ofoegbu Temple University.
Go to Table of Content Single Variable Regression Farrokh Alemi, Ph.D. Kashif Haqqi M.D.
Byron Gangnes Econ 427 lecture 3 slides. Byron Gangnes A scatterplot.
1 Multiple Regression A single numerical response variable, Y. Multiple numerical explanatory variables, X 1, X 2,…, X k.
STA 286 week 131 Inference for the Regression Coefficient Recall, b 0 and b 1 are the estimates of the slope β 1 and intercept β 0 of population regression.
Multiple Regression. Simple Regression in detail Y i = β o + β 1 x i + ε i Where Y => Dependent variable X => Independent variable β o => Model parameter.
Econ 3790: Business and Economic Statistics Instructor: Yogesh Uppal
Curve Fitting Introduction Least-Squares Regression Linear Regression Polynomial Regression Multiple Linear Regression Today’s class Numerical Methods.
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 + 
Chi Square Test for Goodness of Fit Determining if our sample fits the way it should be.
1 1 Slide © 2011 Cengage Learning Assumptions About the Error Term  1. The error  is a random variable with mean of zero. 2. The variance of , denoted.
1 AAEC 4302 ADVANCED STATISTICAL METHODS IN AGRICULTURAL RESEARCH Part II: Theory and Estimation of Regression Models Chapter 5: Simple Regression Theory.
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,
Bivariate Regression. Bivariate Regression analyzes the relationship between two variables. Bivariate Regression analyzes the relationship between two.
Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 10: Comparing Models.
The simple linear regression model and parameter estimation
Regression and Correlation of Data Summary
REGRESSION G&W p
Chapter 12 Simple Linear Regression and Correlation
Math 4030 – 10b Inferences Concerning Variances: Hypothesis Testing
Ch12.1 Simple Linear Regression
Active Learning Lecture Slides
Part Three. Data Analysis
Relationship with one independent variable
Quantitative Methods Simple Regression.
Tests in biostatistics and how to apply them? Prepared by Ajay Prakash Uniyal Department of Plant Sciences Central University of Punjab.
Econ 3790: Business and Economic Statistics
Chapter 12 Inference on the Least-squares Regression Line; ANOVA
Review of Chapter 3 where Multiple Linear Regression Model:
Statistical Analysis Professor Lynne Stokes
Linear Regression.
The Chi-Square Test The chi-square test is a statistical test commonly used to compare the observed results of a genetic cross with the expected results.
Part IV Significantly Different Using Inferential Statistics
Chapter 12 Simple Linear Regression and Correlation
Simple Linear Regression
Department of Computer Science
Model Comparison: some basic concepts
ANOVA Table Models can be evaluated by examining variability.
Linear regression Fitting a straight line to observations.
Statistical Process Control
Basic Concepts PhD Course.
Relationship with one independent variable
Simple Linear Regression
Multiple Regression Berlin Chen
Number of treatments ____________________________________________
Introduction to Biological Systems
Presentation transcript:

Department of Computer Science StepMiner Analysis Debashis Sahoo Department of Computer Science CSE291 – H00 – Lecture 10

Computing Mean – Regression Try every possible values from min to max Compute SSE (Sum of Square Error). 𝑖=𝑚𝑖𝑛 𝑚𝑎𝑥 𝑋𝑖 −𝑣 2 Determine the value that minimize the SSE

Estimating the mean Student’s t distribution Degrees of freedom (X – mu)/sigma/sqrt(n) Degree of freedom n - 1

Estimating Sum of Squares Chi-squared distribution Sum of squares of independent standard normal distribution

Ratio of Sum of Squares F-distribution Ratio of two Chi-sq distribution

StepMiner – Regression Try every possible value of threshold from min to max Compute SSE (Sum of Square Error) based on two means. 𝑖=𝑚𝑖𝑛 𝑚𝑎𝑥 𝑋𝑖 −𝑣 2 Determine the value that minimize the SSE

StepMiner Statistics