1 BA 275 Quantitative Business Methods Please turn in Progress Report #2 Quiz # 5 Simple Linear Regression Introduction Case Study: Housing Prices Agenda.

Slides:

Advertisements

Similar presentations

BA 275 Quantitative Business Methods

Advertisements

Student Quiz Grades Test Grades 1.Describe the association between Quiz Grades and Test Grades. 2.Write the.

STAT E-100 Section 2—Monday, September 23, :30-6:30PM, SC113.

1 Simple Linear Regression and Correlation The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES Assessing the model –T-tests –R-square.

BA 555 Practical Business Analysis

1 BA 275 Quantitative Business Methods Statistical Inference: Confidence Interval Estimation Estimating the population mean  Margin of Error Sample Size.

1 BA 275 Quantitative Business Methods Residual Analysis Multiple Linear Regression Adjusted R-squared Prediction Dummy Variables Agenda.

Statistics 350 Lecture 16. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.

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

Fall 2006 – Fundamentals of Business Statistics 1 Chapter 13 Introduction to Linear Regression and Correlation Analysis.

1 BA 275 Quantitative Business Methods Review Statistical Inference on Two Populations The p-value Approach Practice Problems Office Hours: Monday, 2/27/06:

1 BA 275 Quantitative Business Methods Simple Linear Regression Introduction Case Study: Housing Prices Agenda.

Chapter Topics Types of Regression Models

Stat 217 – Week 10. Outline Exam 2 Lab 7 Questions on Chi-square, ANOVA, Regression  HW 7  Lab 8 Notes for Thursday’s lab Notes for final exam Notes.

7/2/ Lecture 51 STATS 330: Lecture 5. 7/2/ Lecture 52 Tutorials  These will cover computing details  Held in basement floor tutorial lab,

1 BA 275 Quantitative Business Methods Quiz #7 Analyzing Survey Data Hypothesis Testing for Proportion(s) Chi-square Test of Independence Contingency Tables.

1 BA 555 Practical Business Analysis Review of Statistics Confidence Interval Estimation Hypothesis Testing Linear Regression Analysis Introduction Case.

Correlation and Regression Analysis

Introduction to simple linear regression ASW, Economics 224 – Notes for November 5, 2008.

Correlation and Regression Quantitative Methods in HPELS 440:210.

McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Simple Linear Regression Analysis Chapter 13.

Maths Study Centre CB Open 11am – 5pm Semester Weekdays

MAT 254 – Probability and Statistics Sections 1,2 & Spring.

Chapter 14 Introduction to Multiple Regression Sections 1, 2, 3, 4, 6.

Review for Exam 2 (Ch.6,7,8,12) Ch. 6 Sampling Distribution

OPIM 303-Lecture #8 Jose M. Cruz Assistant Professor.

1 Experimental Statistics - week 10 Chapter 11: Linear Regression and Correlation.

Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.

Example 1: page 161 #5 Example 2: page 160 #1 Explanatory Variable - Response Variable - independent variable dependent variable.

Correlation Correlation is used to measure strength of the relationship between two variables.

STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.

Aim: Review for Exam Tomorrow. Independent VS. Dependent Variable Response Variables (DV) measures an outcome of a study Explanatory Variables (IV) explains.

June 11, 2008Stat Lecture 10 - Review1 Midterm review Chapters 1-5 Statistics Lecture 10.

Statistics: Unlocking the Power of Data Lock 5 Exam 2 Review STAT 101 Dr. Kari Lock Morgan 11/13/12 Review of Chapters 5-9.

Assignment (1) WEEK 3.

ECON 338/ENVR 305 CLICKER QUESTIONS Statistics – Question Set #8 (from Chapter 10)

Chapter Bivariate Data (x,y) data pairs Plotted with Scatter plots x = explanatory variable; y = response Bivariate Normal Distribution – for.

LECTURE 9 Tuesday, 24 FEBRUARY STA291 Fall Administrative 4.2 Measures of Variation (Empirical Rule) 4.4 Measures of Linear Relationship Suggested.

Chapter 12: Correlation and Linear Regression 1.

Chapter 14: Inference for Regression. A brief review of chapter 4... (Regression Analysis: Exploring Association BetweenVariables )  Bi-variate data.

Economics 173 Business Statistics Lecture 10 Fall, 2001 Professor J. Petry

1 Simple Linear Regression and Correlation Least Squares Method The Model Estimating the Coefficients EXAMPLE 1: USED CAR SALES.

STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.

Lecture 10 Introduction to Linear Regression and Correlation Analysis.

BPA CSUB Prof. Yong Choi. Midwest Distribution 1. Create scatter plot Find out whether there is a linear relationship pattern or not Easy and simple using.

Topics, Summer 2008 Day 1. Introduction Day 2. Samples and populations Day 3. Evaluating relationships Scatterplots and correlation Day 4. Regression and.

Video Conference 1 AS 2013/2012 Chapters 10 – Correlation and Regression 15 December am – 11 am Puan Hasmawati Binti Hassan

1 BA 275 Quantitative Business Methods Analyzing Survey Data Chi-square Test of Independence Contingency Tables Chi-square Test of a Contingency Table.

Introduction. We want to see if there is any relationship between the results on exams and the amount of hours used for studies. Person ABCDEFGHIJ Hours/

Introduction Many problems in Engineering, Management, Health Sciences and other Sciences involve exploring the relationships between two or more variables.

1 Objective Given two linearly correlated variables (x and y), find the linear function (equation) that best describes the trend. Section 10.3 Regression.

Statistics and probability Dr. Khaled Ismael Almghari Phone No:

26134 Business Statistics Week 4 Tutorial Simple Linear Regression Key concepts in this tutorial are listed below 1. Detecting.

PSY 325 aid Something Great/psy325aid.com

REGRESSION (R2).

Linear Regression.

Objectives By the end of this lecture students will:

Simple Linear Regression - Introduction

BA 275 Quantitative Business Methods

Correlation and Regression

Review of Hypothesis Testing

Regression Models - Introduction

BA 275 Quantitative Business Methods

The Least-Squares Line Introduction

Multiple Linear Regression

EQUATION 4.1 Relationship Between One Dependent and One Independent Variable: Simple Regression Analysis.

Correlation & Trend Lines

Cases. Simple Regression Linear Multiple Regression.

Statistics 101 CORRELATION Section 3.2.

Regression and Correlation of Data

Presentation transcript:

1 BA 275 Quantitative Business Methods Please turn in Progress Report #2 Quiz # 5 Simple Linear Regression Introduction Case Study: Housing Prices Agenda

2 Midterm Examination #2 Monday, February 26, 2007 in class for 110 minutes. It covers materials assigned in Week 4 – 7. Need a calculator and a good night sleep. Close book/note/friends except for a 4” x 6” index card. I will provide you 1. the normal probability table. 2. Table D A 4” x 6” index card is allowed. Write your name and section number on top and turn it in with your exam. Office Hours: Friday, 2/23/2007, 3:10 – 5:00 p.m. Monday, 2/26/2007, 8:30 – 11:00 a.m.

3 Regression Analysis A technique to examine the relationship between an outcome variable (dependent variable, Y) and an explanatory variable (independent variable, X) => Simple Regression Analysis and a group of explanatory variables (independent variables, X1, X2, …). => Multiple Regression Analysis

4 Case Study: Housing Prices Does AREA affect PRICE? If so, how large is the effect? What is the expected price of a house = 2000 sf?

5 Initial Analysis

6 Correlation  (rho): Population correlation (its value most likely is unknown.) r: Sample correlation (its value can be calculated from the sample.) Correlation is a measure of the strength of linear relationship. Correlation falls between –1 and 1. No linear relationship if correlation is close to 0.

7 Correlation (  vs. r) Is a  or r? Sample size P-value for H 0 :  = 0 H a :  ≠ 0

8 Regression Report from SG

9 Fitted ModelFitted Model: Least Squares Line b1b1 b0b0 Least squares line: estimated_Price = – Area.

10 Fitted ModelFitted Model: Least Squares Line