Economics 173 Business Statistics Lecture 24 © Fall 2001, Professor J. Petry

Slides:



Advertisements
Similar presentations
1 1 Chapter 5: Multiple Regression 5.1 Fitting a Multiple Regression Model 5.2 Fitting a Multiple Regression Model with Interactions 5.3 Generating and.
Advertisements

© Department of Statistics 2012 STATS 330 Lecture 32: Slide 1 Stats 330: Lecture 32.
ECON 251 Research Methods 11. Time Series Analysis and Forecasting.
Operation Research By Anitha Chandran Chitra.R Radha.R Sudhit Sethi.
Chapter 12 Goodness-of-Fit Tests and Contingency Analysis
Conclusion to Bivariate Linear Regression Economics 224 – Notes for November 19, 2008.
Economics 173 Business Statistics Lecture 14 Fall, 2001 Professor J. Petry
Bar Graphs. Bar Graph or Double Bar Graph (REVIEW) A bar graph uses length of bars to represent numbers and COMPARE data. In some cases, the use of a.
Econ 140 Lecture 151 Multiple Regression Applications Lecture 15.
Chapter 13 Multiple Regression
Qualitative Forecasting Methods
Statistics for Managers Using Microsoft® Excel 5th Edition
Multiple Regression Involves the use of more than one independent variable. Multivariate analysis involves more than one dependent variable - OMS 633 Adding.
Econ 140 Lecture 181 Multiple Regression Applications III Lecture 18.
Econ 140 Lecture 171 Multiple Regression Applications II &III Lecture 17.
MODEL BUILDING IN REGRESSION MODELS. Model Building and Multicollinearity Suppose we have five factors that we feel could linearly affect y. If all 5.
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics 11 th Edition.
Chapter 11 Multiple Regression.
7.1 Lecture #7 Studenmund(2006) Chapter 7 Objective: Applications of Dummy Independent Variables.
Saturday May 02 PST 4 PM. Saturday May 02 PST 10:00 PM.
1 4. Multiple Regression I ECON 251 Research Methods.
Sequences and Series Day 2&3 Happy Monday
Chapter 11 Solved Problems 1. Exhibit 11.2 Example Linear and Nonlinear Trend Patterns 2.
Ch. 14: The Multiple Regression Model building
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 15-1 Chapter 15 Multiple Regression Model Building Basic Business Statistics 11 th Edition.
Multiple Regression Dr. Andy Field.
Guide to Using Excel For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 6th Ed. Chapter 14: Multiple Regression.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-1 Chapter 13 Introduction to Multiple Regression Statistics for Managers.
Multiple Linear Regression Response Variable: Y Explanatory Variables: X 1,...,X k Model (Extension of Simple Regression): E(Y) =  +  1 X 1 +  +  k.
Statistics for the Social Sciences Psychology 340 Fall 2013 Thursday, November 21 Review for Exam #4.
Chapter 12 Multiple Regression and Model Building.
Lecture Slide #1 OLS Review Review of Multivariate OLS –Topics –Data Analysis –Questions Exam Particulars.
Multiple Regression Analysis
Chapter 6 Regression Algorithms in Data Mining
Model Selection1. 1. Regress Y on each k potential X variables. 2. Determine the best single variable model. 3. Regress Y on the best variable and each.
Forecasting MKA/13 1 Meaning Elements Steps Types of forecasting.
Correlation and Linear Regression. Evaluating Relations Between Interval Level Variables Up to now you have learned to evaluate differences between the.
26134 Business Statistics Maths Study Centre CB Tutorial 8: Probability Distribution Key concepts in this tutorial.
Definition of Time Series: An ordered sequence of values of a variable at equally spaced time intervals. The variable shall be time dependent.
Correlation Analysis. A measure of association between two or more numerical variables. For examples height & weight relationship price and demand relationship.
Multiple Regression Lab Chapter Topics Multiple Linear Regression Effects Levels of Measurement Dummy Variables 2.
Economics 173 Business Statistics Lecture 20 Fall, 2001© Professor J. Petry
Chapter 11 Linear Regression Straight Lines, Least-Squares and More Chapter 11A Can you pick out the straight lines and find the least-square?
MGS3100_04.ppt/Sep 29, 2015/Page 1 Georgia State University - Confidential MGS 3100 Business Analysis Regression Sep 29 and 30, 2015.
Lesson Multiple Regression Models. Objectives Obtain the correlation matrix Use technology to find a multiple regression equation Interpret the.
Regression Models Fit data Time-series data: Forecast Other data: Predict.
© Buddy Freeman, 2015 Multiple Linear Regression (MLR) Testing the additional contribution made by adding an independent variable.
Graphs.  Graphs are used to present numerical information in picture form.  Two common types of graphs are bar graphs and broken-line graphs. New Car.
Chapter 13 Multiple Regression
1 Experimental Statistics - week 14 Multiple Regression – miscellaneous topics.
Click the button to begin. 12:25 What time does this clock show? 5:00 4:00.
Economics 173 Business Statistics Lecture 19 Fall, 2001© Professor J. Petry
Introduction to Basic Statistical Tools for Research OCED 5443 Interpreting Research in OCED Dr. Ausburn OCED 5443 Interpreting Research in OCED Dr. Ausburn.
Economics 173 Business Statistics Lecture 23 © Fall 2001, Professor J. Petry
Economics 173 Business Statistics Lecture 10 Fall, 2001 Professor J. Petry
Economics 173 Business Statistics Lecture 26 © Fall 2001, Professor J. Petry
7.1: Simplifying Rational Expressions March 31, 2009.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 14-1 Chapter 14 Introduction to Multiple Regression Basic Business Statistics 10 th Edition.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 15-1 Chapter 15 Multiple Regression Model Building Basic Business Statistics 10 th Edition.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 14-1 Chapter 14 Multiple Regression Model Building Statistics for Managers.
Statistics for Managers Using Microsoft Excel, 5e © 2008 Prentice-Hall, Inc.Chap 14-1 Statistics for Managers Using Microsoft® Excel 5th Edition Chapter.
Applied Quantitative Analysis and Practices LECTURE#28 By Dr. Osman Sadiq Paracha.
Economics 173 Business Statistics Lecture 27 © Fall 2001, Professor J. Petry
Happy Days by Charles Fox and Norman Gimbel PowerPoint by Camille Page.
Biostatistics Regression and Correlation Methods Class #10 April 4, 2000.
Solve. Make sure to Simplify all answers WARM UP MONDAY, APRIL 14 (USE YOUR WARM UP SHEET FROM LAST WEEK)
DSCI 346 Yamasaki Lecture 7 Forecasting.
Multiple Regression Prof. Andy Field.
Full Model: contain ALL coefficients of interest
Presentation transcript:

Economics 173 Business Statistics Lecture 24 © Fall 2001, Professor J. Petry

2 –The moving average method does not provide smoothed values (moving average values) for the first and last set of periods Exponential Smoothing –The exponential smoothing method provides smoothed values for all the time periods observed. >the moving average method considers only the observations included in the calculation of the average value. –When smoothing the time series at time t, –exponential smoothing considers all the data available at t (yt, yt-1,…)

3 Exponentially smoothed time series S t = exponentially smoothed time series at time t. y t = time series at time t. S t-1 = exponentially smoothed time series at time t-1.  = smoothing constant, where 0 <=  <=1. S t =  y t + (1-  )S t-1

4 The process (example 20.2) Calculate the gasoline smoothed time series using exponential smoothing with  =.2. Set S 1 = y 1 S 1 = 39 S 2 = wy 2 + (1-w)S 1 S 2 = (.2)(37) + (1-.2)(39) = 38.6 S 3 = wy 3 + (1-w)S 2 S 3 = (.2)(61) + (1-.2)(38.6) = 43.1

5 The process (example 20.2) The smoothed series with  =.2 The smoothed series with  =.7 Small  provides a lot of smoothing

6 1.Using the now familiar data from Armani’s pizza, exponentially smooth the data. Week 1 Monday35 Tuesday42 Wednesday56 Thursday46 Friday67 Saturday51 Sunday39 Example

7 Project II – Human Resources Application Your team comprises Human Resources You are given a list of questions that management wants answered Criterion for hiring the most productive employees, along with a few related issues We give you the data 1 Dependent variable –Dollar sales 17 Independent Variables –Motivation of worker, enjoy customer interaction, education level, gender, work experience, age... 1,000 observations for each variable Involves dummy variables, transformations, etc

8 Project II – Human Resources Application Graph your independent variable with dependent variable as a first step to insure relationship is properly specified. You will use two regression models “Full Model”, and “Reduced Model” Selection will be based on t-tests of coefficient values, adjusted R 2 and Partial F-test (See below) Which version you use to answer which questions is generally specified in the project description—though your judgment is important General Report Guidelines still apply. Include 1 page executive summary The body of the report should be no more than 5 pages double spaced (anything beyond 5 pages will not be read). Include detailed tables, graphs, etc in a professionally formatted and presented appendix.

9 Project II – Partial F-test To determine which variables we retain and which we eliminate from a multiple regression, use: individual t-test, (beware of multicollinearity) adjusted R 2 Partial F-test –Like the F-test, used to simultaneously test whether numerous Beta coefficients equal 0. run a “full regression” with all variables included run a “reduced regression” after eliminating certain Xs use the difference in SSR b/n regressions as test statistic

10 Project II – Partial F-test