Regression Problem 1 What is your forecast fore the next period? In which period are we? 7. Next period is 8. Standard Deviation of Forecast = 2.09.

Slides:



Advertisements
Similar presentations
© 2003 Anita Lee-Post Forecasting Part 3 By Anita Lee-Post By.
Advertisements

4-1 Operations Management Forecasting Chapter 4 - Part 2.
Collinearity. Symptoms of collinearity Collinearity between independent variables – High r 2 High vif of variables in model Variables significant in simple.
Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.
Qualitative Forecasting Methods
1-1 Regression Models  Population Deterministic Regression Model Y i =  0 +  1 X i u Y i only depends on the value of X i and no other factor can affect.
1 BA 275 Quantitative Business Methods Residual Analysis Multiple Linear Regression Adjusted R-squared Prediction Dummy Variables Agenda.
Forecasting.
Correlation and Regression Analysis
Chapter 13 Introduction to Linear Regression and Correlation Analysis
Chapter 13 Forecasting.
Linear Regression and Correlation Analysis
Chapter 14 Introduction to Linear Regression and Correlation Analysis
Exponential Smoothing 1 Ardavan Asef-Vaziri 6/4/2009 Forecasting-2 Chapter 7 Demand Forecasting in a Supply Chain Forecasting -2.2 Regression Analysis.
Active Learning Lecture Slides
Introduction to Linear Regression and Correlation Analysis
1 DSCI 3023 Linear Regression Outline Linear Regression Analysis –Linear trend line –Regression analysis Least squares method –Model Significance Correlation.
MAT 254 – Probability and Statistics Sections 1,2 & Spring.
Non-continuous Relationships If the relationship between the dependent variable and an independent variable is non-continuous a slope dummy variable can.
Linear Trend Lines Y t = b 0 + b 1 X t Where Y t is the dependent variable being forecasted X t is the independent variable being used to explain Y. In.
Simple Linear Regression. Correlation Correlation (  ) measures the strength of the linear relationship between two sets of data (X,Y). The value for.
1 FORECASTING Regression Analysis Aslı Sencer Graduate Program in Business Information Systems.
Chapter 6 & 7 Linear Regression & Correlation
Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.
Bivariate Distributions Overview. I. Exploring Data Describing patterns and departures from patterns (20%-30%) Exploring analysis of data makes use of.
ESTIMATING & FORECASTING DEMAND Chapter 4 slide 1 Regression Analysis estimates the equation that best fits the data and measures whether the relationship.
Managerial Economics Demand Estimation. Scatter Diagram Regression Analysis.
Run the colour experiment where kids write red, green, yellow …first.
Introduction to Linear Regression
Chap 12-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 12 Introduction to Linear.
EQT 373 Chapter 3 Simple Linear Regression. EQT 373 Learning Objectives In this chapter, you learn: How to use regression analysis to predict the value.
Applied Quantitative Analysis and Practices LECTURE#23 By Dr. Osman Sadiq Paracha.
Ch4 Describing Relationships Between Variables. Section 4.1: Fitting a Line by Least Squares Often we want to fit a straight line to data. For example.
Aim: How do we compute the coefficients of determination and the standard error of estimate?
Correlation Correlation is used to measure strength of the relationship between two variables.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Ordinary Least Squares Estimation: A Primer Projectseminar Migration and the Labour Market, Meeting May 24, 2012 The linear regression model 1. A brief.
Steps in Regression Analysis (1) Choose the dependent and independent variables (2) Examine the scatterplots and the correlation matrix Check for any high.
1 Forecasting Formulas Symbols n Total number of periods, or number of data points. A Actual demand for the period (  Y). F Forecast demand for the period.
Linear Trend Lines = b 0 + b 1 X t Where is the dependent variable being forecasted X t is the independent variable being used to explain Y. In Linear.
Forecasting. 預測 (Forecasting) A Basis of Forecasting In business, forecasts are the basis for budgeting and planning for capacity, sales, production and.
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
CORRELATION: Correlation analysis Correlation analysis is used to measure the strength of association (linear relationship) between two quantitative variables.
Chapter 16 Data Analysis: Testing for Associations.
4-1 Operations Management Forecasting Chapter 4 - Part 2.
SCHEDULE OF WEEK 10 Project 2 is online, due by Monday, Dec 5 at 03:00 am 2. Discuss the DW test and how the statistic attains less/greater that 2 values.
Statistics for Business and Economics 8 th Edition Chapter 11 Simple Regression Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Ch.
Lecture 10: Correlation and Regression Model.
Psy302 Quantitative Methods
Scatter Diagrams scatter plot scatter diagram A scatter plot is a graph that may be used to represent the relationship between two variables. Also referred.
Essential Statistics Chapter 51 Least Squares Regression Line u Regression line equation: y = a + bx ^ –x is the value of the explanatory variable –“y-hat”
Linear Prediction Correlation can be used to make predictions – Values on X can be used to predict values on Y – Stronger relationships between X and Y.
1.What is Pearson’s coefficient of correlation? 2.What proportion of the variation in SAT scores is explained by variation in class sizes? 3.What is the.
Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
1 Forecasting/ Causal Model MGS Forecasting Quantitative Causal Model Trend Time series Stationary Trend Trend + Seasonality Qualitative Expert.
The coefficient of determination, r 2, is The fraction of the variation in the value of y that is explained by the regression line and the explanatory.
Section 9.3 Measures of Regression and Prediction Intervals.
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.
Assignable variation Deviations with a specific cause or source. forecast bias or assignable variation or MSE? Click here for Hint.
Introduction Many problems in Engineering, Management, Health Sciences and other Sciences involve exploring the relationships between two or more variables.
REGRESSION (R2).
LESSON 23: MULTIPLE REGRESSION
The Weather Turbulence
STEM Fair Graphs.
1.7 Nonlinear Regression.
Chapter 7 Demand Forecasting in a Supply Chain
OUTLINE Questions? Quiz Go over homework Next homework Forecasting.
Forecasting 3 Regression Analysis Ardavan Asef-Vaziri
Presentation transcript:

Regression Problem 1 What is your forecast fore the next period? In which period are we? 7. Next period is 8. Standard Deviation of Forecast = 2.09

Given the following regression report for the relationship between demand and time. (Demand is the dependent variable and Time is the independent variable) What is your forecast for the next period? 52+10(20+1) = 262 What is the standard deviation of your forecast for the next period? Is there a strong relationship between the dependent and the independent variables? Yes R-Square (Coefficient of Determination) id 0.95, Multiple R (Correlation Coefficient) is 0.97, p-value is very small Is the relationship positive or negative? Positive. We can check it by Multiple R being + or b1 being + Regression Problem 2

Short Questions If the coefficient of determination between interest rate (x) and residential real estate prices (y) is 0.85, this means that: A) 85% of the y values are positive B) 85% of the variation in y can be explained by the variation in x C) 85% of the x values are equal D) 85% of the variation in x can be explained by the variation in y E) none of the above 2. Which value of the coefficient of correlation (r) indicates a stronger correlation than 0.7? A) 0.6 B) -0.9 C) 0.4 D) -0.5 E) none of the above

3. In a good regression we expect A)P-value to be high and R-square to be high B)P-value to be low and R-square to be low C)P-value to be low and R-square to be high D)P-value to be high and R-square to be low E)none of the answers Short Questions Discuss the relationship between MAD in moving average and exponential smoothing and Standard Error in regression. Standard Error in regression is an estimate of the Standard Deviation of the Forecast. Standard Deviation of the Forecast = Standard Error MAD in moving average and exponential smoothing is an estimate of the Standard Deviation of the Forecast. Standard Deviation of the Forecast = 1.25MAD