METODE PERAMALAN Pertemuan 16

Slides:



Advertisements
Similar presentations
Forecasting OPS 370.
Advertisements

Chapter 12 Simple Linear Regression
Analisis Varians Dwi Arah Pertemuan 22 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Regresi Linear Sederhana Pertemuan 01 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Pengujian Parameter Regresi Ganda Pertemuan 22 Matakuliah: L0104/Statistika Psikologi Tahun: 2008.
Uji Kelinearan dan Keberartian Regresi Pertemuan 02 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Regresi dan Korelasi Linear Pertemuan 19
Bina Nusantara Model Ramalan Pertemuan 14: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008.
Regression Analysis Once a linear relationship is defined, the independent variable can be used to forecast the dependent variable. Y ^ = bo + bX bo is.
Simple Linear Regression and Correlation
4-1 Operations Management Forecasting Chapter 4 - Part 2.
CORRELATON & REGRESSION
Regresi dan Analisis Varians Pertemuan 21 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.
Class 20: Chapter 12S: Tools Class Agenda –Answer questions about the exam News of Note –Elections Results—Time to come together –Giants prove that nice.
Korelasi Ganda Dan Penambahan Peubah Pertemuan 13 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Interaksi Dalam Regresi (Lanjutan) Pertemuan 25 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Forecasting.
1 Pertemuan 13 Uji Koefisien Korelasi dan Regresi Matakuliah: A0392 – Statistik Ekonomi Tahun: 2006.
Chapter 3 Forecasting McGraw-Hill/Irwin
Pengujian Parameter Koefisien Korelasi Pertemuan 04 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Operations Management R. Dan Reid & Nada R. Sanders
4 Forecasting PowerPoint presentation to accompany Heizer and Render
Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Korelasi dalam Regresi Linear Sederhana Pertemuan 03 Matakuliah: I0174 – Analisis Regresi Tahun: Ganjil 2007/2008.
Pertemua 19 Regresi Linier
Korelasi dan Regresi Linear Sederhana Pertemuan 25
T T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of.
1 Pertemuan 13 Regresi Linear dan Korelasi Matakuliah: I0262 – Statistik Probabilitas Tahun: 2007 Versi: Revisi.
Slides 13b: Time-Series Models; Measuring Forecast Error
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Simple Linear Regression Analysis Chapter 13.
1 DSCI 3023 Linear Regression Outline Linear Regression Analysis –Linear trend line –Regression analysis Least squares method –Model Significance Correlation.
The Importance of Forecasting in POM
© 2006 Prentice Hall, Inc.4 – 1 Forcasting © 2006 Prentice Hall, Inc. Heizer/Render Principles of Operations Management, 6e Operations Management, 8e.
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.
1 FORECASTING Regression Analysis Aslı Sencer Graduate Program in Business Information Systems.
Forecasting OPS 370.
© 2001 Prentice-Hall, Inc. Statistics for Business and Economics Simple Linear Regression Chapter 10.
Section 5.2: Linear Regression: Fitting a Line to Bivariate Data.
Forecasting MD707 Operations Management Professor Joy Field.
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.
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.
10B11PD311 Economics REGRESSION ANALYSIS. 10B11PD311 Economics Regression Techniques and Demand Estimation Some important questions before a firm are.
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.
4-1 Operations Management Forecasting Chapter 4 - Part 2.
Chapter 4 Class 2.
STATISTICS 12.0 Correlation and Linear Regression “Correlation and Linear Regression -”Causal Forecasting Method.
1 Pertemuan 22 Regresi dan Korelasi Linier Sederhana-2 Matakuliah: A0064 / Statistik Ekonomi Tahun: 2005 Versi: 1/1.
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Port of Baltimore Exponential Smoothing Example QtrActual Tonnage Unloaded.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
WELCOME TO THE PRESENTATION ON LINEAR REGRESSION ANALYSIS & CORRELATION (BI-VARIATE) ANALYSIS.
13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.
To accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Fourth Edition  1996 Addison-Wesley Publishing Company, Inc. All rights.
Correlation and Regression Basic Concepts. An Example We can hypothesize that the value of a house increases as its size increases. Said differently,
3-1Forecasting Weighted Moving Average Formula w t = weight given to time period “t” occurrence (weights must add to one) The formula for the moving average.
CHAPTER 3 Describing Relationships
Linear Regression Special Topics.
Regresi dan Korelasi Pertemuan 10
Pengertian... (1). Pengertian... (1) Pengertian .... (2)
Pengujian Parameter Regresi dan Korelasi Pertemuan 20
Basic Estimation Techniques
INFERENSIA KORELASI DAN REGRESI LINIER SEDERHANA Pertemuan 12
Exponential Smoothing with Trend Adjustment - continued
Least-Squares Regression
Correlation and Regression
Correlation and Regression
Least-Squares Regression
Correlation & Trend Lines
Forecasting 3 Regression Analysis Ardavan Asef-Vaziri
Presentation transcript:

METODE PERAMALAN Pertemuan 16 Matakuliah : J1186 - Analisis Kuantitatif Bisnis Tahun : 2009/2010 METODE PERAMALAN Pertemuan 16

Framework Metode Peramalan Regresi Aplikasi Model (Model Regresi Sederhana dan Regresi Berganda) Koefisien Determinasi Interpretasi Hasil dan Analisis Model Bina Nusantara University

d). Linear Trend Line Rumus Umum: Y = a + bx dimana: a = intersep b = kemiringan x = periode waktu Y = ramalan untuk periode Bina Nusantara University

Linear Trend Projection Model b > 0 a Y b < 0 a X Bina Nusantara University

Contoh : Linear Trend Projection Period (x) 1 8 2 11 3 13 4 15 5 19 Sales (y) xy 60 95 22 39 xy=224 x2 9 16 25 x2=55 x=3 y=13.2 Bina Nusantara University

Lanjutan Period (x) MA ES 1 2 3 4 5 8 11 13 15 19 Err. 6 10.67 13.00 15.67 12 13.5 16.25 4.33 6.00 Sales (y) 3.0 5.5 TP 21.0 18.4 15.8 -0.8 0.6 TP = Trend Projection: Y = 5.4 + 2.6x Small errors! Bina Nusantara University

Kesalahan Peramalan Kesalahan Peramalan = Ukuran yang digunakan: Mean Absolute Deviation (MAD) Mean Squared Error (MSE) Pilih metode peramalan yang menghasilkan MAD atau MSE terkecil Bina Nusantara University

Model Regresi Linear Shows linear relationship between dependent & explanatory variables Example: Sales & advertising (not time) Y-intercept Slope ^ Y = a  b X i i Dependent (response) variable Independent (explanatory) variable Bina Nusantara University

Linear Regression Model Y a Y b X i =  Error  i Observed value Y a b X =  Regression line Error ^ i i X Bina Nusantara University

Interpretasi Koefisien Regresi Slope (b): Y changes by b units for each 1 unit increase in X. If b = +2, then sales (Y) is forecast to increase by 2 for each 1 unit increase in advertising (X). Y-intercept (a): Average value of Y when X = 0. If a = 4, then average sales (Y) is expected to be 4 when advertising (X) is 0. Bina Nusantara University

Koefisien Determinasi Answers: ‘How strong is the linear relationship between the variables?’ Coefficient of correlation - r Measures degree of association; ranges from -1 to +1 Coefficient of determination - r2 Amount of variation explained by regression equation. Used to evaluate quality of linear relationship. Bina Nusantara University

Koefisien Korelasi Bina Nusantara University

Selecting Forecasting Model Example You’re a marketing analyst for Hasbro Toys. You’ve forecast sales with a linear regression model & exponential smoothing. Which model do you use? Linear Regression Exponential Actual Model Smoothing Year Sales Forecast Forecast (.9) 1 1 0.6 1.00 2 1 1.3 1.00 3 2 2.0 1.00 4 2 2.7 1.90 5 4 3.4 1.99 This slide begins an example of choosing a model. Bina Nusantara University

Linear Regression 1.10 Year Y i F’cast 1 0.6 0.4 0.16 2 1.3 -0.3 0.09 2.0 0.0 0.00 4 2.7 -0.7 0.49 0.7 5 3.4 0.36 Total Error Error2 |Error| 1.10 MSE = Σ Error2 / n = 1.10 / 5 = 0.220 MAD = Σ |Error| / n = 2.0 / 5 = 0.400 MAPE = Σ[|Error|/Actual]/n = 1.2/5 = 0.24 = 24% Bina Nusantara University

Model Eksponential Smoothing Year Y i F’cast 1 1.00 0.0 0.00 2 3 1.0 4 1.90 0.1 0.01 5 2.01 4.04 Total 0.3 5.05 3.11 Error Error2 |Error| 1.99 MSE = Σ Error2 / n = 5.05 / 5 = 1.01 MAD = Σ |Error| / n = 3.11 / 5 = 0.622 MAPE = Σ[|Error|/Actual]/n = 1.0525/5 = 0.2105 = 21% Bina Nusantara University

Mana Yang Terbaik??? Linear Regression : MSE = Σ Error2 / n = 1.10 / 5 = 0.220 MAD = Σ |Error| / n = 2.0 / 5 = 0.400 MAPE = Σ[|Error|/Actual]/n = 1.2/5 = 0.24 = 24% Exponential Smoothing: MSE = Σ Error2 / n = 5.05 / 5 = 1.01 MAD = Σ |Error| / n = 3.11 / 5 = 0.622 MAPE = Σ[|Error|/Actual]/n = 1.0525/5 = 0.2105 = 21% This slide presents the result of the calculations of MSE and MAD for the Linear and Exponential Smoothing models. Students should be asked to choose the “better” model. Students should also be asked to consider the differences between the values calculated for the error measures for a given model, and between the two models. Do these differences tell us more than simply that one model is preferable to the other? (For example, is the exponential smoothing model 22 times better than the linear model?) Bina Nusantara University