Forecasting the rate of adoption of new products © Vince Daly & Kingston University, October 2009 This work is licenced under a Creative Commons Licence.Creative.

Slides:



Advertisements
Similar presentations
1.4 – Shifting, Reflecting, and Stretching Graphs
Advertisements

Ridiculously Simple Time Series Forecasting We will review the following techniques: Simple extrapolation (the naïve model). Moving average model Weighted.
Quantitative Methods Interactions - getting more complex.
Household Projections for England Yolanda Ruiz DCLG 16 th July 2012.
Forecasting Models With Linear Trend. Linear Trend Model If a modeled is hypothesized that has only linear trend and random effects, it will be of the.
Forecasting Using the Simple Linear Regression Model and Correlation
Fuzzy Logic E. Fuzzy Inference Engine. “antecedent” “consequent”
Chapter 5. MARKET MEASUREMENT BA L.P.Chew
MARKET SUPPLY Microeconomics Made Easy by Dr. William Yacovissi Mansfield University.
1 Population Forecasting Time Series Forecasting Techniques Wayne Foss, MBA, MAI Wayne Foss Appraisals, Inc.
Standard Trend Models. Trend Curves Purposes of a Trend Curve: 1. Forecasting the long run 2. Estimating the growth rate.
Math 3C Euler’s Method Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB.
Regression in EXCEL r2 SSE b0 b1 SST.
Product Life Cycle  There are four stages to the Life Cycle Introduction Maturity Growth Decline This material.
NEW PRODUCT FORECASTING Growth curve fitting S-Curves of Growth  S-Curves are also called  Growth Models  Saturation Models  Substitution Models.
Diane Stockton Trend analysis. Introduction Why do we want to look at trends over time? –To see how things have changed What is the information used for?
BIS Application Chapter two
VCE Further Maths Least Square Regression using the calculator.
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.
Scatterplots October 14, Warm-Up Given the following domain and range in set notation, write the equivalent domain and range in algebraic notation.
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.
4.1 Solving Linear Inequalities
Graphing of Data Why do we display data with graphs?
Regression Regression relationship = trend + scatter
Time series Decomposition Farideh Dehkordi-Vakil.
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.
Linear Regression. Determine if there is a linear correlation between horsepower and fuel consumption for these five vehicles by creating a scatter plot.
Revision: Pivot Table 1. Histogram 2. Trends 3. Linear 4. Exponential
Copyright © 2010 Pearson Education, Inc Chapter Twenty-Three Report Preparation and Presentation.
2007/12/21 MR Final Report 1 An Evaluation of the Time-varying Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle.
20.3 Compound Interest. The more common form of interest used is Compound Interest. It is called compound because the interest accumulates each year is.
Chapter 4: Part One The Human Population and the Environment.
Example 16.5 Regression-Based Trend Models | 16.1a | 16.2 | 16.3 | 16.4 | 16.6 | 16.2a | 16.7 | 16.7a | 16.7b16.1a a16.7.
T T18-07 Seasonally Adjusted Linear Trend Forecast Purpose Allows the analyst to create and analyze a "Seasonally Adjusted Linear Trend" forecast.
WARM-UP: USE YOUR GRAPHING CALCULATOR TO GRAPH THE FOLLOWING FUNCTIONS Look for endpoints for the graph Describe the direction What is the shape of the.
Section 1-3: Graphing Data
Successful Business Idea Generation in a Simple Way By Mr.RAO A Seminar cum Workshop
Week 2 lesson 3: 2.3 visualizing data Week 2 lesson 3: 2.3 visualizing data Students will graph the relationship between independent and dependent variables.
1 Forecasting/ Causal Model MGS Forecasting Quantitative Causal Model Trend Time series Stationary Trend Trend + Seasonality Qualitative Expert.
Low sales High cost per customer Negative or low Create product awareness & trial Offer a basic product Usually is high; use cost-plus formula High distribution.
Selecting Appropriate Projections Input and Output Evaluation.
Example 13.3 Quarterly Sales at Intel Regression-Based Trend Models.
Welcome to Calculating the line of best fit for data (linear regression) Claude Zanardo.
BREAK-EVEN ANALYSIS LEARNING OBJECTIVES 1.To understand and calculate the contribution 2.To check understanding and calculation using the breakeven formula.
STATISTICS 13.0 Linear Time Series Trend “Time Series ”- Time Series Forecasting Method.
CCSS.Math.Content.8.SP.A.1 Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities.
Unit III - Services Marketing - Mr.K.Mohan Kumar
Strategic Management – Part II Forecasting
Linear Inequalities in Two Variables
Prepared by Vince Zaccone
Forecasting Methods ISAT /10/2018.
Product Strategy الفصل التاسع
Investigating Relationships
Scatter Graphs Fitting a line of best fit to a non-linear model
ANATOMY OF THE STANDARD NORMAL CURVE
Moving Averages OCR Stage 8.
© 2015 by Cengage Learning Inc. All Rights Reserved.
Response Curves Ken Homa.
Graphing Techniques.
A graphing calculator is required for some problems or parts of problems 2000.
Section 2-5 What Are Graphs?
Scatter Plots Unit 11 B.
R. D. Shelton Tarek Fadel WTEC ITRI
Ch 9.
Making Sense of Statistics:
Proportional or Non-proportional?
Interpolation Theory Section 1
Cases. Simple Regression Linear Multiple Regression.
Design period is estimated based on the following:
Regression and Correlation of Data
Presentation transcript:

Forecasting the rate of adoption of new products © Vince Daly & Kingston University, October 2009 This work is licenced under a Creative Commons Licence.Creative Commons

Early adopters Take off Mass adoption Slow down The S-curve shape reflects the stages of market development.

S: market saturation level (i.e. max possible sales) = 100 in this example P t : penetration of market at time t Can be changed by logistic transformation to The S-curve has a complicated formula but a change of variables produces a simple linear equivalent. The non-linear S-curve Its linear transformation

MSc student T. Vrionis gathered the OECD data for % penetration of this market EXCELs TREND function can be used to fit a linear trend to the transformed data by the OLS method, and also to extrapolate this trend. The logistic transformation can then be reversed for a graph that shows observed and extrapolated market penetration. DATA:

Saturation Levels Vary By Segment ADVENTIS PLC have used S-Curve calculations as a basis for forecasting development of various M-commerce markets © Adventis PLC, 2001 Extract from presentation to IBC conference Forecasting the Telecoms Market, London 2001