PRESENTED BY
GROUP-3
OVERVIEW:
1. FACTS ABOUT HONDA MOTORS
2. MOTORBIKE MARKET IN US BEFORE HONDA ENTRY
3. POSITIONING OF HONDA IN US MARKET
4. COMPETITIVE FORCES OF STRATEGY
5. STRENGTH OF HONDA MOTORS
6. STRATEGY OF HONDA IN US (AS PER BCG)
7. COUNTER A
Macroeconomics
Wealth of nations and long term growth
Vidya Mahambare, October 2015
Trend growth and business cycles
Question
Since 1950s Indias real GDP growth rate has
averaged
5%
6%
3%
Question
The Indian economy in 1980 was xx times the
size of the
Chapter 2 Conceptual Exercises
Exercise 1
Part (a): We expect that with a very large number of measurements n, a flexible learning method would
be able to learn the signal without as much fear of overfitting.
Part (b): If the number of predictors p is ver
Multiple Dichotomy Analysis
Multiple dichotomy analysis is very similar to the multiple response analysis.
Exercise:
Question: please tick the important reasons why you study at the Open University.
_ Job change
_ Professional development
_ Earning univer
Lesson 8: Multiple Responses and Multiple Dichotomy Analysis
Multiple responses and multiple dichotomy analysis are commonly used in the analysis of
questionnaire or survey data.
Open-ended questions
More than one choices
Multiple Responses
Open-ended que
Lesson 7: Non-Parametric Techniques
For the most non-parametric analyses, assumptions about the shape of the population are not
required. For that reason, they are often used when small sample sizes are involved.
Chi-square test for goodness of fit
(One-s
Lesson 6: Reliability Analysis
Reliability means consistency. It is the degree to which an instrument will give similar
results for the same individuals at different times.
Reliability can take on values of 0 to 1.0, inclusive.
Methods for checking Reliab
Statistical Tools for Research -SPSS (2)
Topic:
Quantitative Data Analysis (Intermediate)
Date:
April 14 & 15, 2003
Time:
6:00 - 8:30pm
Venue:
B0415 & B0416
Facilitators:
Dr. Zhang Wei-yuan (CRIDAL, OUHK)
Ms. Elaine Kwok (CRIDAL, OUHK)
This is the second
Problem Set 1
Due in class on paper, Wednesday October 8, 2014
This problem set includes a gentle introduction to R. If you are new to
R, I recommend spending a few hours at rst going over the early parts of
Dalgaards book. R is easy to nd and download. T
STATS 305 Notes1
Art Owen2
Autumn 2013
1
The class notes were beautifully scribed by Eric Min. He has kindly allowed his notes to be placed online
for stat 305 students. Reading these at leasure, you will spot a few errors and omissions due to the hurried
36
APPENDIX
B
Probability review
c A. B. Owen 2006, 2008
We review some results from probability theory. The presentation avoids
measure theoretic complications, using terms like sets and functions where
measurable sets and measurable functions respective
2
Linear Least Squares
The linear model is the main technique in regression problems and the primary
tool for it is least squares tting. We minimize a sum of squared errors, or
equivalently the sample average of squared errors. That is a natural choice
wh
2
Two or more categorical predictors
Here we extend the ANOVA methods to handle multiple categorical predictors. The statistician has to watch carefully to see whether the effects being considered are properly treated as fixed or random. Just turning the
CHAPTER
1
Introduction
Stat 305 is the rst course in our applied statistics sequence. It focusses on
regression problems, especially the linear model. We will get a deep understanding of linear regression and, in learning the limits of linear regression,
1
One categorical predictor at k levels
Here we look at the regression model in the case where there are k 2 groups. We have a single predictor X cfw_1, 2, . . . , k. For observation we get X which tells us which group and response Y R. Instead of working
Minimal Sufficient |sigma-Fields and Minimal Sufficient Statistics. Two Counterexamples
Author(s): Dieter Landers and Lothar Rogge
Source: The Annals of Mathematical Statistics, Vol. 43, No. 6 (Dec., 1972), pp. 2045-2049
Published by: Institute of Mathema
COMPUTATIONAL IMPLICATIONS OF
REDUCING DATA TO SUFFICIENT STATISTICS
By
Andrea Montanari
Technical Report No. 2014-12
September 2014
Department of Statistics
STANFORD UNIVERSITY
Stanford, California 94305-4065
COMPUTATIONAL IMPLICATIONS OF
REDUCING DATA T
STATS 300A: Theory of Statistics
Fall 2014
Lecture 1 September 23
Lecturer: Lester Mackey
Scribe: Jessy Hwang, Yishun Dong, Sidd Jagadish
Warning: These notes may contain factual and/or typographic errors.
1.1
The Big Picture
Consider the following owchar
STATS 300A: Theory of Statistics
Fall 2013
Lecture 4 October 3
Lecturer: Lester Mackey
Scribe: Meng Wu; Yiming Sun
Warning: These notes may contain factual and/or typographic errors.
4.1
Completeness and Ancillarity
Last time we dened our ideal notion of
STATS 300A: Theory of Statistics
Fall 2013
Lecture 3 October 1
Lecturer: Lester Mackey
Scribe: Nick Doudchenko, Zhou Fan
Warning: These notes may contain factual and/or typographic errors.
3.1
Minimal Suciency
Last time we dened a notion of maximal lossle
STATS 300A: Theory of Statistics
Fall 2014
Lecture 2 September 25
Lecturer: Lester Mackey
2.1
Scribe: Yuanyuan Shen and Zi Yin
Recap
Last time, we set out on a quest to develop optimal inference procedures and, along the way,
encountered an important pair