Introduction to Econometrics
Week 12 Lecture 1
(Ch. 4)
The Classical Assumptions
The Sampling Distribution of (Properties of
the mean, the variance and the standard
error)
The Gauss-Markov Theorem and the
Properties of OLS Estimators
The Classical Assump
Introduc)on to Econometrics
Studenmund Ch14
Week 20 Lecture 2
1
Reminder: Assump6ons underlying the
mul6ple regression model
1. The regression model is linear in the coecients and the
error term.
2. The error term h
Marketing
Domains
By the end of the lecture you should be able to:
Understand the characteristics of different
markets
Explain the differences between consumer
and industrial marketing channels
Understand the differences between product
and services
MKTG2
Welcome to
Marketing 222:
Marketing
Fundamentals
MKTG 222 Marketing
Fundamentals
Zom Musiyiwa
Teaching Fellow
What is Marketing?
MKTG222
2
Marketing 222
Some Ground Rules
Please ensure all mobile phones are OFF!
I cant stand lateness.and I cant stand sm
Customer retention
and management
Explain the relationship between customer
satisfaction and retention
Discuss the impact of profit on customer
retention
Discuss customer portfolio management
MKTG 222
CRM and Loyalty
2
Retention Rate
100
80
60
40
20
0
1
V
The Market
Offering
Learning Outcome
Describe what is meant by the Total Product
concept and the customer value proposition
The Total Product
MKTG222
2
Recommended Reading
Marketing 3rd Ed by Rosalind Masterson &
David Pickton
Principles of Marketing b
Service Channels
The Price
The Symbol
The Service
The
Substance
MKTG 222
Service - Channels
2
Sets of interdependent organizations
involved in the process of making a
product/service available for use
or consumption.
MKTG 222
Service - Channels
3
Channel
Buckets of
Expectations
Explain the experience pipe model
Discuss the expectation and perceived value
gap and its usefulness for marketers
MKTG222
Buckets of Expectation
2
Marketing Fundamentals Chapter 3
MKTG222
Buckets of Expectation
3
Lancaster
Paradi
A little light
revision
Substance
+ Service + Symbol
= Total Product
Substance + Service = Symbol ?
Does the Total Product live up to
the Symbol
A Brand is a PROMISE !
The Symbol symbolises & sets
the expectations how do we
keep customers happy?
MKTG
Describe the categories of services
Discuss the differences between service and
substance
Discuss the marketing implications of the
service concept
Explain service dimensions
The Total Product
The Price
The Symbol
The Service
The
Substance
Channelling th
BRANDS AND BRANDING
Brands and
Branding
A Brand
A name, term, sign, symbol
or design, or a combination of them,
intended to identify the goods
& services of one seller or group
of sellers and to differentiate
them from those of competitors.
A Brand is a P
In relation to the total product
Understanding
Goods differentiation and classification in
consumer and industrial markets
Identify
the key dimensions of substance
Determine
key issues in Substance such as line filling, line
stretching
Focus
on packaging
Segmentation, Targeting, And
Positioning
MKTG 222
Explain how companies identify
attractive market segments and choose a
market-targeting strategy.
Discuss how companies differentiate and
position their products for maximum
competitive advantage
Henkel
Organizations as
Customers
Chapters 8-9
IM
B2B
B2R[2C]
O2O
B2G
B2B Marketing is defined as the
management process responsible for the
facilitation of exchange between producers of
goods and services and their organisational
customers
MKTG 222
Organisation
MKTG222 WEEK 7 COURSEWORK TEST 19/11/2013
WORK FAST -YOU ONLY HAVE 50 MINUTES 12 MINUTES PER QUESTION
UTILISE DIAGRAMS, TABLES, ILLUSTRATIONS, BULLET POINTS,
ANSWERS SHOULD NOT BE SHORT ESSAYS!
Make Sure You Put Your Name and Library Card Number
on the An
Marketing and
Pricing
(The final component
of the Total Product)
The Symbol
The Service
The
Substance
MKTG 222
Marketing and Pricing
2
MAKING MARKETS AND MONEY
MKTG 222
Marketing and Pricing
3
The Price
The Symbol
The Service
The
Substance
MKTG 222
Market
What use is Marketing in
Strategic Consulting work?
MARKETING 222 WEEK 9
Zom Musiyiwa
Department of Marketing
Some Loose Objectives for Today
A
brief background of what Marketing is
The role of the Consultant as analyst of
organizational capability.how
Introduc)on to Econometrics
Studenmund Ch 14
Week 20 Lecture 1
1
Reminder: Assump6ons underlying the
mul6ple regression model
1. The regression model is linear in the coecients and the
error term.
2. The error term
Introduc)on to Econometrics
Studenmund Ch10
Week 19 Lecture 2
1
Reminder: Assump6ons underlying the
mul6ple regression model
1. The regression model is linear in the coecients and the
error term.
2. The error term h
Week 9 Tutorial Worksheet: Cost Minimization
1. A rm with production function f (x1 , x2 ) has a long-run cost function given by:
C(w1 , w2 , y) = y 2 G(w1 , w2 )
Where G is just some arbitrary function of the input prices. What are the returns to scale o
Introduction to Econometrics
Week 13 Lecture 1
Hypothesis Testing (II)
Three Typical Approaches to
Hypothesis Testing : Review
The Interval Approach
The Significance Test Approach
The p-Value
Three Typical Approaches to
Hypothesis Testing: Review
1. The
Introduction to Econometrics
Week 12 Lecture 2
Hypothesis Testing (I) (Ch.5)
Statistical Inference: Review
Null and Alternative Hypotheses
Null hypothesis H0: 0 (the values you do not expect)
Alternative hypothesis HA: > 0 (the values you do expect)
Type
Introduction to Econometrics
Week 13 Lecture 2
Hypothesis Testing (II)
The t-Test
p-Values
Confidence intervals
The t-Test
The t-test is the test that econometricians usually use
to test hypotheses about individual regression slope
coefficients
Tests of
Introduction to Econometrics
Week 11 Lecture 2
Estimating Single Independent Variable Models
with OLS
Recall that the objective of regression analysis is to start from:
(2.1)
And, through the use of data, to get to:
(2.2)
Recall that equation 2.1 is p
Introduction to Econometrics
Week 15 Lecture 1
Introduction to Time-Series Models Ch. 12
The Distributed Lag Model
Example
Selection of Lag order
Univariate Time Series Analysis
The Distributed Lag Model
Time series data: Yt for t=1,.,T
End goal: Regressi
Introduction to Econometrics
Week 14 Lecture 1
Hypothesis Testing (III) (Ch 5)
Test overall significance of a regression model
ESS
2
df ESS (n - k 1 ) R
F=
=
2
RSS
k(1 - R )
df RSS
Test on a sub-set of coefficients
(RSSM RSS )
F=
RSS
M
n k 1
Testing for O
Introduction to Econometrics
Week 15 Lecture 2
Introduction to Time-Series Models
The Autorregresive Model
Nonstationary vs Stationary Time Series
Testing for a Unit Root (Dickey-Fuller Test)
Granger Causality
Cointegration
The Autocorrelation Function
Co
Introduction to Econometrics
Week 14 Lecture 2
Multicollinearity (DEF) Ch.8
Consequences of Multicollinearity
i.e. Variances and standard errors of estimates increase
How to detect Multicollinearity
High simple correlation coefficients (0.8)
High variance