1. INSTRUCTOR INTRODUCTIOON
DR. MUHAMMAD IFTIKHAR UL HUSNAIN
ASSISTANT PROFESOR ,DEPARTMENT OF MANAGEMENT SCIENCES COMSATS, ISLAMABAD SINCE 2012
LECTURER IN ECONOMICS PUNJAB HIGHER EDUCATION DEPARTMENT
AREAS OF INTERST
MICRO ECONOMICS
MACRO ECONOMICS
MATHEMATICAL ECONOMICS
STATISTICS
PUBLIC FINANCE

2. Course: MGT 605 Quantitative Techniques
Instructor: Dr. Muhammad Iftikhar ul Husnain
Course overview
Definition of Quantitative Techniques
Nature and Scope of Quantitative Techniques
Data
Sampling
Simple regression
Multiple regression
Multicollinearity
Auto correlation
Hetrosekasdicity
Dummy variables.
Logit model
Probit model
Panel data

3. Quantitative Methods for Business by Anderson Sweeny and Williams
Course Overview ….
Books
Quantitative Methods for Business by Anderson Sweeny and Williams
Quantitative analysis for management by Render, Stair, Hanna
Johnston, J. and J. DiNardo (n.d). Econometric Methods (4th Eds.) The McGraw Hill Companies.
Maddala, G. S. (1992). Introduction to Econometrics, (2nd Edition), Macmillan Publishing Company, New York.
J. Wooldridge, Introductory Econometrics, 4th Edition, South Western College Press

4. Introduction Back Ground Definition
Quantitative tools have been used for thousands of years (Taylor and Frederick)
Quantitative analysis can be applied to a wide variety of problems acrossdescipline
It’s not enough to just know the mathematics of a technique
One must understand the specific applicability of the technique, its limitations, and its assumptions
Definition
Quantitative Technique: Any procedure, formulae, instrument, model that helps in analyzing any hypothesis, theory, statement is called quantitative techniques.
In Quantitative analysis, we may be interested in predicting Y with X, or with the casual effect of X on Y.
Quantitative analysis is a scientific approach to decision making whereby raw data are processed and manipulated resulting in meaningful information.
Raw data--application of QT----Meaningful information

5. Qualitative versus quantitative analysis
It gives a general and unspecified view
Focus on words
Subjective
Case studies
fewer respondents
No standardized data analysis
Explores impact (why)
It gives clear, specific and numerical prediction of the problem
Focus on number
Objective
Statistical Analysis
higher number of respondents
Standardized data analysis
Suggests/quantify the impact

6. Quantitative vs. Qualitative factors
Quantitative factors
Might be different investment levels, interest rates, inventory levels, demand, or labor cost
Qualitative factors
such as the weather, state and federal legislation, and technology breakthroughs should also be considered. Political instability
Information may be difficult to quantify but can affect the decision-making process
Why Study Quantitative Techniques
In your everyday life, it will help you make sense of what to heed and what to ignore in statistical information provided in news reports, surveys, political campaigns, advertisements
From house to stock market

7. Advantages of Quantitative Analysis
1- It can accurately represent reality
2- It can help a decision maker formulate problems
3- It can give us insight and information
4- It can save time and money in decision making and problem solving
5- It may be the only way to solve large or complex problems in a timely fashion
6- It can be used to communicate problems and solutions to others
7- it can help in forecasting the future on the basis of available information

8. How the Quantitative Approach Works
It is a systematic process
1- Define the problem
It is a starting point
Develop a clear and concise statement.
Most important and difficult step
selecting the right problems is very important
Specific and measurable objectives may have to be developed
In the real world, quantitative analysis models can be complex, expensive and time consuming.

9. 2- Developing a Mathematical Model
What a Model is?
A model is simply a set of mathematical equations.
A model is a mathematical representation of a theory/reality.
Single equation model
If a model has only one equation it is called a single-equation model.
Multiple-equation model
If it has more than one equation
Models generally contain variables, and parameters
Parameters are unknown quantities but there value is fixed.

10. How to Develop a Quantitative Model….
An important part of the quantitative analysis approach
Mathematical Models have two major types.
Mathematical models that do not involve risk are called deterministic models.
We know all the values used in the model with complete certainty.
Mathematical models that involve risk, chance, or uncertainty are called probabilistic models
In this course our concern is with the probabilistic model.
Example: Demand function
Profit function

11. 3- Specification of the probabilistic/econometric Model
Mathematical model assumes that there is an exact relationship between the variables.
However it is not of much interest in social sciences
But relationships between variables are generally inexact in social sciences.
The introduction of error term
It captures the effect of unquantifiable forces.

12. 4- Significance of the stochastic term Ui
Error term is proxy for all the omitted variables but collectively affect Y.
- Why not introduce all the variable explicitly?
Unavailability of data
Core versus peripheral variables- joint effect of many variable is so small that for practical consideration and cost effectiveness it does not pay to introduce them explicitly in the model.
Randomness in Human Behavior
Poor Proxy Variable
Principle of parsimony
Wrong functional form

13. 4- Acquiring data
To estimate the Statistical model given/ obtain the numerical values of β1 and β2 , we need data.
Data may come from a variety of sources such as company reports, company documents, interviews.
The quality data is extremely important.
The reliability of results is directly proportional to quality of data.
Data collecting is an art.
Data is collected only on the required variables.

14. 5. Estimation of the Statistical Model
To estimate the values of the parameters we need some techniques.
Common techniques are
Solving equations
Trial and error – trying various approaches and picking the best result

15. 6- Hypothesis Testing/testing the solution
Model should be tested for accuracy before analysis and implementation.
Whether the results are according to the theory .
Results should be logical, consistent, and represent the real situation.
We use statistical inference (hypothesis testing) to know the significance of the parameter.

16. 7- Forecasting or Prediction
If the chosen model does not refute the hypothesis or theory under consideration,
we may use it to predict the future value(s) of the dependent variable Y on the basis of known variables.
8- implementing the results/ policy
Implementation can be very difficult
People can resist changes
Many quantitative analysis efforts have failed because a good, workable solution was not properly implemented

17. Possible Problems in the Quantitative Analysis Approach
Problems are not easily identified
Conflicting viewpoints-linear or non linear relationship
Beginning assumptions
Fitting the textbook models
Understanding the model
Validity of data
Hard-to-understand mathematics and statistics

18. Variables and their Types
Ratio scale
Two values of a variable say X1 and X2,
(i)X1/X2 (ii)(X1-X2) (iii) X1≤ X2 and vice versa are meaningful quantities. Most economic variable are ratio scale.
Interval Scale
Satisfies last two properties. Distance b/w two time periods ( ) is meaningful. But 1990/2012 is senseless.
Ordinal Scale
Only it satisfies the third property of ratio scale. Grades, A,B,C,D. Upper, Middle, Lower. Example: Indifference curve in Economics
Nominal Scale:
None of the feature of ratio scale. Gender, male, female, marital status, single, married, divorced, unmarried simply denote categories

19. Probability
A probability is a numerical statement about the chance that an event will occur.
Two basic rules regarding the probability
1- The probability, p, of any event is greater than or equal to 0 and less than or equal to ≤p≤1
A- 0 means that an event is never expected to occur
b- 1 means that an event is always to occur.
2- The sum of the simple probabilities for all possible outcomes of an activity must be equal to 1.
Examples:
Quantity Demanded Number of days
p=40/200 (.20)
p=80/200 (.40)
p=(50/200) (.25)

20. Types of probability Two different ways to determine the probability
Objective p(event)= number of occurrence of the event /total number of events
Examples: tossing of a fair coin- it is based on the previous logic.
Subjective: logic and history are not appropriate. So subjectivity arises.
Examples
what is the probability that floods will come?
What is the probability that depression will come in an economy?
For this opinion polls are conducted and then probabilities are found.

21. Mutually exclusive and collectively exhaustive events
Mutually exclusive events : If only one of the event can occur on any one trial.
Collectively exhaustive events: They are said to be mutually exhaustive if the list include all the possible outcomes i.e. A U B= S.
Not mutually exclusive
The occurrence of one event does not restrict the occurrence of the other event.
Examples: Drawing a 5 and drawing a diamond from a deck of cards- it can be both 5 and diamond

22. Adding mutually exclusive events
We are interested in whether one event or second event will occur.
When events are mutually exclusive the law of addition is simply as follows.
P(event A or event B)= p(event A)+ p(event B)
Drawing spade or drawing a club out of a deck card are mutually exclusive. 13/52+13/52=1/2
Venn diagram
Addition of not mutually exclusive events.
P(A or B)= P(A)+P(B)-P(A and B)
Examples:
In a math class of 30 students, 17 are boys and 13 are girls. On a unit test, 4 boys and 5 girls made an A grade. If a student is chosen at random from the class, what is the probability of choosing a girl or an A student?
P(girl or A)= P(girl)+ P(A)- P(girl and A)= 13/30+9/30-5/30=17/30

23. Some Basic Concepts in Mathematics
Derivatives
Definition
Maxima
Minima
Rules of Derivatives
1- Constant function rule
2- Power function rule
3- Sum difference rule
4- Product rule
5- Quotient rule
6- Chain rule
7- Inverse function rule

24. Notation Dependent variable Independent variable
Explained variable Explanatory variable
Predictand Predictor
Regressand Regressor
Response Stimulus
Endogenous Exogenous
Outcome Covariate
Controlled variable Control variable
LHS RHS