| Course Status : | Ongoing |
| Course Type : | Core |
| Language for course content : | English |
| Duration : | 15 weeks |
| Category : |
|
| Credit Points : | 5 |
| Level : | Undergraduate |
| Start Date : | 08 Jul 2024 |
| End Date : | 31 Oct 2024 |
| Enrollment Ends : | 31 Aug 2024 |
| Exam Date : | 07 Dec 2024 IST |
| Exam Shift : | Shift-I |
Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.
Course Title: Statistical Methods for Economics for UG Economics
subject
(Level & Subject) Syllabus (Based on Choice Based Credit System)
|
Unit |
Week |
Video |
Title of Video and Reading
text/Lecture/ppt |
|
INTRODUCTION
TO STATISTICS, FREQUENCY DISTRIBUTION, GRAPHICAL PRESENTATION, MEASURES OF
CENTRAL TENDENCY |
WEEK-1 |
Day 1 |
Introduction to
Statistics and Economics. Ø
Introduction
– Definition, Characteristics and limitations Ø
Scope
of Statistics in Economics – Situations or examples Ø
Terminologies
of Statistics with examples Ø
Scales
of measurement – properties and classification Ø
Data
collection methods |
|
Day 2 |
Organization of the
data. Ø
Classification
of the data Ø
Tabulation
of the data Ø
Frequency
distribution – Terminologies Ø
Frequency
distribution – univariate Ø
Frequency
distribution – Bi-variate |
||
|
Day 3 |
Graphical
presentation of the data. Ø
Histogram
– equal and unequal width Ø
Frequency
curve – with and without histogram Ø
Frequency
polygon – with and without histogram Ø
Ogive
– less than and more than ogive Ø
Scatter
plot |
||
|
Day 4 |
Measures of Central
Tendency – Part-I. Ø
Introduction
– meaning, characteristics, limitations, various measures of central tendency Ø
Applications
of central tendency in field of economics-with examples Ø
Computation
of arithmetic mean-raw data Ø
Computation
of arithmetic mean-discrete data Ø
Computation
of arithmetic mean-continuous data |
||
|
MEASURES
OF CENTRAL TENDENCY AND MEASURES OF DISPERSION |
WEEK-2 |
Day 1 |
Measures of
Central Tendency – Part-II. Ø
Partition
values – Meaning, properties, various partition values Ø
Median
– Raw data (odd and even number of observations), discrete and continuous
frequency distribution. Graphical location. Ø
Quartiles
– Raw data, discrete and continuous frequency distribution Ø
Deciles
- Raw data, discrete and continuous frequency distribution Ø
Percentiles
- Raw data, discrete and continuous frequency distribution |
|
Day 2 |
Measures of Central
Tendency – Part-III. Ø
Mode
– Meaning, properties. Ø
Computation
of mode – raw data, discrete frequency distribution. Ø
Computation
of mode-continuous frequency distribution. Ø
Empirical
relationship of mode. Ø
Graphical
location of mode. |
||
|
Day 3 |
Measures of
Dispersion – Part-I. Ø
Introduction
– meaning, characteristics, limitations, absolute and relative measures,
various measures of dispersion, importance of dispersion in the field of
economics. Ø
Range
– meaning, properties, application, computation for raw data (absolute and
relative measure) Ø
Computation
of range for discrete and continuous frequency distribution. Ø
Lorenz
Curve and its interpretation Ø
Gini’s
co-efficient and its interpretation |
||
|
Day 4 |
Measures of
Dispersion – Part – II. Ø
Quartile
deviation - meaning, properties,
computation in case of raw data, discrete and continuous frequency
distribution Ø
Average
deviation (mean) – meaning, properties, computation in case of raw data. Ø
Average
deviation (mean) – computation for discrete and continuous frequency
distribution. Ø
Average
deviation (median) – computation in case of raw data. Ø
Average
deviation (median) – computation for discrete and continuous frequency
distribution. |
||
|
MEASURES
OF DISPERSION AND CONCEPT OF PROBABILITY |
WEEK-3 |
Day 1 |
Measures of
Dispersion – Part – III. Ø
Standard
deviation – meaning, properties, computation in case of raw data. Ø
Standard
deviation – computation for discrete frequency distribution Ø
Standard
deviation – computation for continuous frequency distribution Ø
Variance
and co-co-efficient of variation Ø
Application
of standard deviation in economics |
|
Day 2 |
Skewness, Kurtosis and Moments. Ø
Concept
of moments, moments about mean, arbitrary point, origin, Ø
Skewness
– Concept, types, methods of skewness. Ø
Computation
of skewness-Karl Pearson’s and Bowley’s method. Ø
Kurtosis
– concept, type. Ø
Skewness
and Kurtosis based on moments |
||
|
Day 3 |
Concept of Probability. Ø
Introduction
to concept of probability Ø
Terminologies
and notations associated with probability. Ø
Construction
of sample space and events Ø
Classical
probability – concept + Activity Ø
Empirical
probability |
||
|
Day 4 |
Probability Axioms – I. Ø
Addition
theorem of probability of dependent events Ø
Addition
theorem of probability of independent events Ø
Addition
theorem of probability of mutually exclusive events Ø
Concept
of conditional probability Ø
Activity
– 1 |
||
|
PROBABILITY
THEORY, RANDOM VARAIBLE AND MATHEMATICAL EXPECTATION |
WEEK-4 |
Day 1 |
Probability Axioms – II. Ø
Multiplication
theorem of probability of dependent events Ø
Multiplication
theorem of probability of independent events Ø
Important
results on probability of events (Conditional events) Ø
Activity
– 1 Ø
Activity
- 2 |
|
Day 2 |
Inverse Probability (Baye’s Theorem). Ø
Rule
of Inverse probability Ø
Activity
- 1 Ø
Tree-Diagram
method of solution Ø
Activity-2 Ø
Activity-3 |
||
|
Day 3 |
Random Variable and Probability Distribution. Ø
Concept
of a random variable and random experiment. Ø
Probability
distribution of a discrete random variable. Ø
Probability
distribution of a continuous random variable. Ø
Activity
-1 (discrete random variable). Ø
Activity-2
(continuous random variable). |
||
|
Day 4 |
Mathematical Expectation of a
random variable. Ø
Definition
of mathematical expectation. Ø
Theorems
/ Properties of mathematical expectation. Ø
Mathematical
expectation of a discrete and continuous random variable. Ø
Mathematical
expectation of a function of the random variable. Ø
Activity-1. |
||
|
DISCRETE
THEORETICAL DISTRIBUTIONS |
WEEK-5 |
Day 1 |
Introduction to theoretical distributions. Ø
Introduction
to theoretical distributions. Ø
Concept
of discrete theoretical distributions. Ø
Concept
of continuous theoretical distributions. Ø
Importance
of theoretical distributions in economics. Ø
Activity
- 1 |
|
Day 2 |
Discrete Uniform distribution. Ø
Concept
and definition of discrete uniform distribution. Ø
Properties
/ features of discrete uniform distribution. Ø
Computation
of discrete uniform probabilities-1 (using density function) Ø
Computation
of discrete uniform probabilities -2 (using distribution function) Ø
Application
of discrete uniform distribution in economics. |
||
|
Day 3 |
Binomial
Distribution. Ø
Concept
and definition of binomial distribution. Ø
Properties
/ features of binomial distribution. Ø
Computation
of binomial probabilities (using density and distribution function), Ø
Fitting
of a binomial distribution. Ø
Application
of binomial distribution in economics. |
||
|
Day 4 |
Poisson Distribution. Ø
Concept
and definition of Poisson distribution. Ø
Properties
/ features of Poisson distribution. Ø
Computation
of Poisson probabilities (using density and distribution function). Ø
Fitting
of Poisson distribution. Ø
Application
of Poisson distribution in Economics. |
||
|
CONTINUOUS
THEORETICAL DISTRIBUTION |
WEEK-6 |
Day 1 |
Continuous Uniform Distribution. Ø
Concept
and definition of continuous uniform distribution. Ø
Properties
/ features of continuous uniform distribution. Ø
Computation
of continuous uniform probabilities-1 (using density function) Ø
Computation
of continuous uniform probabilities -2 (using distribution function) Ø
Application
of continuous uniform distribution in economics. |
|
Day 2 |
Exponential Distribution. Ø
Concept
and definition of exponential distribution. Ø
Properties
/ features of exponential distribution. Ø
Computation
of exponential probabilities-1 (using density function). Ø
Computation
of exponential probabilities-2 (using distribution function). Ø
Application
of exponential distribution in economics. |
||
|
Day 3 |
Normal Distribution-1. Ø
Concept
and definition of normal distribution. Ø
Properties
/ features of normal distribution. Ø
Reading
of normal table. Ø
Application
of normal distribution in economics. |
||
|
Day 4 |
Normal Distribution-II. Ø
Computation
of normal probabilities-1 (using density function) Ø
Computation
of normal probabilities-2 (using distribution function) Ø
Fitting
of normal distribution Ø
Generating
random samples from normal distribution |
||
|
BIVARIATE
RANDOM VARIABLE AND ITS MATHEMATICAL EXPECTATION |
WEEK-7 |
Day 1 |
Bi-variate random variable.-Discrete Ø
Concept
of a discrete bi-variate random variable. Ø
Bi-variate
density functions of a discrete random variable. Ø
Bi-variate
distribution function of a discrete random variable Ø
Problems
on bi-variate discrete random variable Ø
Uses
of bi-variate discrete random variable in economics |
|
Day 2 |
Bi-variate random variable.-Continuous Ø
Concept
of a continuous bi-variate random variable. Ø
Bi-variate
density functions of a continuous random variable. Ø
Bi-variate
distribution functions of a continuous random variable. Ø
Problems
on bi-variate continuous random variable. Ø
Uses
of bi-variate continuous random variable in economics. |
||
|
Day 3 |
Marginal and Conditional distribution. Ø
Marginal
distribution - discrete random
variable Ø
Marginal
distribution – continuous random variable Ø
Conditional
distribution- discrete random variable Ø
Conditional
distribution-continuous random variable Ø
Properties
of marginal and conditional distributions |
||
|
Day 4 |
Mathematical Expectation of a Bi-variate random
variable Ø
Concept
of mathematical expectation of a bi-variate random variable. Ø
Addition
theorem on mathematical expectation. (Discrete and Continuous) Ø
Multiplication
theorem on mathematical expectation. (Discrete and Continuous) Ø
Properties
of mathematical expectation of a bi-variate random variable. Ø
Application
of mathematical expectation in economics. |
||
|
MARGINAL
AND CONDITIONAL EXPECTATION AND SAMPLING |
WEEK-8 |
Day 1 |
Marginal and Conditional Expectation. Ø
Marginal
expectation - discrete random variable Ø
Marginal
expectation – continuous random variable Ø
Conditional
expectation- discrete random variable Ø
Conditional
expectation-continuous random variable Ø
Properties
of marginal and conditional expectations |
|
Day 2 |
Mathematical Expectation – Covariance and
correlation Ø
Covariance
using mathematical expectation. Ø
Correlation
using mathematical expectation. Ø
Interpretation
of the correlation value. Ø
Activity
on correlation analysis. Ø
Application
of correlation analysis in economics. |
||
|
Day 3 |
Sampling Ø
Concept
and definition of sampling. Ø
Terminologies
in sampling. Ø
Principle
steps in sample survey. Ø
Methods
of sampling – advantages and disadvantages. Ø
Sampling
Errors. |
||
|
Day 4 |
Non Probability Sampling Techniques Ø
Introduction
to non-probability sampling techniques, merits and demerits. Ø
Convenience
sampling - introduction, merits and demerits. Ø
Judgement
sampling – introduction, merits and demerits. Ø
Quota
sampling – introduction, merits and demerits. Ø
Snowball
or network sampling – introduction, merits and demerits. |
||
|
SAMPLING
TECHNIQUES AND THEORY OF ESTIMATION |
WEEK-9 |
Day 1 |
Simple Random Sampling Ø
Introduction
to simple random sampling Ø
Methods
of simple random sampling Ø
Results
/ characteristics of simple random sampling Ø
Advantages
and disadvantages of simple random sampling Ø
Situations
where simple random sampling is applicable |
|
Day 2 |
Stratified Random Sampling Ø
Introduction
to stratified random sampling Ø
Types
of stratified random sampling Ø
Results
/ characteristics of stratified random sampling Ø
Advantages
and disadvantages of simple random sampling Ø
Situations
where stratified random sampling is applicable |
||
|
Day 3 |
Other random sampling techniques. Ø
Systematic
random sampling Ø
Cluster
random sampling Ø
Multistage
sampling Ø
Multiphase
sampling Ø
Situation
where systematic, cluster, multistage, multiphase sampling techniques are
applicable. |
||
|
Day 4 |
Theory of Estimation Ø
Introduction
of theory of estimation Ø
Terminologies
used in estimation Ø
Unbiasedness
+Activity Ø
Consistency + Activity Ø
Efficiency
+ Activity |
||
|
POINT
ESTIMATION |
WEEK-10 |
Day 1 |
Point estimation – methods of moments (Discrete) Ø
Introduction
– Steps involved in estimation for discrete random variable Ø
Binomial
distribution-1 Ø
Binomial
distribution-2 Ø
Poisson
distribution-1 Ø
Poisson
distribution-2 |
|
Day 2 |
Point estimation – methods of moments
(Continuous) Ø
Introduction
– Steps involved in estimation for continuous random variable. Ø
Continuous
Uniform distribution Ø
Exponential
distribution Ø
Normal
distribution-1 Ø
Normal
distribution-2 |
||
|
Day 3 |
Point estimation – maximum likelihood procedure
(Discrete) Ø
Introduction
– Steps involved in estimation for discrete random variable Ø
Binomial
distribution-1 Ø
Binomial
distribution-2 Ø
Poisson
distribution-1 Ø
Poisson
distribution-2 |
||
|
Day 4 |
Point estimation – maximum likelihood procedure
(Continuous) Ø
Introduction
– Steps involved in estimation for continuous random variable. Ø
Continuous
Uniform distribution Ø
Exponential
distribution Ø
Normal
distribution-1 Ø
Normal
distribution-2 |
||
|
INTERVAL
ESTIMATION |
WEEK-11 |
Day 1 |
Interval estimation – I Ø
Concept
and terminologies associated with interval estimation Ø
Construction
of confidence interval for mean – variance known Ø
Activity
on construction of confidence interval for mean – variance known Ø
Construction
of confidence interval for mean – variance unknown Ø
Activity
on construction of confidence interval for mean – variance unknown |
|
Day 2 |
Interval estimation – II Ø
Construction
of confidence interval for difference of mean – variance known Ø
Activity
on construction of confidence interval for difference of mean – variance
known Ø
Construction
of confidence interval for difference of
mean – variance unknown Ø
Activity
on construction of confidence interval for difference of – variance unknown |
||
|
Day 3 |
Interval estimation - III Ø
Construction
of confidence interval for variance – mean known Ø
Construction
of confidence interval for variance – mean unknown Ø
Construction
of confidence interval for ratio of variance – mean known Ø
Construction
of confidence interval for ratio of variance – mean unknown Ø
Activity
on confidence interval for variance and ratio of variance. |
||
|
Day 4 |
Interval estimation - IV Ø
Construction
of confidence interval for single proportion. Ø
Construction
of confidence interval for difference of proportion. Ø
Activity
on confidence interval for proportion. Ø
Construction
of confidence interval for correlation coefficient. Ø
Activity
on confidence interval for correlation coefficient. |
||
|
APPLICATION
TOOLS OF STATISTICS |
WEEK-12 |
Day 1 |
Sample size determination Ø
Sample
size using mean and standard deviation from pilot study Ø
Sample
size using prevalence from pilot study. Ø
Sample
size for finite and infinite population Ø
Case
studies |
|
Day 2 |
Questionnaire Preparation. Ø
Case
study: Data on reality shows Ø
Construction
of Questionnaire Ø
Excel
entries with coding for the data |
||
|
Day 3 |
Validity and Reliability Ø
Validity
– Definition, purpose Ø
Different
types of validity Ø
Reliability-Definition,
purpose Ø
Different
types of reliability Ø
Comparison
between validity and reliability |
||
|
DESCRIPTIVE STATISTICS AND
PROBABILITY COMPUTATION FROM STANDARD DISTRIUTIONS USING MS EXCEL AND
CONCLUSION. |
|
Day 1 |
Descriptive Analysis – Using MS Excel Ø
Frequency
tables for raw data Ø
.Graphs Ø
Measures
of central tendency Ø
Measures
of dispersion Ø
Correlation
Analysis |
|
WEEK-13 |
Day 2 |
Probability Distributions –Using MS Excel Ø
Binomial
distribution Ø
Poisson
distribution Ø
Continuous
uniform Ø
Exponential
Ø
Normal |
|
|
Day 3 |
Summarization of the
whole concept with an example. |
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