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CEC

Data Analysis

CEC and Bathinda College of Law via Swayam

Overview

This course begins with some basic concepts and terminology that are fundamental to data analysis and inference. This course introduces the student to collection and presentation of data. It also discusses how data can be summarized and analysed for drawing statistical inferences. The students will be introduced to important data sources that are available and will also be trained in the use of statistical software to analyse data.The main objectives of this course are:To know about the process of data collection and presentation.To learn the way of obtaining secondary data from various sources.To discover the technique of processing and analysing the data for drawing statistical inferences.Learning OutcomesAfter completing this course you shall be able to:Understand the data, its types of measurement and collection.Know the procedure of determining the sample size and distribution.Outline the designing of questionnaire and procedure of pre – testing.Perform the coding, editing, classification, tabulation and graphical representation of data.Analyse the univariate and bivariate frequency distributions.Understand the theoretical foundations of different tests and their role in hypothesis testing.Design the composite index numbers.

Syllabus

Course Content

Module No.

Title of Lesson/Module

Introduction and overview

1

Data and Types of Measurement

2

Primary and Secondary Data: Merits and Demerits

3

Methods of Collecting Primary Data

4

Population and Sample: Merits and Demerits

5

Sampling Methods: Random and Non-Random

6

Sampling Size and Distribution

7

Sampling and Non - Sampling Errors

8

Designing a Questionnaire: Editing and Pretesting

9

Types of Interview Techniques

10

Methods of Collecting Secondary Data

11

Data Processing - Editing and Coding

12

Data Processing - Classification and Tabulation

13

Cross Tabulation and its Significance

14

Practical Problems

15

Graphical Representation of Data - Line Graph, Bar Diagram and Pie Chart

16

Graphical Representation of Data – Histograms and Ogives

17

Practical Problems of Graphical Representation

18

Univariate Frequency Distribution-Measures of Central Tendency

19

Univariate Frequency Distribution-Measures of Dispersion

20

Univariate Frequency Distribution- Skewness, Moments and Kurtosis

21

Numerical Problems: Univariate Frequency Distribution

22

Bivariate Frequency Distribution-Correlation, Various Methods

23

Bivariate Frequency Distribution-Regression Analysis

24

Numerical Problems: Bivariate Frequency Distribution

25

Estimation of Population Parameters

26

Methods of Estimation

27

Unbiased Estimator of Population Mean

28

Unbiased Estimator of Population Variance

29

Basic concepts of inference

30

Testing of hypothesis-types, uses

31

Testing of hypothesis: t test

32

Testing of hypothesis: F test

33

Testing of hypothesis: Z test

34

Testing of hypothesis: Chi Square test

35

ANOVA- one way and Interpretation

36

ANOVA- two way and Interpretation

37

Numerical Problems

38

Basics of Index Numbers

39

Price and Quantity Indices and their Properties

40

Numerical Problems of Index Numbers

Taught by

Dr Jaspreet Kaur, Assistant Professor of Economics |

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