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Tamil Nadu Public Service Commission Syllabus for Research Assistant Exam
(Evaluation and Applied Research Department)
Unit - I:
Indian Economy – Growth and development: Agriculture: Area, Yield, Production,
marketing and sales. Rural Development – Industry: Input and output – Service Sector :
Types of service sector, skilled labour, semi skilled labour and unskilled labour –
Changing Sectoral contributions – Industrial Policy Changes – FDI – Infrastructure:
Public Health – Education – drinking water – sanitation – Public transport –Banking and
financial services–housing facilities–Nutrition – Investment – Public Private Participation
– Inclusive growth. Human Development: Concepts, Trends and Issues – Employment,
unemployment, under employment and disguised unemployment – concepts,
measurement and Trends – Employment in Organized and unorganized sector –
Strategies for Employment Generation. Poverty: Concepts, measurement and Trends,
Fiscal Policy – FRBM Act – Goods and Service Tax (VAT)– Fiscal Federalism – Parallel
economy in India – Inflation: Definition, trends, estimates, consequences and remedies
– RBI – Monetary and Fiscal policy – Banking Sector Reforms – India’s foreign trade,
composition, direction and recent changes in trade policy – Balance of payments – India
and WTO requirements – Global Financial Crisis and its Impact on India. Index number:
Consumer price index – Whole Sale price Index. Time series analyses-Moving average
Method

Unit - II:
Primary data collection-Field Investigation- Census method-Sampling Method – Direct
personal observation-Interview method- Indirect oral interview-Information through
agencies-Mailed Questionnaires and scheduled sent through enumerator.Secondary
data collection- Books-Journals-Government (Ministry) Records and reports from
government agencies- Bibliography-Autography- Web site-News Paper- Periodicals, etc
Evaluation and preparation of the report and components of evaluation Report:
Executive summary-background and purpose-statement of the problem-objectives and
Hypotheses – evaluation methods (Statistical methods used in the study) –result and
discussion of the result –conclusion and recommendation- reference or bibliographyappendices.

Unit – III:
Econometrics - definition, methodology, Data – meaning and types of data such as
cross-section, time-series and panel data, primary & secondary data- advantages &
disadvantages, Variables – meaning and types such as quantitative and qualitative
variables, measurement scales of variables such as ratio, interval, ordinal and nominal-
simple examples. Correlation – simple and multiple correlation, properties of correlation
coefficients, Karl Pearson’s and Spearman’s rank correlation coefficients, Simple
numerical problems - Simple & multiple linear regression model – introduction,
assumptions, least squares principle, least squares estimators, properties of regression
coefficients(Gauss-Markov theorem), interpretation of regression output - R2 & adjusted
R2 , regression coefficients with t-values and level of significance, Simple numerical
problems - advantages of regression analysis over correlation analysis, Functional
forms of regression models – log-linear, log- lin and lin-log models and computation of
elasticity, computation of simple and compound growth rates - simple numerical
examples.

Unit - IV:
Econometric Problems: heteroscedasticity, multicollinearity and autocorrelation –
definition and consequences. Dummy variables: definition, generation of dummy
variables, problem of dummy variable trap, cautions in using dummy variables, inclusion
and interpretation of dummy variables in the regression. Qualitative response
regression models – linear probability, logit and probit models – an introduction. Basics
of time-series: additive and multiplicative models, determination of trend by free
drawing, moving average methods, and seasonal indices, definition of stationarity, tests
for stationarity and transformation of non-stationary series into stationary series.

Unit - V :
Compilation and Tabulation of data Collected - Classification – Types of Classification –
Formulation of discrete and continuous frequency distribution ( uni-variate and Bivariate
). Pictorial Representation of data: Bar diagrams – Pie diagrams – Histogram –
Ogives and Lawrence Curve Measures of Location – Mean , Median, Mode, Harmonic
Mean and Geometric Mean – Quartiles - Measures of variation : Range, Quartile
deviation , Mean deviation, Variance and Standard deviation. Coefficient of variation ,
skewness and Kurtosis.( All the calculation of measures are for both grouped and
ungrouped data ) Probability and Distribution: Introduction to probability: Random
Experiments, Sample Space and events, Definition of probability. Classical, Empirical
and Axiomatic approach to probability; Addition and Multiplication Theorem, Conditional
probability and Baye’s Theorem. Random Variables and Distribution function –
Mathematical Expectation and conditional Expectation. Convergence in Probability –
Weak Law of large Numbers and strong law of large Number – Central limit Theorem –
Discrete distributions: Binomial, Poisson. Continuous Distributions: Normal Distribution
Chi-Square, t and F distributions and their properties.

Unit - VI:
Application of Statistical Methods - Sampling Theory: Introduction to the theory of
Sampling: Sampling designs – Simple Random Sampling with and without replacement
- Systematic, Stratified, Ratio and Regression Sampling methods , Sampling and non –
sampling errors – Cluster sampling, Purposive Sampling Quota Sampling. Testing of
Hypothesis: Introduction to Testing of Hypothesis: Simple, Null and Alternative
hypotheses, composite hypothesis, two kinds of Errors – Critical Region – Power
function. Testing of significance using chi-square, t and F tests.(simple problems)
ANOVA- One way and Two way classifications. Application of Statistical Methods
through MS-Excel – Language : Over view of MS-Excel: Construction of charts and
diagrams – Sorting – Filtering – Removing duplicates – Calculation of Measures of
Central tendency, Measures of dispersion, correlation Regression and curve fitting
using – Excel.

Attached Files

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