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# pue.kar.nic.in Statistics Syllabus for II Year : Pre University Education

Organisation : Department of Pre University Education
Subject : Statistics
Applicable For : II Year
Announcement : Syllabus

Website : https://pue.karnataka.gov.in/

## Statistics Syllabus :

Unit I :
Vital Statistics : Meaning of demography and Vital statistics. Methods of collection of Vital Statistics and uses. Fertility, growth and mortality rates. Life table. Definition of fertiliy and fecundity.

Related : II Year Political Science Syllabus Pre University Education : www.syllabus.gen.in/1189.html

Fertility rates- CBR, ASFR, GFR and TFR – definition, merits and demerits, computation. Growth rate- Gross reproduction rate and Net reproduction rate – definition, merits and demerits, computation.

Mortality rates- CDR, ASDR, S.T.D.R., IMR, NMR and MMR – definition, merits and demerits, computation.

Life table- Meaning and uses. Components of a life table- Explanation of the columns of a life table and simple problems on survival ratio, mortality ratio, average number of survivals and life expectancy.

Unit II :
Index Numbers : Meaning of an index number, characteristics, uses and limitations, brief description of the steps in the construction of a price index number, classification of index numbers as simple and weighted (AM, GM and Aggrigative) index numbers. Price, Quantity and Value index numbers.

Construction of unweighted and weighted price index numbers. Construction of Laspeyre’s, Paasche’s, MarshallEdgeworth’s, Dorbish-Bowley’s and Fisher’s Price and quantity index numbers. Construction of Kelly’s price index number. Bias in an index number.

Testing the appropriateness of an index number – Time reversal and Factor reversal tests- description. Verification of index numbers satisfying the reversibility tests. Unit and Circular Tests (statements and explanation only). Fisher’s index number is ideal (reasons).

Consumer price index numbers: Meaning, uses and brief description of the steps in the construction of a consumer price index number. Construction of consumer price index numbers – Aggregative expenditure method and family budget method.

Unit III :
Time Series Analysis : Explanation of a time series with example, uses of time series analysis. Brief description of the components of a time series with examples.

Measurement of trend by Graphical, Semi average, moving averages method (Period of moving average being 3, 4 or 5) and method of least squares applications. Drawing Historigram and plotting trend values.

Fitting a linear trend- normal equations, obtaining trend values, estimating future trend, drawing the historigram and the trend line. Fitting a second degree (Quadratic) and exponential trends- Normal equations and obtaining the trend equation, making future estimates.

Unit IV :
Interpolation And Extrapolation : Binomial expansion method of interpolation and extrapolation- conditions, formula and problems (with two missing values – one within and one outside the range).

Newton’s method of interpolation and extrapolation, conditions, formula and problems (one missing value-within or outside the range).

Unit V :
Theoretical Distributions : Bernoulli distribution – definition through p.m.f., examples of occurrence of Bernoulli distribution, expressions for mean and variance, features, applications. Bernoulli trials.

Binomial Distribution- Definition through p.m.f., examples of occurrence of Binomial distribution, expression for mean and variance, features. Given mean and variance, finding the parameters. Computing probabilities. Recurrence relation between successive probabilities and frequencies.

Fitting a Binomial distribution (The case of given p and estimated p), obtaining expected frequencies. Poisson distribution – definition through p.m.f., examples of occurrence of Poisson distribution, Expressions for mean and variance, features.

Computing probabilities for large n and small p, for the given . Recurrence relation between successive probabilities and frequencies. Finding for given two successive probabilities or frequencies.

Fitting a Poisson distribution to the given frequency distribution and finding expected frequencies.