Statics and computer application (subsidiary) paper -I
T.M.B.U and MUNGER UNIVERCITY Static and computer application subsidiary paper-I syllabus.
75 marks
candidate are required to answer 5 question at least two from each group. Question one is objective and compulsory"
GROUP-A
4 Questions
PROBABILITY THEORY :-
Important concepts in probability:-
definition of probability classical and relative frequency approach to probability.
Richard Von Mises.Cramer and kolmogorovs approaches to probability,merits and demerits of these approaches only general idea to given.
Random Experiment:- Trial, sample point and sample space, definition of an event, operation of human mutually exclusive and exhaustive events.
Discrete sample space, properties of probability based on axiomatic approach , conditional probability, independence of events Baye's theorem and its application. (ISL)
Random variables:- Definition of discrete random variables ,probability mass function, idea of continuous random variable , probability density function.
Illustration of random variable and its properties expectation of a random variable and its properties moments, measure of location dispersion ,skewness and kurtosis, probabilities, generating function (if it exist): their properties and uses (20L)
Standard univariate discrete distributions and their properties; discrete uniform. Binomial , Poisson, Hypergeometric, and negative binomial distributions .(20L)
Continuous Univariate distributions uniform, normal Cauchy; Laplace, Exponential,Chi-square,Gamma and Beta distributions, Bivariate normal distribution (including marginal and conditional distributions (25L)
Chebyshey's inequality and applications, statements and applications of weak law of large number and central limit theorems.(5L)
GROUP-B
4 Questions
DESCRIPTIVE STATISTICS
Types of data :- concepts of statistical population and sample from a population; qualitative and quantitative data nominal and ordinal data; cross sectional and time series data; discrete and continuous data, frequency and none frequency data.
Different type of scale -nominal ordinal ratio and interval.
Collection and scrutiny of data, primary data designing a questionnaire and a schedule, checking their constituency , secondary data its major sources experiments,observational studies and sample survey scrutiny of data for internal consistency and detection of errors of recording ideas of cross validation (5L).
Presentation of data:-
Constructions of table with one or more/factors of classification .Diagrammatic and graphical representation of groups data.
Frequency cumulative frequency distributions and their graphical representation- histogram,frequency polygon and gives steam and leaf chat box plot ( 15 ).
Analysis of quantitative data:- univariate data: concept of Central tendency or location , dispersion and relative discription .
Skewness and kurtosis, and their measures including those best on quantities and moments . Sheppard's correction for moments for grouped data without derivation (20L)
Bivariate data:- scatter digram,product moment correlation coefficient and its properties.
Coefficient of determination. Correlation ratio, concepts of error in regression , principle of least squares.
Fitting of linear regression and related result. Fitting of curves reducible to polynomial transformation.
Rank correlation-Spearman's and Kendall's measures.(20L)
Multivariate data:- multiple regression multiple correlation and partial correlation in three variables.
Their measures and related results. (15L)
Analysis of categorical data:-
Consistency of categorical data, independence and association of attributes. Various measures of association for two way and three way classified data. Odd ratio. (10L)
PRACTICAL
- Presentation of data by frequency tables, Diagrams and graphs.
- calculation of measures of Central tendency, dispersion; skewness and Kurtosis.
- Product moment correlation and correlation ratio.
- Fitting of curves by the least square method.
- Regression of two variables.
- Spearman's rank correlation and Kendall's tau.
- Multiple regression of 3 variables.
- Multiple correlation and partial correlation.
- evolution of probability using addition and multiplication theorems. Conditional probability and Baye's theorem.
- Exercises on mathematical !expectations and finding measures of Central tendency dispersion and skewness and kurtosis of univariate probability distribution.
- Fitting of standard Univariate: and continuous distributions. (30 Practicals)
Undergraduate curriculum of Home science
MDS University at undergraduate level two types of course have been prescribed in home science. Students have to select any one of them according to their needs and eligibility .
- Food and nutrition
- Home science - composite
Eligibility - for the B.Sc. honours in food and nutrition:
The candidates should have completed intermediate / junior College (+2) with physics chemistry and biology.
For the B.Sc. honours in composite - home science
the candidate should have completed intermediate / junior College (+2) with any of three combination of subjects:
Physics, Chemistry ,Biology ,Home science, Commerce ,Economics , Sociology, Psychology, Mathematics,Accountancy, Computer science.
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