Outcomes

After completion of the course the student should be able to

Descriptive Statistics

CO 1.knowledge of Statistics and its scope and importance in various areas such as Medical, Engineering, Agricultural and Social Sciences etc.

CO 2.knowledge of various types of data, their organization and evaluation of summary measures such as measures of central tendency and dispersion etc.

CO 3. knowledge of other types of data reflecting quality characteristics including concepts of independence and association between two attributes,

CO4. Insights into preliminary exploration of different types of data.

CO5.Knowledge of correlation, regression analysis, regression diagnostics, partial and multiple correlations

SEMESTER-II

CO1.Ability to distinguish between random and non-random experiments,

CO 2.knowledge to conceptualize the probabilities of events including frequentest and axiomatic approach. Simultaneously, they will learn the notion of conditional probability including the concept of Bayes’ Theorem,

CO 3.knowledge related to concept of discrete and continuous random variables and their probability distributions including expectation and moments,

CO 4.knowledge of important discrete and continuous distributions such as Binomial, Poisson, Geometric, Negative Binomial and Hyper-geometric, normal, uniform, exponential, beta and gamma distributions,

CO5.Acumen to apply standard discrete and continuous probability distributions to different situations.

SEMESTER- III

CO1.Concept of law large numbers and their uses

CO 2.Concept of central limit theorem and its uses in statistics

CO 3.concept of random sample from a distribution, sampling distribution of a statistic, standard error of important estimates such as mean and proportions,

CO4.knowledge about important inferential aspects such as point estimation, test of hypotheses and associated concepts,

COC CO5.knowledge about inferences from Binomial, Poisson and Normal distributions as illustrations,

CO1.Introduced to various statistical sampling schemes such as simple, stratified and systematic sampling.

CO 2.An idea of conducting the sample surveys and selecting appropriate sampling techniques,

CO 3.Knowledge about comparing various sampling techniques.

CO4.carry out one way and two way Analysis of Variance,

CO5.understand the basic terms used in design of experiments,

Applied Statistics

CO1.time series data, its applications to various fields and components of time series,

CO 2.fitting and plotting of various growth curves such as modified exponential, Gompertz and logistic curve,

CO 3.fitting of trend by Moving Average method,

CO4.measurement of Seasonal Indices by Ratio-to-Trend , Ratio-to-Moving Average and Link Relative methods,

CO5.Applications to real data by means of laboratory assignments.

CO1.To know the scope of Operations Research

CO 2. To link the OR techniques with business environment and life sciences

CO 3. To convert real life problems into mathematical models

CO4. To find a solution to the problem in different cases

CO5. To inculcate logical thinking to find a solution to the problem.

CO1.To solve the problems in logistics

CO 2.To find a solution for the problems having space constraints

CO 3.To minimize the total elapsed time in an industry by efficient allocation of jobs to the suitable persons.

CO4. To find a solution for an adequate usage of human resources

CO5.To find the most plausible solutions in industries and agriculture when a random environment exists.