Course Outcome
B.Sc.(General) Statistics programme consists of 36 credits spread over six semesters. Each credit has one hour of class room teaching per week. This programme emphasizes both theory and applications of statistics and is structured to provide knowledge and skills in depth necessary for the employability of students in industry, other organizations, as well as in academics.
COURSE OUTCOME
B.Sc. (General) Statistics programme is of three years duration, with semester pattern.
1. During first two semesters, students will be given the basic information that includes methods of data representation and summarization. Further, they will be introduced to probability and distributions along with applications, correlation and regression techniques.
2. During third and fourth semesters, students are expected to study statistical inference, designs of experiments and sampling techniques.
3. During fifth and sixth semesters, some theory papers and practicals deal with theoretical as well as applied aspects of statistics. Besides, they are supposed to take up a Project Work preferably on a problem related to industries.
Outcome of the Course (Semester Wise)
Semester-I (Descriptive Statistics)
At the end of the course students will be able to
1. Define and use the basic terminology of statistics.
2. Organize and display data by means of various tables, charts, and graphs.
3. Analyze statistical data using measures of central tendency, dispersion and location.
4. Find and interpret the sample correlation coefficient (r) to determine the strength and direction of the linear relationship between predictor and response variables.
5. Use scatter plots to determine if outliers are present and if data can be represented by a simple linear regression model.
6. Find the simple linear regression model and be able to interpret the slope and y-intercept.
7. Use r-squared to determine if a simple linear regression model is a strong predictor.
8. Predict values of “y” using the simple linear regression model
Semester-II (Elementary Probability Theory)
At the end of the course students will be able to
1. Calculate probabilities by applying probability laws and theoretical results.
2. Identify an appropriate probability distribution for a given discrete or continuous random variable and use its properties to calculate probabilities.
3. Derive probability distributions of functions of random variables.
4. Derive expressions for measures such as the mean and variance of common probability distributions using calculus and algebra.
5. Calculate probabilities for joint distributions including marginal and conditional probabilities.
6. Apply results from large-sample theory and the Central Limit Theorem to approximate a sampling distribution.
Semester-III (Introduction to Statistical Inference)
At the end of the course students will be able to
1. Explain the concept of estimation of parameters.
2. Calculate the problems related to point estimation and interval estimation.
3. Explain the concepts of Testing of Hypotheses, (Large Sample Tests small sample test).
4. Solve the problems related to Testing of Hypotheses, (Large Sample Tests small sample test).
5. Identify situations where one—way ANOVA is and is not appropriate.
6. State the modeling assumptions underlying ANOVA.
7. State the null and alternative hypotheses for the ANOVA test.
8. Explain the partitioning of the total sum of squares into the “within” and “between” group components.
9. Understand the basic terms used in design of experiments.
10. Use appropriate experimental designs to analyze the experimental data.
Semester-IV (Applications of Statistics)
At the end of the course students will be able to
1. The basic principles underlying survey design and estimation.
2. Methods for designing and selecting a sample from a population.
3. How to estimate finite population parameters e.g. totals and means, for some standard sampling schemes.
4. How to assess estimation errors.
5. The ability to analyses and solve problems.
6. Understand the origins and basic features of axiomatic, economic and stochastic approaches to price index.
7. Apply the common elementary index formulae and the characteristic hedonic index method.
8. Assess the uncertainty associated with price index numbers calculated based on a sample of products.
9. Appreciate the fundamental challenges of price index based on scanner data and web scraping data.
10. Understand the basic structure of the consumer price index (CPI) and perform calculations involving its use.
11. Understand the concepts of time series analysis in the time domain.
12. Be able to determine and apply appropriate models for the real life datasets.
13. Have developed skills in statistical computing of time series problems.
14. Understand and apply advanced methods in demography Interpret complex demographic data.
15. Analyses data using key advanced demographic methods.
16. Generate clear and professional reports based on demographic analyses.
17. Apply demographic methods to understanding current issues in demography and health.