Coming soon
Applied Statistics (Theory)
This course will enable the students to:
- To understand the role of statistics and computer applications in research.
- To apply statistical techniques to research data for analyzing and interpreting data carefully
Course Outcomes (COs):
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Course |
Learning outcomes (at course level) |
Learning and teaching strategies |
Assessment Strategies |
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Paper Code |
Paper Title |
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HFN 222 |
Research Methodology (Theory)
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The students will be able to – COFN37: Develop understanding on various kinds of research, objectives of doing research, research process, research designs and sampling. COFN38: Describe qualitative and quantitative research techniques COFN39: Develop skills on measurement & scaling techniques as well as the quantitative data analysis COFN40: Analyse data for hypothesis testing procedures and report writing.
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Approach in teaching: Interactive Lectures, Discussion, Power Point Presentations, Informative videos, group discussion
Learning activities for the students: Self learning assignments, Effective questions, presentations, Field trips
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Quiz, Poster Presentations, Power Point Presentations, Individual and group projects, Open Book Test, Semester End Examination, discussion, demonstration
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Introduction of Statistics. Classifications and Tabulation of data. Frequency Distribution. Diagrammatic(one- dimensional and two- dimensional) and Graphical presentation of data( Histogram, Frequency Polygon, Frequency curve and ogives)
Measure of Central Tendency- Mean, Median and Mode, their properties, merits and demerits.
Measure of Dispersion- Range, Quartile Deviation, Mean Deviation and standard deviation.
Concept of Probability. Properties of Normal Probability Curve and its applications.
Correlation Analysis- Definition and concept, types and measures of studying correlation (Karl Pearson’s coefficient of correlation, its assumptions, properties, merits and demerits, Spearman’s Rank correlation coefficient)
Regression Analysis- Definition, concept, uses and properties. Least Square Methods, Regression Coefficients, Fitting of Regression lines.
Sampling Distribution and Standard Error. Elements of Testing a Statistical Hypothesis- Formulation of the problem, Types of errors . Level of significance, Large sample tests for proportions, Single mean and difference in two means.
Small sample tests- Application of t- test for testing the significance of single mean & difference in two means ( independent and paired-t). Chi-square test for testing normal population variance. Test for goodness of fit, independence of attributes using 2x2 and rxc contingency tables).
Definations of F test, application of F test for testing of equality of two variances.
Analysis of Variance- Concept , assumptions, basic idea of one way and two way classification with simple questions.
