Source document. This page reproduces the syllabus this course was written to, as published — its semesters, credits and paper numbers are that document’s, not this site’s. The course itself is studied on its own, in any order.
Course Information
Title
Descriptive Statistics
Theory Credits
3 (3 hrs/week)
Practical Credits
1 (2 hrs/week)
Program Objectives
To build the basis for promoting various statistical methods theoretically and their applications
in study of multidisciplinary sciences by emphasizing real life problems.
To inculcate statistical thinking and computer approach towards statistical methods, tools and
techniques among the students.
To develop skills in handling complex problems in data analysis and research design.
Course Outcomes
After successful completion of the course, students will be able to:
Acquaint with the role of statistics in different fields with special reference to business and economics.
Review good practice in presentation and the format most applicable to their own data.
Learn the measures of central tendency or averages reduce the data to a single value which is highly useful for making comparative studies.
Familiar with the measures of dispersion throw light on reliability of average and control of variability.
Theory — Five Units
Unit 1: Statistical Description of Data
Origin, history and definitions of Statistics. Importance, Scope and limitations of Statistics.
Function of Statistics — Collection, Presentation, Analysis and Interpretation. Collection of data —
primary and secondary data and its methods. Classification of data — Quantitative, Qualitative,
Temporal, Spatial. Presentation of data — Textual, Tabular — essential parts.
Unit 2: Measurement Scales & Frequency Distribution
Measurement Scales — Nominal, Ordinal, Ratio and Interval. Frequency distribution and types of
frequency distributions, forming a frequency distribution. Diagrammatic representation of data —
Historiagram, Bar, Multiple bar and Pie with simple problems. Graphical representation of data:
Histogram, frequency polygon and Ogives with simple problems.
Arithmetic Mean — properties, methods. Median, Mode, Geometric Mean (GM), Harmonic Mean (HM).
Calculation of mean, median, mode, GM and HM for grouped and ungrouped data. Median and Mode through
graph. Empirical relation between mean, median and mode. Features of good average.
Concept and problems — Range, Quartile Deviation, Mean Deviation and Standard Deviation and their
coefficients, Variance and its applications viz. Business and Pharmacy etc.
Central and Non-Central moments and their interrelationship, Problems. Sheppard's correction for
moments and problems. Skewness and its methods, kurtosis and related problems.