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Descriptive Statistics with R Software

Indian Institute of Technology Kanpur via NPTEL

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Overview

Any data analysis is incomplete without statistics. After getting the data, any statistical analysis starts with descriptive statistics which aims to extract the information hidden inside the data. The tools of descriptive statistics are based on mathematical and statistical functions which are to be evaluated using the software. The statistical software are paid as well as free. Most of the statistical software are paid software. A popular free statistical software is R. What are the basic tools of descriptive statistics and how to use the R software for descriptive statistical analysis is the objective of the course to be taught.

Syllabus

Descriptive Statistics with R Software

 

ABOUT THE COURSE:

Any data analysis is incomplete without statistics. After getting the data, any statistical analysis starts with descriptive statistics which aims to extract the information hidden inside the data. The tools of descriptive statistics are based on mathematical and statistical functions which are to be evaluated using the software. The statistical software are paid as well as free. Most of the statistical software are paid software. A popular free statistical software is R. What are the basic tools of descriptive statistics and how to use the R software for descriptive statistical analysis is the objective of the course to be taught.

INTENDED AUDIENCE:
UG students of Science and Engineering. Students of humanities with basic mathematical and statistical background can also do it. Working professionals in analytics can also do it.

CORE/ELECTIVE: Core/Elective

UG/PG: UG and PG

PREREQUISITES: Mathematics background up to class 12 is needed. Some minor statistics background is desirable.l

INDUSTRY SUPPORT: All industries having R & D set up will use this course.
 
COURSE LAYOUT:

Week 1: Calculations with R Software
Week 2: Introduction to Descriptive Statistics, frequency distribution
Week 3: Graphics and Plots
Week 4: Central Tendency of Data
Week 5: Variation in Data
Week 6: Moments, Association of Variables
Week 7: Association of Variables
Week 8: Association of Variables, Fitting of Linear Models

Taught by

Prof. Shalabh

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