Statistics with R Programming Notes- Download B.Tech Study Material

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Statistics with R Programming Pdf Notes

After taking the course, students will be able to Use R for statistical programming, computation, graphics, and modeling, Write functions and use R in an efficient way, Fit some basic types of statistical models, Use R in their own research, Be able to expand their knowledge of R on their own. R is a statistical computer program made available through the Internet under the General Public License (GPL).

That is, it is supplied with a license that allows you to use it freely, distribute it, or even sell it, as long as the receiver has the same rights and the source code is freely available. It exists for Microsoft Windows XP or later, for a variety of Unix and Linux platforms, and for Apple Macintosh OS X.

Introduction to R Statistics:

R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. … R is a GNU package.

Statistics with R programming Pdf Notes

introduction to r statistics


introduction to r programming


Statistics with R Programming notes pdf Study material


Statistics with R Programming Question Paper


List of Reference Books for Statistics with R Programming- 2nd Year

  • The Art of R Programming, Norman Matloff, Cengage Learning
  • R for Everyone, Lander, Pearson
  • Siegel, S. (1956), Nonparametric Statistics for the Behavioral Sciences, McGraw-Hill International, Auckland.
  • R Cookbook, PaulTeetor, Oreilly.
  • R in Action, Rob Kabacoff, Manning
  • Venables, W. N., and Ripley, B. D. (2000), S Programming, Springer-Verlag, New York.
  • Venables, W. N., and Ripley, B. D. (2002), Modern Applied Statistics with S, 4th ed., Springer-Verlag, New York.
  • Weisberg, S. (1985), Applied Linear Regression, 2nd ed., John Wiley & Sons, New York.
  • Zar, J. H. (1999), Biostatistical Analysis, Prentice Hall, Englewood Cliffs, NJ

Statistics with R Programming Syllabus – 1st sem


Introduction, How to run R, R Sessions, and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes.


R Programming Structures, Control Statements, Loops, – Looping Over Nonvector Sets,- If-Else, Arithmetic, and Boolean Operators and values, Default Values for Argument, Return Values, Deciding Whether to explicitly call return- Returning Complex Objects, Functions are Objective, No Pointers in R, Recursion, A Quicksort Implementation-Extended Extended Example: A Binary Search Tree.


Doing Math and Simulation in R, Math Function, Extended Example Calculating Probability- Cumulative Sums and Products-Minima and Maxima- Calculus, Functions Fir Statistical Distribution, Sorting, Linear Algebra Operation on Vectors and Matrices, Extended Example: Vector cross Product- Extended Example: Finding Stationary Distribution of Markov Chains, Set Operation, Input /out put, Accessing the Keyboard and Monitor, Reading and writer Files,


Graphics, Creating Graphs, The Workhorse of R Base Graphics, the plot() Function – Customizing Graphs, Saving Graphs to Files.


Probability Distributions, Normal Distribution- Binomial Distribution- Poisson Distributions Other Distribution, Basic Statistics, Correlation and Covariance, T-Tests,-ANOVA.


Linear Models, Simple Linear Regression, -Multiple Regression Generalized Linear Models, Logistic Regression, – Poisson Regression- other Generalized Linear Models-Survival Analysis, Nonlinear Models, Splines- Decision- Random Forests,


At the end of this course, students will be able to:
• List motivation for learning a programming language
• Access online resources for R and import new function packages into the R workspace
• Import, review, manipulate and summarize data-sets in R
• Explore data-sets to create testable hypotheses and identify appropriate statistical tests
• Perform appropriate statistical tests using R Create and edit visualizations with

Statistics with R Programming Important Questions

  • Explain about Variables, Constants and Data Types in R Programming
  • How to create, name , access , merging and manipulate list elements? Explain with examples.
  •  Write about Arithmetic and Boolean operators in R programming?
  •  How to create user defined function in R? How to define default values in R? Write syntax and examples?
  •  Explain functions for accessing the keyboard and monitor, Reading and writing files
  •  Write an R function to find sample covariance.
  • Write about the following functions with example
    a)points() b) legend() c)text() d) locator()
  •  Describe R functions for Reading a Matrix or Data Frame From a File
  • Fit a poisson distribution to the following data
    x 0,1,2,3,4,5
    f 3,9,12,27,4,1
    Also, test the adequacy of the model
  •  Calculate the coefficient of correlation to the following data
    X 10 12 18 24 23 27
    Y 13 18 12 25 30 10

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