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Statistics With R Programming Pdf Notes October 2018

Statistics With R Programming: You can download the Study materials and notes for Statistics With R Programming in PDF files from the official website.

Statistics With R Programming Books

The Books and Study Materials for B-tech are updated on the official website. Candidates who are on a hunt for the Books and Study Materials for B-tech can check the web. We have updated many Books on several subjects so that candidates can choose the books they want and start preparing for the examination. In this article, we are focusing on the subject of Statistics With R Programming. This subject aims to provide essential knowledge about R programming and Statistics.

Statistics With R Programming Books 2018

R is a free software environment for statistical computing and programming language and graphics that are supported by the R Foundation for Statistical Computing. This subject aims to provide the basic knowledge of Statistics and R language so that it will be helpful for their future endeavors. For this, We are providing the best Books and Study Materials which are recommended by experts. These Books will be beneficial for you at the time of preparation for your examination. The detailed syllabus of the subject is also available on the web so that candidate can follow it and the learning process will be natural.

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 /output, 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 Pdf Notes

introduction to r statistics Download
introduction to r programming Download
Statistics with R Programming notes pdf Study material Download
Statistics with R Programming Question Paper Download

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

Frequently Asked 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 a user-defined function in R? How to specify 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 for 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 a model
  •  Calculate the coefficient of correlation to the following data
    X 10 12 18 24 23 27
    Y 13 18 12 25 30 10

All the necessary materials for the Statistics with R Programming are updated on the website. Candidates can download the files in Pdf format for free or can purchase it directly. Go through all the questions mentioned above and the previous year question to gain confidence to face the examination. Make sure that you share this link with your friends so that these books will be helpful for them also.

Candidates can keep in touch with our website for more information on Statistics With R Programming Books.


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