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- Download Statistics with R Programming Notes PDF
- List of Reference Books for Statistics with R Programming
- Statistics with R Programming Syllabus
- Statistics with R Programming Important Questions
- What books should I read to learn Statistics with R Programming?
- How to download PDF Formatted Statistics with R Programming Notes & Study Material?
- What are some good resources for learning Statistics with R Programming?
About Statistics with R Programming
Students taken up this course can use R for Statistical Programming, Computation Graphics, Modeling, Write functions, and Use R in an effective way. R language is used among statisticians and data miners for developing statistical software and data analysis. It is an Open Source Programming Language and software environment for Statistical Computing.
Download Statistics with R Programming Notes PDF
Candidates pursuing their B.Tech can use the Statistics with R Programming Notes available on this page. Refer to the Study Material and Notes PDF and score well in the exam. Access the Statistics with R Programming Question Papers and use them to know the kind of questions appearing in the exams. Gain indepth knowledge of the topics and lay a stronger foundation of concepts.
| Introduction to R Statistics | Download |
| Introduction to R Programming | Download |
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| Statistics with R Programming Question Paper | Download |
List of Reference Books for Statistics with R Programming
With the right Books for Statistics with R Programming, you can have an indepth knowledge of the concepts. Refer to the following best books as a part of preparation. Kick start your preparation right now and score max. marks in the exam. The books over here will help you learn the basics of R Programming that can be useful for you in the future. Use these preparation aids that can help you to have a better understanding of concepts.
- 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
Also, Refer: Computer Programming Notes
Statistics with R Programming Syllabus
For those who are preparing for the upcoming Statistics with R Programming Exam can check the syllabus here. Be aware of the Statistics with R Programming Exam Syllabus before you dive into preparation. Have an overview of overall concepts in the R Programming Syllabus and get to know the topics you should prepare. Go through the Unitwise Statistics with R Programming Concepts to have a clear idea of what to study. Finish all the topics before attempting the exam so that you can score high in the exam easily.
Unit I
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
Unit II
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
Unit III
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
Unit IV
Creating Graphs, Graphics, The Workhorse of R Base Graphics, the plot() Function, Customizing Graphs, Saving Graphs to Files
Unit V
Probability Distributions, Normal Distribution, Poisson Distributions, Binomial Distribution, Other Distribution, Correlation and Covariance, Basic Statistics, T-Tests,-ANOVA
Unit VI
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
Statistics with R Programming Important Questions
The following questions list will assist you in your preparation and makes it easy to clear the Exam. Refer to all of these and get a grip on the subject so that you can clear the exam with ease.
- Explain about Variables, Constants and Data Types in R Programming
- How to create a user-defined function in R? How to define default values in R? Write syntax and examples?
- Write about Arithmetic and Boolean operators in R programming?
- How to create, name, access, merging, and manipulate list elements? Explain with examples.
- Write an R function to find sample covariance.
- Describe R functions for Reading a Matrix or Data Frame From a File
- Explain functions for accessing the keyboard and monitor, Reading and writing files
- Write about the following functions with example
a)points() b) legend() c)text() d) locator() - 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
Do Check: Robotics Engineering Books
FAQs on Statistics with R Programming
1. What books should I read to learn Statistics with R Programming?
Aspirants can refer to the following books referred by subject experts and they are as follows
1. The Art of R Programming, Norman Matloff, Cengage Learning
R for Everyone, Lander, Pearson
2. Siegel, S. (1956), Nonparametric Statistics for the Behavioral Sciences, McGraw-Hill International, Auckland
3. R Cookbook, PaulTeetor, Oreilly
4. R in Action, Rob Kabacoff, Manning
Final Words
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