4
Lessons
22h
Duration
English
Language
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OBJECTIVES:
- R is an open source environment used for solving the statistical problems. This package is useful for researchers doing research in their respective fields; R will help you in analyzing your experimental results.
- In this course, the candidate will learn how to program in R and how to use R for effective data analysis and other statistical inference. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
- These modules covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.
- R For: R programming tool is useful for all the research scholars and business developers for performing the analysis on the scientific data, scholars from following field can use this environment for their effective data analysis;
- Commerce
- Management
- Computer Science
- Information Technology
- Biological / Life Sciences
- Bioinformatics
- Biotechnology
- Engineering / Technology
- Economics
- Ergonomics
- Medical Sciences etc.
Course features:
- Practical hands on
- Lab sessions
- Training by experienced faculty
PRE-REQUISITES:
- Basic Knowledge about computers, Excel and logical skills.
Learning Path
- Downloading and Installing R
- Starting R
- Using Help Functions
- Searching the Packages
- Importing the Packages
- Solving expressions
- Creating Variables
- Vectors
- Computing Basic Statistics
- Creating Sequences
- Comparing Vectors
- Performing Vector Arithmetic
- Defining Functions
- Creating Functions
- Input / Output Operations: Entering Data from Keyboard
- Redirecting Output Files
- Listing Files
- Importing data from Excel
4 hours
- Data Transformation: Splitting Vectors in Groups, Handling List and Vectors
- Basic String Operations
- Probability: Counting Number of Combinations
- Random Number Generation
- Generating Random Samples
- Probabilistic Calculation for Discrete and Continuous Distributions
6 hours
- Statistics: Summarizing Data
- Calculating Relative Frequency
- Tabulating Factors
- Testing Categorical Variables
- Quantiles
- Converting data to Z-Score
- Chi-square
- Testing the Mean of Sample
- Confidence Interval of Mean
- Median
- Proportion
- Testing for Normality
- Runs
- Means of Two Samples
- Testing a Correlation for Significance
- Performing Pairwise Comparison of Mean
- Graphs Plotting: Scatter Plot
- Adding Grid
- Adding a Legend
- Plotting Regression Variable
- Bar Chart
- Box Plot
- Histogram
6 hours
- Statistical models in R: Defining statistical models; formulae
- Contrasts
- Linear models
- Generic functions for extracting model information
- Analysis of variance and model comparison
- ANOVA tables
- Updating fitted models
- Generalized linear models
- Families
- The glm() function
- Nonlinear least squares and maximum likelihood models
- Least squares
- Maximum likelihood
- Some non-standard models
- Solving Research Problem