R

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==Introduction to R==

R ( http://www.r-project.org/) is an open source software- a well organized and sophisticated package- that facilitates data analysis, modeling, inferential testing and forecasting. It is a user friendly software which allows to create new function commands to solve statistical problems. It runs on a variety of UNIX platforms(and similar systems such as LINUX), Windows and Mac OS.

R is a free, useful software package to anybody who wishes to undertake extensive statistical computations (a user community that includes students, researchers and professionals belonging to various disciplines).


Basic Level

Introductory sessions in R

  1. Installing R
    • Downloading and installing R
  2. Basic operations in the R console
    • To open the R console
    • To run commands in R
    • To correct errors made in the R commands
    • To save work done in R
    • To quit the R console
  3. Documentation and Packages in R
    • To access installed documentation and packages in R
    • To install and load packages in R
    Data structures
  4. Variables and Vectors in R
    • Creation and deletion of variables and vectors
    • Listing the vectors
    • Modifying vectors
    • Creating row and column vectors
  5. Vector Algebra and Matrices in R
    • Vector algebra
    • Creating matrices
    • Matrix operations
  6. Sequences, lists, strings and dates in R
    • Creation of sequences and lists
    • Modifying lists, selecting elements from a list
    • Modifying strings, substrings
    • Date-string conversion
    • Other functions related to dates
  7. Handling data in R
    • Creating and modifying data frames
    • Reading data stored in files of different formats
    Basic computations in R
  8. Elementary operations in R
    • Arithmetic
    • Higher powers and roots of a number
    • Logarithms and exponentials
    • Operations on complex numbers
  9. Measures of central tendency and dispersion
    • Mean, median and mode
    • Variance, standard deviation and quantiles
    Probability distributions
  10. Discrete probability distributions:Binomial,Poisson and Geometric
    • Binomial,Poisson and Geometric densities, distribution and quantile functions, random variables
  11. Discrete probability distributions:Negative Binomial and Hypergeometric
    • Negative Binomial and Hypergeometric densities, distribution and quantile functions, random variables
  12. Continuous probability distributions
    • Normal, Chi squared, F and t densities, distribution and quantile functions, random variables
    • Log-normal, logistic, exponential and gamma densities, distribution and quantile functions, random variables
    • Beta, cauchy and weibull densities, distribution and quantile functions, random variables
    Graphical representation of information using R
  13. Histograms, barcharts and box plots
    • Creating histograms, addding density estimate to a histogram
    • Creating and colouring bar charts, adding confidence intervals
    • Creating box plots
  14. Scatter diagrams, regression lines and Q-Q plots
    • Plotting a scatter digram, adding title, label, grid and a legend
    • Graphing a function, a regression line (superimposing on scatter plot)
    • Creating Q-Q plots
    Econometrics in R
  15. Simple and multiple linear regression
    • OLS, log-linear, log-log and semi-log regressions
    • Dummy variable regression, regression through the origin and with standardised coefficients
  16. Regression Analysis
    • Confidence intervals
    • P-values and power functions
  17. Tests for heteroskcedasticity
    • Park test
    • Goldfeld-Quandt test
    • Breusch-Pagan-Godfrey test
    • White’s General Heteroskcedasticity test
  18. Tests for autocorrelation and specification errors
    • Durbin Watson test
    • Ramsey reset specification test

ADVANCED LEVEL TUTORIALS

  1. Models of microeconometrics
  2. Bayesian Econometrics
  3. Time series Econometrics
  4. Programming your own analysis

Contributors and Content Editors

Manivel, Nancyvarkey, Sudhakarst