WEDNESDAY, March 7
10:30-12:00 (Session A - SB)
Introduction to Statistical Methods
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What is Statistics?
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Principles of Statistical Methods
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Importance in Applied Sciences
Day 1
12:00-13:00 (Session B - BC, SM)
Introduction to R and RStudio
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Installation and different options in RStudio, customizing the environment
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Installing Packages (install.packages), loading packages (require, library), project, workspace
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Looking into help files
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Familiar with options (setwd, getwd, sessionInfo)
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Document generation options
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Get the problems resolved in individual laptops so that next six days sessions runs smoothly
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Data import and export (read.table, read.csv, write.table, write.csv)
14:30-16:00 (Session C - ARB)
Handling data in R (with example data from selected branch)
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Basic data types: numeric, integer, logical, character
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Data storage facilities in R (c(), vector, matrix, data.frame, list, rep, numeric, seq)
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Difference between matrix and data.frame
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Subsetting and modifying data (select, subset, filter, which, reshape)
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Combining data (rbind, cbind, merge)
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Some useful functions (is.na, dim, complete.cases, summary, aggregate, class)
16:30-18:00 (Session D - SM)
Statistical distribution for ecologists (using R)
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Visualizing data distributions: Histogram, boxplot (hist, boxplot, plot)
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Probability mass functions and Probability density functions
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Area under the curve (Integrate)
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Binomial distribution, Uniform distribution, Normal distribution, t- distribution (rbinom, runif, rnorm, dnorm, qnorm, pnorm)
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Skewed distribution, descriptive measures (mean, median, mode, quantile, range, sd, kurtosis, skew, describe, min, max, var)
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Strong emphasis on visualizing distribution functions
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Important packages: Hmisc, psych
