# R Analytics & R-Studio

R is a command-driven statistical package. At first sight, this can make it rather daunting to use. However, there are a number of reasons to learn statistics using this computer program. The two most important are:

• R is free; you can download it from http://www.r-project.org and install it onto just about any sort of computer you like.
• R allows you to do all the statistical tests you are likely to need, from simple to highly advanced ones. This means that you should always be able to perform the right analysis on your data.   An additional bonus is that R has excellent graphics and programming capabilities, so can be used as an aid to teaching and learning. For example, all the illustrations in this book have been produced using R; by clicking on any illustration, you can obtain the R commands used to produce it.

A final benefit, which is of more use once you have some basic knowledge of either statistics or R, is that there are many online resources to help users of R.

## R-Statistics in Dataedy • Univariarte Analysis
• 1.1 Univariarte Exploratory Data Analysis
• 1.2 Point estimation; inference of the mean
1. Bivariate correlation and regression
• 2.1 Conceptual issues in correlation and regression
• 2.2 Bivariate Exploratory Data Analysis
• 2.3 Bivariate Correlation Analysis
• 2.4 Fitting a regression line
• 2.5 Bivariate Linear Regression
• 2.6 Bivariate Regression Analysis from scratch
• 2.7 Regression diagnostics
• 2.7.1 Fit to observed data
• 2.7.2 Large residuals
• 2.7.3 Distribution of residuals
• 2.7.4 Leverage
• 2.7.5 DFBETAS
• 2.8 Prediction
• 2.9 Robust regression
• 2.10 Structural Analysis
• 2.11 Structural Analysis by Principal Components
• 2.12 A more difficult case
• 2.13 Non-parametric correlation
1. One-way Analysis of Variance (ANOVA)
• 3.1 Exploratory Data Analysis
• 3.2 One-way ANOVA
• 3.3 ANOVA as a linear model
• 3.4 Means separation
• 3.5 One-way ANOVA from scratch

• 4 Multivariate correlation and regression
• 4.1 Multiple Correlation Analysis
• 4.1.1 Pairwise simple correlations
• 4.1.2 Pairwise partial correlations
• 4.2 Multiple Regression Analysis
• 4.3 Comparing regression models
• 4.3.1 Comparing regression models with the adjusted R
• 4.3.2 Comparing regression models with the AIC
• 4.3.3 Comparing regression models with ANOVA
• 4.4 Stepwise multiple regression
• 4.5 Combining discrete and continuous predictors
• 4.6 Diagnosing multi-colinearity
• 4.7 Visualising parallel regression
• 4.8 Interaction
• 4.9 Analysis of covariance
• 4.10 Design matrices for combined models
• 5 Factor analysis
• 5.1 Principal components analysis
• 5.1.1 The synthetic variables
• 5.1.2 Residuals
• 5.1.3 Biplots
• 5.1.4 Screeplots
• 5.2 Factor analysis
• 6 Geostatistics
• 6.1 Postplots
• 6.2 Trend surfaces
• 6.3 Higher-order trend surfaces
• 6.4 Local spatial dependence and Ordinary Kriging
• 6.4.1 Spatially-explicit objects
• 6.4.2 Analysis of local spatial structure
• 6.4.3 Interpolation by Ordinary Kriging ## Our Partners

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