# Month: May 2018

## Quick start with R: Basic commands (Part 8)

In Part 8, let’s look at some basic commands in R. Set up the following vectors by cutting and pasting from this document: a <- c(3,-7,-3,-9,3,-1,2,-12, -14) b <- c(3,7,-5, 1, 5,-6,-9,16, -8) d <- c(1,2,3,4,5,6,7,8,9) Now figure out what each of the following commands do. You should not need me to explain each command, …

## Using Sample Size Calculator application

The following video tutorial gives a brief overview of the Sample Size Calculator application. The Sample Size Calculator is an interactive Shiny application which allows you to calculate sample size when estimating population mean value or population proportion.   Zlatko KovačićDirector of Wellington based My Statistical Consultant Ltd company. Retired Associate Professor in Statistics. Has …

## Quick start with R: Further plotting (Part 7)

In Part 7, let’s look at further plotting in R. Try entering the following three commands to create three variables. X <- c(3, 4, 6, 6, 7, 8, 9, 12) B1 <- c(4, 5, 6, 7, 17, 18, 19, 22) B2 <- c(3, 5, 8, 10, 19, 21, 22, 26) Graph B1 using a y …

## Quick start with R: Basic plotting (Part 6)

In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line). x <- seq(-4, 4, 0.2) ;  y <- 2*x^2 + 4*x – 7 plot(x, y) This is a very basic plot, but we can do much …

## Quick start with R: Exponential models (Part 5)

In Parts 3 and 4 we used the lm() command to perform least squares regressions. We saw how to check for non-linearity in our data by fitting polynomial models and checking whether they fit the data better than a linear model. Now let’s see how to fit an exponential model in R. As before, we …

## Quick start with R: Advanced regression models (Part 4)

In Part 3 we used the lm() command to perform least squares regressions. In Part 4 we will look at more advanced aspects of regression models and see what R has to offer. One way of checking for non-linearity in your data is to fit a polynomial model and check whether the polynomial model fits …

## Quick start with R: More about regression (Part 3)

In Part 2 we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again. height = c(186, 165, 149, 206, 143, 187, 191, 179, 162, 185) weight = c(89, 56, …

## Quick start with R: Graph and simple regression (Part 2)

In Part 1 we installed R and used it to create a variable and summarise it using a few simple commands. Today let’s re-create that variable and also create a second variable, and see what we can do with them. As before, we take height to be a variable that describes the heights (in cm) …

## Quick start with R

Many of you have heard of R (the R statistics language and environment for scientific and statistical computing and graphics). Perhaps you know that it uses command line input rather than pull-down menus. Perhaps you feel that this makes R hard to use and somewhat intimidating! Indeed, R has a longer learning curve than other …

## Installing R packages

Select CRAN server Click the R desktop icon to start R program. The start screen should look like the following. Click Packages on the main menu and then select Set CRAN mirror from the drop-down list. There are dozens of servers around the world from where R and its packages could be downloaded and istalled. …