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galaxy_bed <- read.table(file = "Galaxy1-[Main_on_Human].bed", stringsAsFactors = FALSE)

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names(galaxy_bed) <- c("chrom",
  "chromStart",
  "chromEnd",
  "name",
  "score",
  "strand",
  "blockCount",
  "blockSizes")

# This shows how to 'subset' with either the $ operator or [[

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# CHAPTER 1

# FUNDAMENTALS

# The response variable is the things you are working on, the variable whose variation you are attempting to understand.
# This is the variable that goes in the "y" axis of the graph (the ordinate). 
# The explanatory variable goes on the "x" axis of the graph (the abscissa).

# The explanatory variables:
# - Regression: all explanatory variables continuous.

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#!/usr/bin/perl -w
# 
use strict;
use Getopt::Long;

# You might want to have a flag --binwidth and --bintarget
# perl ion_stats.pl --binwidth 30 --bintarget 150
# the above would specify bin parameters: 135-165

my $binwidth;

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# CHAPTER 3

#ENTRAL TENDENCY

yvals<-read.csv("C:\\MSc\\Statistics\\Data\\yvalues.csv")
# open file

attach(yvals)
hist(yvals)
# the data values that occour the most are called 'mode'

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# CHAPTER 6

# TWO SAMPLES

# COMPARING TWO VARIANCES 

# To test to see whether the sample variances are significantly different we use Fisher's F test
# You divide the larger variance by the smaller variance
# We need to look for the critical value of the variance ratio 'qf' (quantiles of the F distribution)

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#!/usr/bin/perl -w
#
BEGIN{
        use File::Basename;
        push @INC,
use Getopt::Long
#
my $binwidth;
my $bintarget;
GetOptions ("binwidth" => \$binwidth,

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package session01;

import java.util.Scanner;

public class  calc {

	public static void main(String[] args) {
		
		Scanner keyboard = new Scanner(System.in);
		

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#!/usr/bin/perl 

use diagnostics;
use strict;
use warnings;
use Getopt::Long;

# Defining bin size and parameters

my $data =  "table.tsv";

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#!/usr/bin/env perl

package ProNTo::IonStats;

use diagnostics;
use strict;
use warnings;
use Getopt::Long;

use Exporter;