is this the correct way to determine variance? calculating the Fstats, p-value and variance.

clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
% load the feature matrices
feature1 = load('feature1.mat');
feature2 = load('feature2.mat');
feature3 = load('feature3.mat');
feature4 = load('feature4.mat');
feature5 = load('feature5.mat');
feature6 = load('feature6.mat');
% Combine the first two structs into a single struct
mouse1 = [feature1.features, feature2.features];
% Combine the next two structs into a single struct
mouse2 = [feature3.features, feature4.features];
% Combine the last two structs into a single struct
mouse3 = [feature5.features, feature6.features];
%extract fields

%mouse1 profilecounts
profileCounts1 = [mouse1(1).profileCounts; mouse1(2).profileCounts; mouse1(3).profileCounts; mouse1(4).profileCounts; mouse1(5).profileCounts; mouse1(6).profileCounts];
%mouse2 profilecounts
profileCounts2 = [mouse2(1).profileCounts; mouse2(2).profileCounts; mouse2(3).profileCounts; mouse2(4).profileCounts; mouse2(5).profileCounts; mouse2(6).profileCounts];
%%mouse3 profilecounts
profileCounts3 = [mouse3(1).profileCounts; mouse3(2).profileCounts; mouse3(3).profileCounts; mouse3(4).profileCounts; mouse3(5).profileCounts; mouse3(6).profileCounts];

% Concatenate profileCounts for each group
% Calculate the variance between the groups
var_between_groups = var([profileCounts1; profileCounts2; profileCounts3]);
% Calculate the degrees of freedom for the F-test
df_between_groups = 2; % the number of groups minus 1
df_within_groups = length(profileCounts1) + length(profileCounts2) + length(profileCounts3) - 3; % the total number of observations minus the number of groups
% Calculate the variance within each group
var_within_groups = (var(profileCounts1)*(length(profileCounts1)-1) + var(profileCounts2)*(length(profileCounts2)-1) + var(profileCounts3)*(length(profileCounts3)-1))/(length(profileCounts1) + length(profileCounts2) + length(profileCounts3) - 3);
% Calculate the variance between the groups
var_between_groups = var([profileCounts1; profileCounts2; profileCounts3]);
% Calculate the F-statistic
F_statistic = var_between_groups/var_within_groups;
% Calculate the degrees of freedom
df_between_groups = 2 - 1;
df_within_groups = length(profileCounts1) + length(profileCounts2) + length(profileCounts3) - 3;
% Calculate the p-value
p_value = 1 - fcdf(F_statistic, df_between_groups, df_within_groups);
% Determine if the variance between the groups is significant
if p_value < 0.05
    disp('The variance between the groups is significant');
else
    disp('The variance between the groups is not significant');
end

[Chanille]

@Cris LaPierre how is this duplicate? to where?

[Chanille]

I am following this thread, thanks!

[Laura]

@Cris LaPierre there are two different ways i could approach this. My question is should i do it this way or in my other question where i used the anova test. Sorry if I did not clearly state this as a seperate question. Please advise. Thank you!

@Chanille please see this thread: https://www.mathworks.com/matlabcentral/answers/1956664-error-x-and-group-must-have-the-same-length-but-the-lengths-are-the-same-this-must-be-a-malfunctin?s_tid=srchtitle

[Cris LaPierre]

You didn't share your mat files, so the intent was to give others a way to find them. You now provide the link in your comment, so I've removed the flag.

[Laura]

@Cris LaPierre Oh I understand, thanks!! Since my intent is to compare each field variable (profileCounts, zoneArea et al.) in feature 1 and 2 (group1) to feature 3 and 4 (group2) then to feature 5 and 6 (group3) and I was having a hard time getting doing that with anova would this work where the comparisons were performed brute force? Is this method correct or is there another method? Would greatly appreciate your helpp!!! by the way i added the files for ease.