i would like to know why my code doesn’t work please, the data load factors is supposed to be taken from a .txt file from DASL website
function load_factors_analysis()
% Load the data (assuming ‘load_factors.csv’ is in the working directory)
data = load(‘load_factors.csv’);
% Get central and dispersion measurements for each variable
[mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data);
% Create histograms and/or bar graphs for each variable
histograms_and_bar_graphs(data);
% Create a boxplot (assuming data(:,1) is continuous and data(:,2) is categorical)
boxplot_categorical(data(:, 1), data(:, 2));
% Perform a regression analysis (assuming data(:,1) and data(:,2) are continuous)
regression_analysis(data(:, 1), data(:, 2));
% Discuss the results (replace with your actual analysis)
discussion(data, mean, median, stddev, min_val, max_val);
end
function [mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data)
% Get central and dispersion statistics
mean = mean(data);
median = median(data);
stddev = std(data);
min_val = min(data);
max_val = max(data);
% Return the calculated values
return (mean, median; stddev; min_val; max_val);
end
function histograms_and_bar_graphs(data)
for i = 1:size(data, 2)
if isnumeric(data(:, i))
h = histogram(data(:, i));
title(strcat(‘Histogram of ‘, data(1, i)));
show(h);
else
b = bar(unique(data(:, i)), count(data(:, i)));
title(strcat(‘Bar graph of ‘, data(1, i)));
show(b);
end
end
end
function boxplot_categorical(data_continuous, data_categorical)
% Create a boxplot for continuous data divided by categorical classes
boxplot(data_continuous, data_categorical);
end
function regression_analysis(data1, data2)
% Perform linear regression
[b, a, rsq, pval, se] = regress(data1, data2);
% Print the results
disp(‘Coefficients:’);
disp([b, a]);
disp(‘R-squared:’);
disp(rsq);
disp(‘P-value:’);
disp(pval);
disp(‘Standard error:’);
disp(se);
end
function discussion(~, ~, ~, ~, ~, ~)
% Replace this with your analysis of the data and calculated statistics
disp(‘This is a placeholder for your data analysis discussion.’);
disp(‘Here, you would discuss insights from the central tendency’);
disp(‘(mean, median), dispersion (standard deviation),’);
disp(‘minimum and maximum values, and any relationships found’);
disp(‘between variables using the boxplot and regression analysis.’);
endfunction load_factors_analysis()
% Load the data (assuming ‘load_factors.csv’ is in the working directory)
data = load(‘load_factors.csv’);
% Get central and dispersion measurements for each variable
[mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data);
% Create histograms and/or bar graphs for each variable
histograms_and_bar_graphs(data);
% Create a boxplot (assuming data(:,1) is continuous and data(:,2) is categorical)
boxplot_categorical(data(:, 1), data(:, 2));
% Perform a regression analysis (assuming data(:,1) and data(:,2) are continuous)
regression_analysis(data(:, 1), data(:, 2));
% Discuss the results (replace with your actual analysis)
discussion(data, mean, median, stddev, min_val, max_val);
end
function [mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data)
% Get central and dispersion statistics
mean = mean(data);
median = median(data);
stddev = std(data);
min_val = min(data);
max_val = max(data);
% Return the calculated values
return (mean, median; stddev; min_val; max_val);
end
function histograms_and_bar_graphs(data)
for i = 1:size(data, 2)
if isnumeric(data(:, i))
h = histogram(data(:, i));
title(strcat(‘Histogram of ‘, data(1, i)));
show(h);
else
b = bar(unique(data(:, i)), count(data(:, i)));
title(strcat(‘Bar graph of ‘, data(1, i)));
show(b);
end
end
end
function boxplot_categorical(data_continuous, data_categorical)
% Create a boxplot for continuous data divided by categorical classes
boxplot(data_continuous, data_categorical);
end
function regression_analysis(data1, data2)
% Perform linear regression
[b, a, rsq, pval, se] = regress(data1, data2);
% Print the results
disp(‘Coefficients:’);
disp([b, a]);
disp(‘R-squared:’);
disp(rsq);
disp(‘P-value:’);
disp(pval);
disp(‘Standard error:’);
disp(se);
end
function discussion(~, ~, ~, ~, ~, ~)
% Replace this with your analysis of the data and calculated statistics
disp(‘This is a placeholder for your data analysis discussion.’);
disp(‘Here, you would discuss insights from the central tendency’);
disp(‘(mean, median), dispersion (standard deviation),’);
disp(‘minimum and maximum values, and any relationships found’);
disp(‘between variables using the boxplot and regression analysis.’);
end function load_factors_analysis()
% Load the data (assuming ‘load_factors.csv’ is in the working directory)
data = load(‘load_factors.csv’);
% Get central and dispersion measurements for each variable
[mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data);
% Create histograms and/or bar graphs for each variable
histograms_and_bar_graphs(data);
% Create a boxplot (assuming data(:,1) is continuous and data(:,2) is categorical)
boxplot_categorical(data(:, 1), data(:, 2));
% Perform a regression analysis (assuming data(:,1) and data(:,2) are continuous)
regression_analysis(data(:, 1), data(:, 2));
% Discuss the results (replace with your actual analysis)
discussion(data, mean, median, stddev, min_val, max_val);
end
function [mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data)
% Get central and dispersion statistics
mean = mean(data);
median = median(data);
stddev = std(data);
min_val = min(data);
max_val = max(data);
% Return the calculated values
return (mean, median; stddev; min_val; max_val);
end
function histograms_and_bar_graphs(data)
for i = 1:size(data, 2)
if isnumeric(data(:, i))
h = histogram(data(:, i));
title(strcat(‘Histogram of ‘, data(1, i)));
show(h);
else
b = bar(unique(data(:, i)), count(data(:, i)));
title(strcat(‘Bar graph of ‘, data(1, i)));
show(b);
end
end
end
function boxplot_categorical(data_continuous, data_categorical)
% Create a boxplot for continuous data divided by categorical classes
boxplot(data_continuous, data_categorical);
end
function regression_analysis(data1, data2)
% Perform linear regression
[b, a, rsq, pval, se] = regress(data1, data2);
% Print the results
disp(‘Coefficients:’);
disp([b, a]);
disp(‘R-squared:’);
disp(rsq);
disp(‘P-value:’);
disp(pval);
disp(‘Standard error:’);
disp(se);
end
function discussion(~, ~, ~, ~, ~, ~)
% Replace this with your analysis of the data and calculated statistics
disp(‘This is a placeholder for your data analysis discussion.’);
disp(‘Here, you would discuss insights from the central tendency’);
disp(‘(mean, median), dispersion (standard deviation),’);
disp(‘minimum and maximum values, and any relationships found’);
disp(‘between variables using the boxplot and regression analysis.’);
end statistics, code issues MATLAB Answers — New Questions
