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.');

end

Answer 1

[Voss]

There are a few problems that I see:

1. In MATLAB, return doesn't take arguments, so you don't say

return (mean, median; stddev; min_val; max_val);

You just say

return

and the vaues that are returned from the function (i.e., the output arguments of the function) are defined in the function definition, as you are doing

function [mean, median, stddev, min_val, max_val] = central_dispersion_measurements(data)

% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ outputs

However, in this case, since the return statement is at the end of the function, you don't need it at all. You can remove it entirely.

2. Don't use mean and median as variable names, since they are names of built-in functions that you need to use. Replace those variable names with other names that don't conflict with built-in MATLAB functions, such as mean_data and median_data, for example.

3. The show function is for displaying information about an optimization object, not for making graphics show up or whatever you intended. You don't need to do anything special to make graphics show up, just create them (e.g., using histogram or bar, like you do here). Remove the show lines.

4. For robustness (i.e., correctly handling the case that data has only one row), you should specify that you want to take the mean, median, etc., over the rows of data by specifying dimension 1 in those functions. That is:

mean_data = mean(data,1);

median_data = median(data,1);

stddev = std(data,0,1);

min_val = min(data,[],1);

max_val = max(data,[],1);

There may be other problems. If you run into other problems, upload your data file here (using the paperclip button).

[Ahmed]

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_data, median_data, stddev_data, min_val_data, max_val_data] = 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_data, median_data, stddev_data, min_val_data, max_val_data] = central_dispersion_measurements(data)
% Get central and dispersion statistics
mean_data = mean(data,1);
median_data = median(data,1);
stddev_data = std(data,0,1);
min_val_data = min(data,[],1);
max_val_data = max(data,[],1);

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)));
    else
        b = bar(unique(data(:, i)), count(data(:, i)));
        title(strcat('Bar graph of ', data(1, i)));
    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

[Voss]

Any questions, let me know. Otherwise, please "Accept" this answer. Thanks!

[Ahmed]

hello again,

thank you very much for your support, i'm trying to solve some code issues and gonna run the program now, if i have anything i´ll let you know

[Ahmed]

So i did some tweaks and here is my latest code, also i had one error on line 4

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_data, median_data, stddev, min_val, max_val] = central_dispersion_measurements(data)

% Get central and dispersion statistics
mean_data = mean(data,1);
median_data = median(data,1);
stddev = std(data,0,1);
min_val = min(data,[],1);
max_val = max(data);1;

end

function histograms_and_bar_graphs(data)

for i = 1:size(data, 2)
  if isnumeric(data(:, i))
      histogram(data(:, i));
    title(strcat('Histogram of ', data(1, i)));
   
  else
     bar(unique(data(:, i)), count(data(:, i)));
    title(strcat('Bar graph of ', data(1, i)));
    
  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

[Walter Roberson]

File load_factors.csv is not in the current directory, and is not in any directory on the MATLAB path.

[Ahmed]

Hello Walter,

i've been trying to figure out how to convert .txt file into csv, please tell me how can i do so ,

best regards,

[Walter Roberson]

Converting a text file to csv would only be worthwhile if you need to read the text file repeatedly. Otherwise just pull the data out from the text file.

The difficulty of pulling the data out of the text file depends greatly on the format of the text file, which we have not been told.

[Ahmed]

the text file was taken from this link

https://dasl.datadescription.com/datafile/load_factors_2019/?_sf_s=load+factors&_sfm_cases=4+59943

[Voss]

max_val = max(data);1;

should be

max_val = max(data,[],1);

[Walter Roberson]

data = readmatrix('load_factors_2019.txt'); 

[Ahmed]

Walter , should i put that instead of data = load( 'load_factors.csv' ); ?

Voss, i did but i got code issue , apparently with the []

[Walter Roberson]

Yes, replace

data = load( 'load_factors.csv' );

with

data = readmatrix('load_factors_2019.txt'); 

[Ahmed]

still having an issue with the file

[Voss]

The relevant text file in your MATLAB Drive is called "load_factors_2019 (1).txt", so try this:

data = readmatrix('load_factors_2019 (1).txt');
%                                   ^ don't forget the space!

[Ahmed]

Hello guys ! i woud like to thank you both for the support throughout this journey, the code did work finally , i appreciate it !

[Ahmed]

sorry i couldn't see the histogram, i only got the boxplot as graphs displayed

[Voss]

@Ahmed: The code generates histograms and boxplots when I run it:

data = readmatrix('load_factors_2019.txt'); 

% 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);

Warning: Input should be a string, character array, or cell array of character arrays.

% 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));

Warning: R-square and the F statistic are not well-defined unless X has a column of ones.
Type "help regress" for more information.

Coefficients:
  241.2692  223.5298  259.0086

R-squared:
   1.0e+03 *

   -0.4107
   -0.6520
   -0.8932
    1.7617
    1.5205
    1.2792
    1.0379
    0.7967
    0.5554
    0.3141
    0.0728
   -0.1684
   -0.4097
   -0.6510
   -0.8922
    1.7627
    1.5215
    1.2802
    1.0389
    0.7977
    0.5564
    0.3151
    0.0738
   -0.1674
   -0.4087
   -0.6500
   -0.8912
    1.7637
    1.5225
    1.2812
    1.0399
    0.7987
    0.5574
    0.3161
    0.0748
   -0.1664
   -0.4077
   -0.6490
   -0.8902
    1.7647
    1.5235
    1.2822
    1.0409
    0.7997
    0.5584
    0.3171
    0.0758
   -0.1654
   -0.4067
   -0.6480
   -0.8892
    1.7657
    1.5245
    1.2832
    1.0419
    0.8007
    0.5594
    0.3181
    0.0768
   -0.1644
   -0.4057
   -0.6470
   -0.8882
    1.7667
    1.5255
    1.2842
    1.0429
    0.8017
    0.5604
    0.3191
    0.0778
   -0.1634
   -0.4047
   -0.6460
   -0.8872
    1.7677
    1.5265
    1.2852
    1.0439
    0.8027
    0.5614
    0.3201
    0.0788
   -0.1624
   -0.4037
   -0.6450
   -0.8862
    1.7687
    1.5275
    1.2862
    1.0449
    0.8037
    0.5624
    0.3211
    0.0798
   -0.1614
   -0.4027
   -0.6440
   -0.8852
    1.7697
    1.5285
    1.2872
    1.0459
    0.8047
    0.5634
    0.3221
    0.0808
   -0.1604
   -0.4017
   -0.6430
   -0.8842
    1.7707
    1.5295
    1.2882
    1.0469
    0.8057
    0.5644
    0.3231
    0.0818
   -0.1594
   -0.4007
   -0.6420
   -0.8832
    1.7717
    1.5305
    1.2892
    1.0479
    0.8067
    0.5654
    0.3241
    0.0828
   -0.1584
   -0.3997
   -0.6410
   -0.8822
    1.7727
    1.5315
    1.2902
    1.0489
    0.8077
    0.5664
    0.3251
    0.0838
   -0.1574
   -0.3987
   -0.6400
   -0.8812
    1.7737
    1.5325
    1.2912
    1.0499
    0.8087
    0.5674
    0.3261
    0.0848
   -0.1564
   -0.3977
   -0.6390
   -0.8802
    1.7747
    1.5335
    1.2922
    1.0509
    0.8097
    0.5684
    0.3271
    0.0858
   -0.1554
   -0.3967
   -0.6380
   -0.8792
    1.7757
    1.5345
    1.2932
    1.0519
    0.8107
    0.5694
    0.3281
    0.0868
   -0.1544
   -0.3957
   -0.6370
   -0.8782
    1.7767
    1.5355
    1.2942
    1.0529
    0.8117
    0.5704
    0.3291
    0.0878
   -0.1534
   -0.3947
   -0.6360
   -0.8772
    1.7777
    1.5365
    1.2952
    1.0539
    0.8127
    0.5714
    0.3301
    0.0888
   -0.1524

P-value:
   1.0e+03 *

   -2.2708    1.4494
   -2.5089    1.2050
   -2.7463    0.9598
   -0.0915    3.6150
   -0.3367    3.3776
   -0.5810    3.1394
   -0.8246    2.9004
   -1.0674    2.6607
   -1.3094    2.4202
   -1.5506    2.1789
   -1.7911    1.9368
   -2.0308    1.6940
   -2.2698    1.4504
   -2.5079    1.2060
   -2.7453    0.9608
   -0.0905    3.6160
   -0.3357    3.3786
   -0.5800    3.1404
   -0.8236    2.9014
   -1.0664    2.6617
   -1.3084    2.4212
   -1.5496    2.1799
   -1.7901    1.9378
   -2.0298    1.6950
   -2.2688    1.4514
   -2.5069    1.2070
   -2.7443    0.9618
   -0.0895    3.6170
   -0.3346    3.3796
   -0.5790    3.1414
   -0.8226    2.9024
   -1.0654    2.6627
   -1.3074    2.4221
   -1.5486    2.1809
   -1.7891    1.9388
   -2.0288    1.6960
   -2.2678    1.4524
   -2.5059    1.2080
   -2.7433    0.9628
   -0.0885    3.6180
   -0.3336    3.3805
   -0.5780    3.1424
   -0.8215    2.9034
   -1.0643    2.6637
   -1.3064    2.4231
   -1.5476    2.1819
   -1.7881    1.9398
   -2.0278    1.6970
   -2.2668    1.4534
   -2.5049    1.2090
   -2.7423    0.9639
   -0.0875    3.6189
   -0.3326    3.3815
   -0.5770    3.1433
   -0.8205    2.9044
   -1.0633    2.6646
   -1.3054    2.4241
   -1.5466    2.1829
   -1.7871    1.9408
   -2.0268    1.6980
   -2.2658    1.4544
   -2.5039    1.2100
   -2.7413    0.9649
   -0.0865    3.6199
   -0.3316    3.3825
   -0.5759    3.1443
   -0.8195    2.9054
   -1.0623    2.6656
   -1.3044    2.4251
   -1.5456    2.1839
   -1.7861    1.9418
   -2.0258    1.6990
   -2.2648    1.4554
   -2.5029    1.2110
   -2.7403    0.9659
   -0.0854    3.6209
   -0.3306    3.3835
   -0.5749    3.1453
   -0.8185    2.9064
   -1.0613    2.6666
   -1.3034    2.4261
   -1.5446    2.1848
   -1.7851    1.9428
   -2.0248    1.7000
   -2.2638    1.4564
   -2.5020    1.2120
   -2.7393    0.9669
   -0.0844    3.6219
   -0.3296    3.3845
   -0.5739    3.1463
   -0.8175    2.9073
   -1.0603    2.6676
   -1.3023    2.4271
   -1.5436    2.1858
   -1.7841    1.9438
   -2.0238    1.7010
   -2.2628    1.4574
   -2.5010    1.2130
   -2.7384    0.9679
   -0.0834    3.6229
   -0.3285    3.3855
   -0.5729    3.1473
   -0.8165    2.9083
   -1.0593    2.6686
   -1.3013    2.4281
   -1.5426    2.1868
   -1.7831    1.9448
   -2.0228    1.7020
   -2.2618    1.4584
   -2.5000    1.2140
   -2.7374    0.9689
   -0.0824    3.6238
   -0.3275    3.3865
   -0.5719    3.1483
   -0.8155    2.9093
   -1.0583    2.6696
   -1.3003    2.4291
   -1.5416    2.1878
   -1.7821    1.9458
   -2.0218    1.7030
   -2.2608    1.4594
   -2.4990    1.2151
   -2.7364    0.9699
   -0.0814    3.6248
   -0.3265    3.3874
   -0.5709    3.1493
   -0.8145    2.9103
   -1.0573    2.6706
   -1.2993    2.4301
   -1.5406    2.1888
   -1.7811    1.9468
   -2.0208    1.7040
   -2.2598    1.4604
   -2.4980    1.2161
   -2.7354    0.9709
   -0.0803    3.6258
   -0.3255    3.3884
   -0.5699    3.1503
   -0.8135    2.9113
   -1.0563    2.6716
   -1.2983    2.4311
   -1.5396    2.1898
   -1.7801    1.9478
   -2.0198    1.7050
   -2.2588    1.4614
   -2.4970    1.2171
   -2.7344    0.9719
   -0.0793    3.6268
   -0.3245    3.3894
   -0.5689    3.1512
   -0.8124    2.9123
   -1.0553    2.6726
   -1.2973    2.4321
   -1.5386    2.1908
   -1.7791    1.9488
   -2.0188    1.7060
   -2.2578    1.4624
   -2.4960    1.2181
   -2.7334    0.9729
   -0.0783    3.6278
   -0.3235    3.3904
   -0.5678    3.1522
   -0.8114    2.9133
   -1.0543    2.6736
   -1.2963    2.4331
   -1.5376    2.1918
   -1.7781    1.9498
   -2.0178    1.7070
   -2.2568    1.4634
   -2.4950    1.2191
   -2.7324    0.9740
   -0.0773    3.6288
   -0.3224    3.3914
   -0.5668    3.1532
   -0.8104    2.9143
   -1.0533    2.6746
   -1.2953    2.4341
   -1.5366    2.1928
   -1.7771    1.9508
   -2.0168    1.7080
   -2.2558    1.4644
   -2.4940    1.2201
   -2.7314    0.9750
   -0.0763    3.6297
   -0.3214    3.3924
   -0.5658    3.1542
   -0.8094    2.9153
   -1.0522    2.6756
   -1.2943    2.4351
   -1.5356    2.1938
   -1.7761    1.9518
   -2.0158    1.7090
   -2.2548    1.4654
   -2.4930    1.2211
   -2.7304    0.9760
   -0.0753    3.6307
   -0.3204    3.3933
   -0.5648    3.1552
   -0.8084    2.9163
   -1.0512    2.6765
   -1.2933    2.4361
   -1.5346    2.1948
   -1.7751    1.9528
   -2.0149    1.7100

Standard error:
   1.0e+05 *

   -0.3680       NaN       NaN    8.9444

% Discuss the results (replace with your actual analysis)
discussion(data, mean, median, stddev, min_val, max_val);

This is a placeholder for your data analysis discussion.
Here, you would discuss insights from the central tendency
(mean, median), dispersion (standard deviation),
minimum and maximum values, and any relationships found
between variables using the boxplot and regression analysis.

function [mean_data, median_data, stddev, min_val, max_val] = central_dispersion_measurements(data)

% Get central and dispersion statistics
mean_data = mean(data,1);
median_data = median(data,1);
stddev = std(data,0,1);
min_val = min(data,[],1);
max_val = max(data,[],1);

end

function histograms_and_bar_graphs(data)

for i = 1:size(data, 2)
  if isnumeric(data(:, i))
      histogram(data(:, i));
    title(strcat('Histogram of ', data(1, i)));
   
  else
     bar(unique(data(:, i)), count(data(:, i)));
    title(strcat('Bar graph of ', data(1, i)));
    
  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