it shows me Check plots - "low income" - top plot, xdata Variable x must be of size [1 866]. It is currently of size [0 0]. Check where the variable is assigned a value

function [corrcoef1, corrcoef2] = analyze_it(opt)
    load('CookCountyFoodInsecurity.mat');
    
    % Divide povertyRate by 100
    povertyRate = povertyRate / 100;
    
    % Assign the appropriate food desert variable based on opt value
    if strcmp(opt, 'low income')
        foodDesertVariable = lowIncomeFoodDesert;
    elseif strcmp(opt, 'black or african american')
        foodDesertVariable = blackFoodDesert;
    elseif strcmp(opt, 'white')
        foodDesertVariable = whiteFoodDesert;
    elseif strcmp(opt, 'asian')
        foodDesertVariable = asianFoodDesert;
    elseif strcmp(opt, 'hispanic')
        foodDesertVariable = hispanicFoodDesert;
    else
        error('Invalid opt value. Use one of: low income, black or african american, white, asian, hispanic');
    end
    
    % Calculate the percentage of population >0.5 miles away from a supermarket and the chosen demographic
    percentage_foodDesert = (foodDesertVariable ./ population) * 100;
    
    % Calculate the percentage of population without access to a vehicle
    percentage_withoutCars = (withoutCars ./ population) * 100;
    
    % Plotting
    figure;
    
    % Subplot 1
    subplot(2,1,1);
    scatter(povertyRate, percentage_foodDesert);
    xlabel('Poverty Rate');
    ylabel(['Percentage ' opt ' Food Desert']);
    title('Correlation Coefficient: To be Calculated');
    
    % Subplot 2
    subplot(2,1,2);
    scatter(percentage_foodDesert, percentage_withoutCars);
    xlabel(['Percentage ' opt ' Food Desert']);
    ylabel('Percentage Without Cars');
    title('Correlation Coefficient: To be Calculated');
    
    % Calculate correlation coefficients
    corrcoef1 = corrcoef_mine(povertyRate, percentage_foodDesert);
    corrcoef2 = corrcoef_mine(percentage_foodDesert, percentage_withoutCars);
    
    % Add correlation coefficients to titles
    title(['Correlation Coefficient: ' num2str(corrcoef1)], 'Parent', subplot(2,1,1));
    title(['Correlation Coefficient: ' num2str(corrcoef2)], 'Parent', subplot(2,1,2));
end

function [corr_coef_xy] = corrcoef_mine(x, y)
    n = length(x);
    mean_x = sum(x) / n;
    mean_y = sum(y) / n;
    
    numerator = 0;
    denominator_x = 0;
    denominator_y = 0;
    
    for i = 1:n
        numerator = numerator + (x(i) - mean_x) * (y(i) - mean_y);
        denominator_x = denominator_x + (x(i) - mean_x)^2;
        denominator_y = denominator_y + (y(i) - mean_y)^2;
    end
    
    corr_coef_xy = numerator / (sqrt(denominator_x) * sqrt(denominator_y));
end

[Voss]

Please upload the mat file (CookCountyFoodInsecurity.mat) using the paperclip button, and we also need to know the value of opt you are using when you run analyze_it.

[ronit]

When i load the file it shows code ran without output

[Voss]

Can you attach the mat-file?

[ronit]

CookCountyFoodIsecurity.mat is the mat file

[Walter Roberson]

On your system please give the command

whos -file CookCountyFoodIsecurity.mat

and show us the output.

[Voss]

@ronit: We need the same mat file you are using, in order to run the code with the same data you have. Please upload the mat file or direct us to the webpage where you got it.

Or we at least need to know what kind of variables (class and size) the mat file contains - this is what the following command tells us:

whos -file CookCountyFoodInsecurity.mat

  Name                     Size            Bytes  Class    Attributes

  lowIncomeFoodDesert      1x2                 4  char               
  population               1x1                 2  char               
  povertyRate              1x2                 4  char               
  withoutCars              1x2                 4  char               

As an illustration of why the data matters, here I'll run the code as-is using a mat file I made up (contents shown above). The code runs without error with this mat file.

[cc1,cc2] = analyze_it('low income')

cc1 = 1

cc2 = -1