Kalkulator tes friedman
Uji Friedman merupakan alternatif nonparametrik dari ANOVA satu arah dengan pengukuran berulang. Ini digunakan untuk menguji perbedaan antar kelompok ketika variabel terikatnya ordinal.
Untuk melakukan uji Friedman pada kumpulan data tertentu, cukup masukkan nilai hingga lima sampel pada sel di bawah, lalu tekan tombol “Hitung”.
Kalkulator akan menampilkan statistik uji Q, nilai p uji, dan perhitungan yang digunakan untuk memperoleh statistik uji Q.
Grup 1 | Grup 2 | Kelompok 3 | Kelompok 4 | Grup 5 |
---|---|---|---|---|
Statistik uji Q:
nilai p:
Larutan
Q =
Q =
Q =
//create function that performs calculations function calc() {
//define addition function function add(a, b) { return a + b; }
//define function that can flatten a multi-dimensional array function flatten(arr) { return arr.reduce(function (flat, toFlatten) { return flat.concat(Array.isArray(toFlatten) ? flatten(toFlatten) : toFlatten); }, []); }
//get total number of rows var row_input = document.getElementsByClassName('table_span_a'); var row_array = []; for (var i = 0; i < row_input.length; i++) { row_array[i] = row_input[i].innerText; } values_row = row_array.filter(n => n); var n = values_row.length;
//create massive for loop that gets ranks for every single row all_data = [];
for (var i = 0; i < n; i++) { var p = "p" var p_number = i-(-1); var str = p.concat(p_number); str = str.replace(/ +/g, ""); //get row data var p1_input = document.getElementsByClassName(str); var p1_array = []; for (var j = 0; j < p1_input.length; j++) { p1_array[j] = p1_input[j].innerText; } values_p1 = p1_array.filter(n => n); var p1 = values_p1.map(Number);
//sort row data var sorted = p1.slice().sort(function(a,b){return a-b}) var reversed = sorted.slice(0).reverse(); var frac_rank = p1.slice().map(function(n) { return ( (sorted.indexOf(n) + 1) + (reversed.length - reversed.indexOf(n)) ) / 2 });
//push sorted row data to total data array all_data.push(frac_rank);
} //end massive for loop that ranks every row
//find total treatments var k = all_data[0].length;
//flatten multi-dimensional array into one long array flat_data = flatten(all_data);
//find sum of ranks for each treatment var a = [], b = [], c = [], d = [], e = [], total_squared_ranks;
if (k == 2) { for (i = 0; i < flat_data.length; i+= k) { a.push(flat_data[i]); } for (i = 1; i < flat_data.length; i+= k) { b.push(flat_data[i]); } var sum_a_squared = Math.pow(a.reduce(add, 0), 2); var sum_b_squared = Math.pow(b.reduce(add, 0), 2); total_squared_ranks = [sum_a_squared, sum_b_squared].reduce(add, 0); } if (k == 3) { for (i = 0; i < flat_data.length; i+= k) { a.push(flat_data[i]); } for (i = 1; i < flat_data.length; i+= k) { b.push(flat_data[i]); } for (i = 2; i < flat_data.length; i+= k) { c.push(flat_data[i]); } var sum_a_squared = Math.pow(a.reduce(add, 0), 2); var sum_b_squared = Math.pow(b.reduce(add, 0), 2); var sum_c_squared = Math.pow(c.reduce(add, 0), 2); total_squared_ranks = [sum_a_squared, sum_b_squared, sum_c_squared].reduce(add, 0); } if (k == 4) { for (i = 0; i < flat_data.length; i+= k) { a.push(flat_data[i]); } for (i = 1; i < flat_data.length; i+= k) { b.push(flat_data[i]); } for (i = 2; i < flat_data.length; i+= k) { c.push(flat_data[i]); } for (i = 3; i < flat_data.length; i+= k) { d.push(flat_data[i]); } var sum_a_squared = Math.pow(a.reduce(add, 0), 2); var sum_b_squared = Math.pow(b.reduce(add, 0), 2); var sum_c_squared = Math.pow(c.reduce(add, 0), 2); var sum_d_squared = Math.pow(d.reduce(add, 0), 2); total_squared_ranks = [sum_a_squared, sum_b_squared, sum_c_squared, sum_d_squared].reduce(add, 0); } if (k == 5) { for (i = 0; i < flat_data.length; i+= k) { a.push(flat_data[i]); } for (i = 1; i < flat_data.length; i+= k) { b.push(flat_data[i]); } for (i = 2; i < flat_data.length; i+= k) { c.push(flat_data[i]); } for (i = 3; i < flat_data.length; i+= k) { d.push(flat_data[i]); } for (i = 4; i < flat_data.length; i+= k) { e.push(flat_data[i]); } var sum_a_squared = Math.pow(a.reduce(add, 0), 2); var sum_b_squared = Math.pow(b.reduce(add, 0), 2); var sum_c_squared = Math.pow(c.reduce(add, 0), 2); var sum_d_squared = Math.pow(d.reduce(add, 0), 2); var sum_e_squared = Math.pow(e.reduce(add, 0), 2); total_squared_ranks = [sum_a_squared, sum_b_squared, sum_c_squared, sum_d_squared, sum_e_squared].reduce(add, 0); } //final calculations for critical value and p value q_term1 = 12 / (n*k*(k-(-1))); q_term3 = 3*n*(k-(-1)); q = (q_term1 * total_squared_ranks) - q_term3; p = 1 - jStat.chisquare.cdf(q, k-1); //output results document.getElementById('q').innerHTML = q.toFixed(5); document.getElementById('p').innerHTML = p.toFixed(5); document.getElementById('solution1').innerHTML = "(12/(nk(k+1)) * (∑R2) - 3n(k+1)"; document.getElementById('solution2').innerHTML = q_term1 + " * " + total_squared_ranks.toFixed(1) + " - " + q_term3; document.getElementById('solution3').innerHTML = q.toFixed(5); } //end calc() function