Kikokotoo cha Kiwango cha Kiwango cha Kawaida (SDI) ili kutathmini usahihi wa matokeo ya mtihani kulingana na wastani wa udhibiti. Muhimu kwa uchambuzi wa takwimu na udhibiti wa ubora wa maabara.
Hesabu Mchanganyiko wa Kawaida (SDI) ili kutathmini usahihi wa matokeo yako ya mtihani.
Kihesabu cha Kiwango cha Kawaida (SDI) ni chombo cha takwimu kinachotumika kutathmini usahihi na usahihi wa matokeo ya mtihani kulinganisha na wastani wa kundi la udhibiti au kundi la rika. Kinakadiria idadi ya viwango vya kawaida ambavyo matokeo ya mtihani yapo mbali na wastani wa udhibiti, na kutoa mwanga muhimu kuhusu utendaji wa mbinu za uchambuzi katika mazingira ya maabara na mazingira mengine ya upimaji.
SDI inakadiria kwa kutumia fomula ifuatayo:
Ambapo:
Ili kuhesabu SDI:
Kiwango:
Hesabu:
SDI ya 1.0 inaashiria kwamba matokeo ya mtihani yako kiwango kimoja cha kawaida juu ya wastani wa udhibiti.
SDI kati ya -1 na +1: Utendaji wa Kukubalika.
Matokeo ya mtihani yako ndani ya kiwango kimoja cha kawaida cha wastani wa udhibiti, kuashiria ulinganifu mzuri na thamani zinazotarajiwa. Hakuna hatua inayohitajika kawaida.
SDI kati ya -2 na -1 au kati ya +1 na +2: Kiwango cha Onyo.
Matokeo ni ya kukubalika lakini yanapaswa kufuatiliwa. Kiwango hiki kinapendekeza tofauti inayoweza kutokea kutoka kwa kawaida ambayo inaweza kuhitaji umakini. Chunguza sababu zinazoweza na fikiria upimaji tena.
SDI chini ya -2 au zaidi ya +2: Utendaji Usio Kubalika.
Uchunguzi unahitajika kubaini na kurekebisha matatizo. Matokeo katika kiwango hiki yanaonyesha tofauti kubwa kutoka kwa thamani zinazotarajiwa na yanaweza kuashiria matatizo ya mfumo katika mchakato wa upimaji au vifaa. Hatua za haraka za kurekebisha zinapendekezwa.
Katika maabara za kliniki, SDI ni muhimu kwa:
Viwanda vinatumia SDI ili:
Wanasayansi hutumia SDI ili:
Dhana ya Kihesabu cha Kiwango cha Kawaida ilitokana na hitaji la mbinu zilizowekwa za kutathmini utendaji wa maabara. Kwa kuanzishwa kwa programu za upimaji wa ufanisi katikati ya karne ya 20, maabara zilihitaji vipimo vya kiasi ili kulinganisha matokeo. SDI ikawa chombo cha msingi, ikitoa njia rahisi ya kutathmini usahihi kulinganisha na data ya kundi la rika.
Watu mashuhuri katika takwimu, kama Ronald Fisher na Walter Shewhart, walichangia katika maendeleo ya mbinu za udhibiti wa ubora wa takwimu ambazo zinaunda msingi wa matumizi ya viashiria kama SDI. Kazi zao zililenga msingi wa mbinu za uhakikisho wa ubora wa kisasa katika sekta mbalimbali.
1' Hesabu SDI katika Excel
2' Kadiria Matokeo ya Mtihani katika seli A2, Wastani wa Udhibiti katika B2, Kiwango cha Kawaida katika C2
3= (A2 - B2) / C2
4
1def calculate_sdi(test_result, control_mean, standard_deviation):
2 return (test_result - control_mean) / standard_deviation
3
4## Mfano wa matumizi
5test_result = 102
6control_mean = 100
7standard_deviation = 2
8
9sdi = calculate_sdi(test_result, control_mean, standard_deviation)
10print(f"SDI: {sdi}")
11
1calculate_sdi <- function(test_result, control_mean, standard_deviation) {
2 (test_result - control_mean) / standard_deviation
3}
4
5## Mfano wa matumizi
6test_result <- 102
7control_mean <- 100
8standard_deviation <- 2
9
10sdi <- calculate_sdi(test_result, control_mean, standard_deviation)
11cat("SDI:", sdi, "\n")
12
1% Hesabu SDI katika MATLAB
2test_result = 102;
3control_mean = 100;
4standard_deviation = 2;
5
6sdi = (test_result - control_mean) / standard_deviation;
7disp(['SDI: ', num2str(sdi)]);
8
1function calculateSDI(testResult, controlMean, standardDeviation) {
2 return (testResult - controlMean) / standardDeviation;
3}
4
5// Mfano wa matumizi
6const testResult = 102;
7const controlMean = 100;
8const standardDeviation = 2;
9
10const sdi = calculateSDI(testResult, controlMean, standardDeviation);
11console.log(`SDI: ${sdi}`);
12
1public class SDICalculator {
2 public static void main(String[] args) {
3 double testResult = 102;
4 double controlMean = 100;
5 double standardDeviation = 2;
6
7 double sdi = (testResult - controlMean) / standardDeviation;
8 System.out.println("SDI: " + sdi);
9 }
10}
11
1#include <iostream>
2
3int main() {
4 double testResult = 102;
5 double controlMean = 100;
6 double standardDeviation = 2;
7
8 double sdi = (testResult - controlMean) / standardDeviation;
9 std::cout << "SDI: " << sdi << std::endl;
10
11 return 0;
12}
13
1using System;
2
3class Program
4{
5 static void Main()
6 {
7 double testResult = 102;
8 double controlMean = 100;
9 double standardDeviation = 2;
10
11 double sdi = (testResult - controlMean) / standardDeviation;
12 Console.WriteLine("SDI: " + sdi);
13 }
14}
15
1<?php
2$testResult = 102;
3$controlMean = 100;
4$standardDeviation = 2;
5
6$sdi = ($testResult - $controlMean) / $standardDeviation;
7echo "SDI: " . $sdi;
8?>
9
1test_result = 102
2control_mean = 100
3standard_deviation = 2
4
5sdi = (test_result - control_mean) / standard_deviation
6puts "SDI: #{sdi}"
7
1package main
2
3import "fmt"
4
5func main() {
6 testResult := 102.0
7 controlMean := 100.0
8 standardDeviation := 2.0
9
10 sdi := (testResult - controlMean) / standardDeviation
11 fmt.Printf("SDI: %.2f\n", sdi)
12}
13
1let testResult = 102.0
2let controlMean = 100.0
3let standardDeviation = 2.0
4
5let sdi = (testResult - controlMean) / standardDeviation
6print("SDI: \(sdi)")
7
Mchoro wa SVG unaoonyesha SDI na maeneo yake ya tafsiri.