---
title: "QAQC v2.1.1 PowerBI Dashboard"
canonical: "https://servicedesk.maxgeo.com/space/DS/2265612289/QAQC%20v2.1.1%20PowerBI%20Dashboard"
format: markdown
---
*This guide outlines key features of the QAQC Dashboard for lab data quality control. It explains using global filters, reviewing batch and sample results, and interpreting statistical plots to monitor performance and meet quality standards. Following it helps users strengthen data analysis skills, boost lab efficiency, and spot potential testing issues.*


> Macro (toc)

![QAQC Dashboard - DQ - Step 0.png](media://604e45aa-ef42-4b9a-acdf-9cc00edb65e7)

To open the QAQC Dashboard, click on **Dashboards**, **General Reports**, then **QAQC v2.1.1**

![QAQC Dashboard - DQ - Step 1.png](media://1c12f6fb-1e8f-4129-9271-e54deddad35e)


**QAQC v2.1.1** has a live connection to the DataShed MDS. This is demonstrated on the **Help** page as shown below.

![QAQC Dashboard v2.1.1 - Step 2.png](media://20d6f751-1417-4330-85d9-4b86c26ed3dd)


> 📝 **Note:** While the Direct Query version of QAQC provides live access to your data, there may be a performance impact with charts and tables taking longer to load.

## Global Filters

In order to utilise the QAQC Dashboard, start by setting Global Filters.

Click the **Global Filters** button to set these to the desired values.  
Step 1: Select a **Project**  
Step 2: Select a **DataSet**  
Step 3: Select a **Source Type**  
Step 4: Select **Lab Code**/s  
Step 5: Set the **Date Range**  
Step 6: Select a **Batch** or **Batches**  
If desired, the "**Related Batches Processing**" can include or exclude related batches in Repeat analyses.  
Then click "**Start Analysis**" to close the **Global Filters** screen.

![QAQC Dashboard - DQ - Step 5 (1).webp](media://fa8cde61-adee-4e16-9a3d-fdfa7ce00e9d)


## QAQC Summary

The **QAQC Summary** page – Total number of Batches and Samples for each section of the Report.

**Repeats** - Indicates how many sample batches were repeated for verification.  
**CRM** (Certified Reference Materials) - Used to ensure accuracy and calibration of testing methods.  
**Lab Turnaround** - Reflects the volume of samples processed and the efficiency of lab operations.  
**Screen Test** - Particle size distribution analysis.  
**Weights** - Monitors sample weight consistency across batches and labs.

![QAQC Dashboard v2.1.1 - Step 7.png](media://46209ab7-6238-4ac0-8802-40b1d64f35dc)


The **QAQC Summary (Ratio)** screen helps:

- Compare lab performance and sample throughput.
- Monitor the use and distribution of standards.
- Evaluate the effectiveness and coverage of QC categories.

**Laboratory Summary** - displays Batch count, Sample QC and Standards counts for each selected Laboratory.  
**Standard Type Ratio** - Shows how different standard types are distributed across labs, with calculated ratios.  
**QC Category Ratios** - Shows how QC samples are categorized and their ratios.

![QAQC Dashboard v2.1.1 - Step 10.png](media://92fb0343-2595-4718-b166-7d57001deed4)


The **Batch Summary Data** screen focuses on Total Batches and Samples, providing a detailed view of sample and result merging status across multiple batches and selected laboratories.

![QAQC Dashboard v2.1.1 - Step 12.png](media://39547084-cd87-4f30-b7c8-a9817dad8942)


## QC Results (CRMs)

The image below is a **QC Results Summary** Dashboard for Certified Reference Materials (CRMs), designed to monitor the quality and performance of standards used in laboratory testing.

- Monitor the performance of CRMs across different labs and batches.
- Identify standards that frequently trigger alerts or actions.
- Ensure compliance with quality thresholds.

![QAQC Dashboard - DQ - Step 20.png](media://2b4f00d9-d814-4845-8f00-2ff8980b1b87)

Additional filtering can be made on: 

- Elements,
- Standard Types,
- Batch_No and
- Lab Code.  
The Total number of CRM samples analysed and the ratio of CRM samples to regular samples, indicating QAQC coverage.  
The Summary table provides a breakdown of CRM performance by standard type.  
Click the "**Blank**" button to show the same break down for Blanks.  
The QC Results (Repeats) screen is focused on Repeats, which are used to assess the consistency and reliability of laboratory results.
- Monitor repeat sample performance across different comparison types and QC categories.
- Identify potential inconsistencies or areas needing further investigation.
- Ensure lab processes are producing reliable and repeatable results.  
Additional filter by Sample Type,  
Batch No or  
Lab Code.

![QAQC Dashboard - DQ - Step 21 (1).webp](media://3d6f0daf-eca0-4464-830e-4f8c35127109)


## Repeats

How we calculate Repeats Test:  
**Alert** : ABS( OriginalResult - RepeatResult ) >= 0.1 * OriginalResult.  
**OK** : Rest of Results.

![QAQC Dashboard - DQ - Step 23.png](media://4d169318-14e6-42d9-b654-29ed0a6bdcea)

## Repeats by Sample Name

This comparison is used when comparing samples with different names. These are usually repeats initiated by the client (for example Field Duplicates, Resamples splits and Umpire samples); and those performed by the analysing laboratory (for example Lab Checks, Lab Duplicates, Lab Second Splits an Coarse Rejects), – these are typically regular checks (e.g. 1 in 20 or 25) typically scheduled by both the client and the laboratory. The relationship between the original sample and the repeat sample, which has a different sample name, is stored in one of the sample QC tables such as tblDHSampQC.

**Section Number 1**

Sample Table  
Using this filter you switch between different type of Samples: DrillHole, DrillHole QC, Linear, etc. Only one type can be selected at the time.

**Section Number 2**

Element.  
Here you switch between different Elements (only one element can be selected at the time).  
Repeats by Test.  
Here you can choose to display only those samples that passed the test.  
How we calculate the TEST you can see in here.  
QC_Category.  
Here you can pick one or multiple QC Categories – separating sample categories is recommended.

**Section Number 3**

- DataSet
- Hole / Site ID  
Depends on the Sample Table selection, here you can display only repeats for specific Hole or Site ID.
- Batch_No  
Display Samples for specific Batch_No.
- LabCode  
Display Samples for specific LabCode.

**Section Number 4**

Advanced Filters

![QAQC Dashboard - DQ - Step 25.png](media://96ba5d12-2fd5-4cdf-afb3-37541a683c97)

**Advanced Filters**

**Match Method**  
This filter will sync across all pages under Repeats (excluding “by Method” and “by Element Name”)

**Additional Filter Options**  
Sample_Category, Sample_Method and Sample_Type have been added in this release.

**Processing Values**  
If you do not want to process negative values (below detection if these have been stored as negatives, or default values for missing samples, samples destroyed in preparation, for example), please activate the filter.  
This filter will sync across all pages under Repeats (excluding “by Element Name”)

![QAQC Dashboard v2.1.1 - Step 26.png](media://73aefdbb-cb93-47ea-81ae-b1845094349b)


Click the **Filter** button to open or close Advanced Filters.

![QAQC Dashboard v2.1.1 - Step 27.png](media://138e1596-a5e4-4e92-8c0a-aac091b5bb9c)


## Scatter Plot

**Scatter plots** display original assay data against any number of types of repeat assay data. Original values are plotted against the X axis and the repeat assay values against the Y axis.

If no bias exists between the two sets of data, they will plot about the line of y = x, a 45-degree line passing through the origin. A scatter plot may show that a relationship exists, but it does not and cannot prove that one variable is causing the other. Both variables could be related to some third variable, or pure coincidence might cause an apparent correlation.

![QAQC Dashboard - DQ - Step 31 (1).png](media://07b999dd-f065-4fe9-a3eb-3321d2bb90bb)

The Scatter Plot can be displayed by:

- DataSet
- LabCode
- QC Category (default)
- Sample Category
- Sample Method
- Sample Type
- Test

![QAQC Dashboard v2.1.1 - Step 30.png](media://89630d04-c3a4-4d10-bce3-2b22122cd71a)



The **Scatter Plot** can be viewed as three different styles:

**Combined (symlog)** - Symmetric log scales.  
**Combined** – Linear scale.  
**By Selection**, where you can choose between LabCode, QC Category and TEST.  
x and y axis on these charts are independent.

![QAQC Dashboard - DQ - Step 33 (1).webp](media://13ab4d36-5ebf-4475-8872-4f8bc35f713e)


## Q-Q - Quantile-Quantile Plots

**Q-Q Plots** are a good way to compare the distributions between two populations and are a very efficient way of highlighting bias in a duplicate data. **Q-Q plots** are generated by sorting the original and repeat results in ascending order and calculating the percentile for each then the paired percentiles are plotted against one another. If the two distributions being compared are identical, the **Q-Q plot** follows the line y=x; if the general trend of the **Q-Q plot** is flatter than the line y=x, the distribution of the Original assay results is more dispersed than the Repeats, and vice versa. Departure from the ideal is often more exaggerated at higher grades. This is less of an issue than a consistent small difference throughout a data range. **Q-Q Plots** are scale sensitive and should be examined at different scales to ensure that bias at low grades is not masked.

It is recommended that **Q-Q plots** should be interpreted using a single population. For instance, field duplicates should be assessed separately to Lab Check QC samples.

![QAQC Dashboard - DQ - Step 35 (1).png](media://26586325-ffde-4c0d-a2e1-ddf6fcf87894)


## MAPD / HARD Plot

This can provide a simple way of checking if the repeatability for a selected grade range and a selected set of batches meets your threshold requirement.  
This chart plots the mean absolute paired difference values for sets of repeats as a percentile chart. It is calculated for a duplicate pair A and B as difference over average expressed as a percentile.

This analysis of repeats shows the Mean absolute paired difference %, shown in the equation:

**(%MAPD)**  
**= 100 * | [(Absolute pair difference) / 2] | / [pair mean]**

**= 100 * | abs(a-b)/2 | / [(a+b)/2]**

where:

**a** = original result

**b** = repeat result

![QAQC Dashboard - DQ - Step 37.png](media://57d6bc0c-cbcc-475d-a86b-ec80d712c984)

## HT – Horwitz Trumpet

This chart can be used to help identify repeat values that are outside acceptable limits. The limit should be entered into the Warning (%) box before running the chart. As with the AVRD plots, high relative difference could indicate poor sampling practice, poor assaying or a high inherent nugget effect in the mineralisation.

This chart plots the percentage difference between the repeat value and the mean of the original-repeat pair at increasing concentrations.

The following formula is used:​

![QAQC Dashboard - DQ - Step 39.png](media://10d1b7ab-00dc-452f-b3cd-ce0ae5005d9e)

![QAQC Dashboard - DQ - Step 40.png](media://54ecb2fd-b9b1-41c2-bfe4-194c7d4b4a05)

## AVRD - Average Relative Difference

This plot allows you to plot and compare the precision across the selected data of multiple repeat types on the same chart. In the example below, you can easily see that lab pulp checks produced overall smaller **AVRD** values (hence greater precision) than Umpire lab samples. High relative difference could indicate poor sampling practise, poor assaying or a high inherent nugget effect in the mineralisation.

This analysis of repeats of the **Average Relative Difference (AVRD)** uses a percentile plot showing the absolute difference divided by the pair mean.

The **AVRD** values are sorted in ascending order and plotted from 0 to 1.0 (100%) on the X Axis, allowing you to see the distribution of **AVRD** values through the selected data.

The following equation is used:

**(AVRD) = | Absolute pair difference | / [pair mean] = | abs(a-b) | / [(a+b)/2]**

where:

**a** = original result

**b** = repeat result

This plot is similar to the %MAPD Plot.

![QAQC Dashboard - DQ - Step 42.png](media://7d866418-915a-4728-9b66-523ab05408ce)

## TH – Thompson-Howarth Plot

**Thompson-Howarth plots** are a good way to show precision over the concentration range of the samples being assayed. However, the large-sample method defined by **Thompson and Howarth** relies on an assumption that the measurement errors are normally distributed; so it produces significantly biased results when the errors are not normally distributed. Normally distributed errors are probably the exception rather than the rule in ore deposits, so using the **TH** approach may provide a significantly inaccurate estimate of the quality of their geochemical concentration data.

To generate a meaningful interpretation of the data it is recommended that a **TH plot** should be generated on single populations which have more than 50 samples.

Each blue dot represents an original-repeat pair. The Y-axis plots the absolute difference between the original and the repeat, and the X-axis shows the mean of the 2 results. The pairs are sorted by increasing mean value then grouped into sets of 11 pairs, plotted as red diamonds. For the grouped points, the Y axis is the median of the differences for the group of 11 and the X axis is the mean of the means for the group of 11 pairs. The red line is a line of best fit (linear regression) for the grouped (red) points.

![QAQC Dashboard - DQ - Step 44.png](media://bfe2a1e4-c584-403a-9d28-9b077c157384)

## Statistics

The **Statistics** Section is divided into two sections.

Left section with overall descriptive and bivariate statistics  
Right section with more detailed information, where you can calculate everything based on selection (by **DataSet**, **LabCode** or by **QC Category**)

![QAQC Dashboard - DQ - Step 46.png](media://a49a9e59-22ae-40b8-9858-cfd8ea60ce35)

DataSet example  
LabCode example  
QC Category example

![QAQC Dashboard - DQ - Step 48.webp](media://da539277-be29-46b8-9b96-e440db5ed939)

## Raw Results

Tabular version of data where all samples are presented in one table.

![QAQC Dashboard - DQ - Step 52.png](media://6c1574a3-8b6c-4fcc-8d15-1cb7d6e87d4a)

## Repeats by Repeat Code

**Basic Information**  
Please note that this page is affected by **Global Filters.**

This comparison is used when comparing samples with same Sample ID but different repeat codes in tblAssay. Laboratory repeats are repeats on specific samples, because of some certain attributes of that sample, which make that sample different to the other samples in the batch. These attributes might be high element results, or contaminants (like sulphur or organics) which might affect the assay result.

For these results the Sample ID is always the same, all that differs is the Repeat number for the sample-element pair. These repeats are often reported as (for example) Au-Rpt1, Au-Rpt2, Au1, AuD, AuS and so on.

**Filters**  
This page has some additional **Filters**.

**Section Number 1**

**Sample Table**  
Using this filter you switch between different type of Samples: DrillHole, DrillHole QC, Linear, etc. Only one type can be selected at the time.

**Section Number 2**

**Element**  
Here you switch between different Elements (only one element can be selected at the time)  
**Repeats by Test**  
Here you can choose to display only those samples that passed the test.  
How we calculate the TEST you can see in here  
**RepeatType (x axis)**  
**RepeatType (y axis)**

**Section Number 3**

**DataSet**  
**Hole / Site ID**  
Depends on the Sample Table selection, here you can display only repeats for specific Hole or Site ID.  
**Batch_No**  
Display Samples for specific Batch_No  
**LabCode**  
Display Samples for specific LabCode

**Section Number 4**

**Advanced Filters**

![QAQC Dashboard - DQ - Step 56.png](media://b3df4962-d9bb-4048-ade7-9521056d4ca0)

**Advanced Filters** can be accessed by clicking the "**Filter**" button.

![QAQC Dashboard - DQ - Step 58.png](media://297dbcd4-45ad-47c3-9d00-6e7d748946f6)

**Advanced Filters**

**Match Method**  
This filter will sync across all pages under Repeats (excluding “by Method” and “by Element Name”)

**Additional Filter Options**

Sample_Category, Sample_Method and Sample_Type have been added in this release.

**Processing Values**  
If you do not want to process negative values (below detection if these have been stored as negatives, or default values for missing samples, samples destroyed in preparation, for example), please activate the filter.  
This filter will sync across all pages under Repeats (excluding “by Element Name”)

![QAQC Dashboard v2.1.1 - Step 51.png](media://71381ef7-747e-4014-b43f-8caa1b81a3da)


Click the **Filters** button to close the Advanced Filters.

![QAQC Dashboard v2.1.1 - Step 52.png](media://276f1df5-d1db-4fdd-8525-30c916e89147)


## Scatter Plot

**Scatter plots** display original assay data against any number of types of repeat assay data. Original values are plotted against the X axis and the repeat assay values against the Y axis.

If no bias exists between the two sets of data, they will plot about the line of y = x, a 45-degree line passing through the origin. A scatter plot may show that a relationship exists, but it does not and cannot prove that one variable is causing the other. Both variables could be related to some third variable, or pure coincidence might cause an apparent correlation.

![QAQC Dashboard v2.1.1 - Step 54.png](media://295b214f-a17f-43e6-8860-f6c47b500169)


The **Scatter Plot** can be viewed as three different styles:

**Combined (symlog)** - Symmetric log scales  
**Combined** – Linear scale  
**By Selection**, where you can choose between DataSet, LabCode, QC Category and TEST.  
x and y axis on these charts are independent.

![QAQC Dashboard - DQ - Step 70 (1).webp](media://cb62ddf2-b9af-4e48-9d30-9f7ef340b0b3)


The Scatter Plot can be displayed by:

- DataSet
- LabCode
- QC Category (default)
- Sample Category
- Samjple Method
- Sample Type
- Test

![QAQC Dashboard v2.1.1 - Step 62.png](media://9dfa283a-dd1f-4dd7-921c-54a9f6181d82)


## Q-Q - Quantile-Quantile Plots

**Q-Q Plots** are a good way to compare the distributions between two populations and are a very efficient way of highlighting bias in a duplicate data. **Q-Q plots** are generated by sorting the original and repeat results in ascending order and calculating the percentile for each then the paired percentiles are plotted against one another. If the two distributions being compared are identical, the **Q-Q plot** follows the line y=x; if the general trend of the **Q-Q plot** is flatter than the line y=x, the distribution of the Original assay results is more dispersed than the Repeats, and vice versa. Departure from the ideal is often more exaggerated at higher grades. This is less of an issue than a consistent small difference throughout a data range. **Q-Q Plots** are scale sensitive and should be examined at different scales to ensure that bias at low grades is not masked.

It is recommended that **Q-Q plots** should be interpreted using a single population. For instance, field duplicates should be assessed separately to Lab Check QC samples.

![QAQC Dashboard - DQ - Step 73.png](media://d17b374f-4a1f-4e69-a674-a36dd470f6a5)

## MAPD – HARD Plot

This can provide a simple way of checking if the repeatability for a selected grade range and a selected set of batches meets your threshold requirement.  
This chart plots the mean absolute paired difference values for sets of repeats as a percentile chart. It is calculated for a duplicate pair A and B as difference over average expressed as a percentile.

This analysis of repeats shows the Mean absolute paired difference %, shown in the equation:

**(%MAPD)**  
**= 100 * | [(Absolute pair difference) / 2] | / [pair mean]**

**= 100 * | abs(a-b)/2 | / [(a+b)/2]**

where:

**a** = original result

**b** = repeat result

![QAQC Dashboard - DQ - Step 75.png](media://17bf74e1-461d-479e-af34-c4f0dd10e22a)

## HT – Horwitz Trumpet

This chart can be used to help identify repeat values that are outside acceptable limits. The limit should be entered into the Warning (%) box before running the chart. As with the AVRD plots, high relative difference could indicate poor sampling practice, poor assaying or a high inherent nugget effect in the mineralisation.

This chart plots the percentage difference between the repeat value and the mean of the original-repeat pair at increasing concentrations.

The following formula is used:

![QAQC Dashboard - DQ - Step 77.png](media://87f90202-c856-4fe1-8ffe-73f6b52c28f7)

![QAQC Dashboard - DQ - Step 78.png](media://521f7a3c-b6d7-4de7-8894-3e0f901034aa)

## AVRD - Average Relative Difference

This plot allows you to plot and compare the precision across the selected data of multiple repeat types on the same chart. In the example below, you can easily see that lab pulp checks produced overall smaller **AVRD** values (hence greater precision) than Umpire lab samples. High relative difference could indicate poor sampling practise, poor assaying or a high inherent nugget effect in the mineralisation.

This analysis of repeats of the **Average Relative Difference (AVRD)** uses a percentile plot showing the absolute difference divided by the pair mean.

The **AVRD** values are sorted in ascending order and plotted from 0 to 1.0 (100%) on the X Axis, allowing you to see the distribution of **AVRD** values through the selected data.

The following equation is used:

**(AVRD) = | Absolute pair difference | / [pair mean] = | abs(a-b) | / [(a+b)/2]**

where:

**a** = original result

**b** = repeat result

This plot is similar to the %MAPD Plot.

![QAQC Dashboard - DQ - Step 80.png](media://dc1b9c0d-4844-4166-a86a-3fafb83d72f3)

## TH – Thompson-Howarth Plot

**Thompson-Howarth plots** are a good way to show precision over the concentration range of the samples being assayed. However, the large-sample method defined by **Thompson and Howarth** relies on an assumption that the measurement errors are normally distributed; so it produces significantly biased results when the errors are not normally distributed. Normally distributed errors are probably the exception rather than the rule in ore deposits, so using the **TH** approach may provide a significantly inaccurate estimate of the quality of their geochemical concentration data.

To generate a meaningful interpretation of the data it is recommended that a **TH** plot should be generated on single populations which have more than 50 samples.

Each blue dot represents an original-repeat pair. The Y-axis plots the absolute difference between the original and the repeat, and the X-axis shows the mean of the 2 results. The pairs are sorted by increasing mean value then grouped into sets of 11 pairs, plotted as red diamonds. For the grouped points, the Y axis is the median of the differences for the group of 11 and the X axis is the mean of the means for the group of 11 pairs. The red line is a line of best fit (linear regression) for the grouped (red) points.

![QAQC Dashboard - DQ - Step 84.png](media://a10ceecb-3a59-49d4-bebf-efbc8327bd62)

## Statistics

The **Stats** Section is divided into two sections:

Left section with overall descriptive and bivariate statistics.  
Right section with more detailed information, where you can calculate everything based on selection (by DataSet, LabCode or by RepeatType (y axis).

![QAQC Dashboard - DQ - Step 87.png](media://a1490601-3163-413e-820a-253929e505ac)

LabCode example  
DataSet example

![QAQC Dashboard - DQ - Step 88 (1).webp](media://fee8bc7f-8ac7-4d99-86e0-1b373d2360fa)


## Raw Results

Tabular version of data where all samples are presented in one table.

![QAQC Dashboard - DQ - Step 90.png](media://0f5d950f-e8e0-4a8b-8700-dfb8e68a5608)

## Repeats by Method

**Basic Information**  
Please note that this page is affected by Global Filters.

(Scatter plot only) Useful in comparing assay and analytical procedures, where you may want to see the difference in results by 2 different analysing methods, for example 4 Acid vs Aqua Regia; Fire Assay vs Screen Fire Assay. These results will only be present if same samples have been assayed by different techniques. They are not to be confused with umpire samples.

**Filters**  
This page has some additional Filters.

**Section Number 1**

**Sample Table**  
Using this filter you switch between different type of Samples, DrillHole, DrillHole QC, Linear, etc. Only one type can be selected at the time.

**Section Number 2**

**Element**  
Here you switch between different Elements (only one element can be selected at the time).  
**Repeats by Test**  
Here you can choose to display only those samples that passed the test.  
How we calculate the TEST you can see in here  
**Method (x axis)**  
**Method (y axis)**

**Section Number 3**

**DataSet**  
**Hole / Site ID**  
Depends on the Sample Table selection, here you can display only repeats for specific Hole or Site ID.  
**Batch_No**  
Display Samples for specific Batch_No  
**LabCode**  
Display Samples for specific LabCode

**Section Number 4**

Advanced Filters

![QAQC Dashboard - DQ - Step 92.png](media://ad6a3868-6444-4682-93f2-8f9d7a74e4e9)

**Advanced Filters**

**Original Preferred**  
Select the Preferred Original Value.  
**Repeat Preferred**  
Select the Preferred Repeat Value.

**OverRange Values**  
If you want to Include or Exclude OverRange values.

**Processing Values**  
If you do not want to process negative values, please activate the filter.  
This filter will sync across all pages under Repeats.

![QAQC Dashboard - DQ - Step 93.png](media://62355575-b554-4a59-af9f-25a0d99d9495)

Click the **Filters** button to close Advanced Filters.

![QAQC Dashboard - DQ - Step 94.png](media://19a1ff65-5140-434f-b559-a205afe3fff8)

## Scatter Plot

**Scatter plots** display original assay data against any number of types of repeat assay data. Original values are plotted against the X axis and the repeat assay values against the Y axis.

If no bias exists between the two sets of data, they will plot about the line of y = x, a 45-degree line passing through the origin. A scatter plot may show that a relationship exists, but it does not and cannot prove that one variable is causing the other. Both variables could be related to some third variable, or pure coincidence might cause an apparent correlation.

The **Scatter Plot** can be shown by DataSet, LabCode, Method or Test.

![QAQC Dashboard - DQ - Step 96.png](media://cacd13f7-defe-4cfb-99f7-7dc9963f2d57)

There are two types of Scatter Plots:

**Combined (symlog)** - Symmetric log scales  
and **Combined** – Linear scale

![QAQC Dashboard - DQ - Step 97 (1).webp](media://82dd2908-07a9-42bf-9f04-7ed6e1f0c46f)


## Raw Results

Tabular version of data where all samples are presented in one table.

![QAQC Dashboard - DQ - Step 99.png](media://18ec5d1b-b6f9-4de9-bc14-fdbb97bae1cc)

## CRM - Certified Reference Material

**Basic Information**  
Please note that all pages under this section (CRMs) are affected by Global Filters.

**How we calculate Test**  
**Action :** ABS( sysResult - ExpectedValue ) > 3 ** ExpStdDev || sysResult >= ExpectedMax.*  
***Alert :**** ABS( sysResult - ExpectedValue ) > 2* * ExpStdDev.  
**OK :** Rest of Results.  
How we calculate Blanks  
If Standard_Type contains "blank" or "blk".

The size of the characters does not matter.

![QAQC Dashboard - DQ - Step 102.png](media://f2444be8-e856-4fe4-b81b-17fee49f34c7)

## CRM Performance

Click "**CRM Performance**" to open the page.

![QAQC Dashboard - DQ - Step 106.png](media://3b4f2bad-1464-4d6c-9b37-fd0d86a7b790)


## **Combined – Control Chart**

There are Data selection dropdowns that can further filter the data.

**Section Number 1**

Select CRMs type  
Here you can switch between Standards and Blanks

**Section Number 2**

**Element**  
Use this filter to select an Element.  
**Standard_Type**  
List of Standard Types

**Standard ID**

Select a specific Standard

**sysAssayStatus**

**sysResult Min & Max**

**Section Number 3**

**Test**  
Action, Alert or OK  
**Method**  
Laboratory determination method  
**Hole / Site ID**  
Depends on the Sample Table selection, here you can display only repeats for specific Hole or Site ID.  
**Batch_No**  
Display Samples for specific Batch_No  
**LabCode**  
Display Samples for specific LabCode

![QAQC Dashboard v2.1.1 - Step 105.png](media://24aece0a-e850-44e5-9bd0-4ffa7f554c7c)



For the best performance max 5-10 standards should be selected.

Control charting is a powerful and simple tool for the daily quality control of routine analytical work. Control charts are an effective, efficient oversight screening tool for monitoring data quality. They are used to determine if the assaying process is in a state of statistical control and to examine the relative variability of repetitive assay data. Control charts are used with reference materials and to assess the accuracy of lab analysis. They are suitable for analysis of any element. More important is to ensure that results being compared are produced under the same conditions, mainly using the same analysis method.

The basis is that the control samples are submitted together with the routine samples in an analytical run. Material of control samples (Standards) can be blank samples, in-house control materials or certified reference materials. After the analytical run is completed the control values are plotted on a control chart.

The central line (CL) in the control chart represents the nominal value of a certified reference material, or where this is not known usually the mean value of the plotted points. In addition to the central line, the control chart normally has four lines. Two of these, the so-called warning limits, are located at a distance of ± two times the standard deviation from the central line (CL ± 2s). Provided that the results are normally distributed, about 95 % of the results should be within these limits. Two other lines are also drawn at a distance of ± three times the standard deviation from the central line (CL ± 3s). These lines are called the action limits and 99.7 % of the data normally distributed should be within these limits. Statistically only three out of 1000 measurements are thus located outside the action limits. If the control value is outside the action limits, there is a high probability that the analysis is in error.

Assay values are plotted in a control chart. In this way it is possible to demonstrate that the analysis procedure performs within given limits. Control charts allow one to see trends in a timely manner, to identify if corrective action is needed. Due to their ease in interpretation, they permit improved oversight and control of data quality. The visual display of data helps to identify patterns and trends that might go unnoticed using summary reports or numerical formats. Charts can be used to identify these patterns, to identify potential problems, and to suggest corrective measures.

Essentially, the control chart helps us to see whether a process is in a state of control or out of control. The variables are a measurable characteristic of a product or service. The control chart shows the spread or dispersion of the results. There is a natural variation even when the process is in control. When a point falls outside its control limits, the process can be considered out of control and requires investigation.

Generally the process can be considered in control if:

- Two thirds of all are near the expected value.
- Points float above and below the centre line.
- Points are balanced on each side of the expected value.
- No points are outside the control limits (standard deviations).
- There are no obvious patterns or trends.  
Whenever reviewing trend charts, look at the overall performance over time. A single divergence may be acceptable if the overall performance meets criteria, provided a critical decision is not based on that data point, a satisfactory explanation has been provided and corrective action has been taken.

Trend charts are an effective broad brush tool. Fine-tuned oversight is still necessary to determine cause of exceedances.

![QAQC Dashboard v2.1.1 - Step 107.png](media://56679f5c-54f7-4e30-87aa-72e6b6cb3490)


**Standards by LabCode**  
Charts below present sysResult for all Samples (for selected Element and Standard ID / Standard Type) separated by LabCode.

It is an easy way to spot some patterns for each LabCode.

![QAQC Dashboard v2.1.1 - Step 109.png](media://43ffe7b4-d281-4a22-9946-d9d0fc9470cd)


**Standards By Date**  
For the best performance max 5-10 Standards should be selected.

Scatterplot below presents samples sysResults by Lab_Job_Date for selected Standards.

Extra reference lines can be shown by selecting “Lines” checkbox on the bottom left of the chart.

![QAQC Dashboard v2.1.1 - Step 111.png](media://8bd54502-6e4b-46b2-9ea6-a2988d1c10e8)


### **Bias Results**

For the best performance max 5-10 Standards should be selected.

**How we calculate BIAS**

**BIAS = 100 * ((Result - Expected Value) / Expected Value)**

Note, that ExpectedValue must be set to show Samples / Standards on the BoxPlot.

Box plots provide a box for every standard for a selected element. The box-plot provides a summary of performance for all standards analysed in the selected batches.

Red vertical line indicates 0.

![QAQC Dashboard v2.1.1 - Step 113.png](media://9e677b8d-61ae-46eb-a57e-0dd744cdd625)


### **Raw Results**

Data presented in the table below shows all Samples for Selected Element, Standard ID / Alias, Standard Type (if selected) and CRM Type (Standards or Blanks).

![QAQC Dashboard v2.1.1 - Step 116.png](media://7ee80b15-c597-4e76-89d7-dd30cebad2ec)


## CRM Summary Stats

The "**by Standard ID**" tab shows the Standards performance by Standard Type. The Standard Type can be expended by clicking the + icon on the right of the Standard Type.

![QAQC Dashboard - DQ - Step 118.png](media://5a48aa19-53a3-4849-b5ea-6e3efa9348c7)

The "**by Others**" tab displays Standard performance by several categories:

LabCode, Standard Type, Batch No and HoleID.

![QAQC Dashboard - DQ - Step 120.png](media://3526246a-f80e-47f9-b0a6-c5b036a0d3b6)

## Screen Test

## Basic Information

Please note that this page is affected by Global Filters.

**Jitter Plot – Individual Samples**  
A jitter plot represents data points in the form of single dots, in a similar manner to a scatter plot. The difference is that the jitter plot helps visualize the relationship between a measurement variable and a categorical variable.

Data presented on the chart shows individual samples by Sample Type.  
x-axis is a random value to avoid overlapping points.

![QAQC Dashboard - DQ - Step 124.png](media://44e7dbce-b7c7-4563-9524-54e9730a0d80)

**Samples by Date**

The bar chart shows Total Number of Samples by Date (Year / Month / Day).

![QAQC Dashboard - DQ - Step 125.png](media://8fc32727-6b32-475a-9cf7-8b3cb14fbfba)

**Samples by Sample Type**  
The bar chart shows Total Number of Samples by Sample Type.

![QAQC Dashboard - DQ - Step 126.png](media://edf98c7f-5ef6-46d1-8434-e14e7a1af8ab)

**Table** – Raw Data

![QAQC Dashboard - DQ - Step 127.png](media://f3319039-38dd-44a9-a244-d5272c7f2fb8)

## Weights

**Basic Information**  
Please note that this page is affected by Global Filters.

Please use Filters on the top (select specific Element or/and Batch_No) to limit number of samples.  
More than 10,000 samples can cause issues with performance.

![QAQC Dashboard - DQ - Step 129.png](media://77bebf21-ba79-473a-8e06-c254a29fffca)

Page has 4 sections:

**Section 1 – Main Filters**  
Here you can limit number of samples to specific Element or Sample ID.  
Multiple selection is allowed.  
**Section 2 – Samples / Weights distribution.**  
Here you can see how the data is distributed across different weight (kg)  
**Section 3 - Outliers**  
In this section you can set the range for outliers.  
Selected range will affect charts in **Section 4** – Sample (dots) will have different colours – green (for non-outliers) and orange (for outliers).

**Section 4 – Analyse samples / weights**  
Here you can analyse your data by:

- Date
- Sample ID
- Batch No
- LabCode
- Raw Data

![QAQC Dashboard - DQ - Step 130 (1).png](media://bf5b4552-6cd0-4577-829a-e36c707494d9)


By Sample ID example.  
By Date example.  
By Batch No example.  
By Batch No/LabCode example.  
Raw Data Table example.

![QAQC Dashboard - DQ - Step 131 (2).webp](media://781935e6-0981-4975-816c-dcc1aa385cfb)