---
title: "QAQC v2.2.0 PowerBI Dashboard"
canonical: "https://servicedesk.maxgeo.com/space/DS/2472247297/QAQC%20v2.2.0%20PowerBI%20Dashboard"
format: markdown
---
*This guide explains the key QAQC 2.2.0 changes so you can quickly adapt workflows: it highlights usability upgrades like dark mode and universal left-panel filters, important plotting defaults (log scale and exclusion of non-positive values), new adjustable test parameters (difference threshold and detection limit factor), and removed legacy pages/tabs to streamline reporting. Reviewing it prevents unexpected analysis gaps and helps you reconfigure settings for consistent results.*


> Macro (toc)

To open the QAQC Dashboard, click on **Dashboards**, **General Reports**, then **QAQC v2.2.0**

![QAQC v2.2.0 Release Note - Step 2.jpeg](media://1475b636-f461-4013-8d63-952335c73749)


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

![QAQC v2.2.0 Release Note - Step 3.jpeg](media://33efb81f-a994-4b7f-a952-86e4c74c1eb7)


> 📝 **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.  


![QAQC Dashboard v2.2.0 - Userguide  - Step 10 (2).jpeg](media://936e2b66-938d-4e29-87c7-47d4a6ff47fc)

Step 1: Select a **Project**

![QAQC Dashboard v2.2.0 - Userguide  - Step 11 (1).jpeg](media://324defdd-6b18-455b-9d61-80f8b1e04d33)

Step 2: Select a **DataSet**

![QAQC Dashboard v2.2.0 - Userguide  - Step 12 (1).jpeg](media://b5dea624-40c4-4794-9334-f2578644683d)

Step 3: Select a **Source Type**

![QAQC Dashboard v2.2.0 - Userguide  - Step 13.jpeg](media://40150ac3-f621-40b2-bc92-c1263be57ac9)

Step 4: Select **Lab Code**/s

![QAQC Dashboard v2.2.0 - Userguide  - Step 14 (1).jpeg](media://52f21c2c-0787-4433-a63d-2dfb9f0a1638)

Step 5: Set the **Date Range**

![QAQC Dashboard v2.2.0 - Userguide  - Step 15 (1).jpeg](media://b5043597-9185-4bde-82c7-33bf8bcf6fd5)

Step 6: Select a **Batch** or **Batches**

![QAQC Dashboard v2.2.0 - Userguide  - Step 16 (1).jpeg](media://8d27e35d-572d-40c3-8477-13687d6f28ce)

Then click "**Start Analysis**" to close the **Global Filters** screen.

![QAQC Dashboard v2.2.0 - Userguide  - Step 17.jpeg](media://afde9188-4f0f-4249-b28c-9072b8271777)



## 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 v2.2.0 - Userguide  - Step 27.jpeg](media://96ca47cd-445b-4a2b-b3fc-f3491dd9f094)


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 v2.2.0 - Userguide  - Step 28.jpeg](media://1c336b01-2b1d-462e-85fd-0bb96e99f76e)


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 v2.2.0 - Userguide  - Step 29.jpeg](media://bbb2d745-861e-488c-8e2b-d2276a4ab88c)


## 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 v2.2.0 - Userguide  - Step 31.jpeg](media://2ace9bae-d7cd-4d5a-8927-87571414bd51)

Additional filtering can be made on Elements,

![QAQC Dashboard v2.2.0 - Userguide  - Step 32.jpeg](media://988de350-3140-49e4-a529-025b87946847)

and Standard Types

![QAQC Dashboard v2.2.0 - Userguide  - Step 33 (1).jpeg](media://c4a4199a-cc72-4653-b57d-f105e19a5d05)

The Total number of CRM samples analysed and the ratio of CRM samples to regular samples, indicating QAQC coverage.

![QAQC Dashboard v2.2.0 - Userguide  - Step 34.jpeg](media://e435c039-ef4f-480d-a46f-cf7c8f6f2075)

The Summary table provides a breakdown of CRM performance by standard type.

![QAQC Dashboard v2.2.0 - Userguide  - Step 35.jpeg](media://275f84b2-60e3-4907-adb2-31c01d52a436)

Click the "**Blank**" button to show the same break down for Blanks.

![QAQC Dashboard v2.2.0 - Userguide  - Step 36.jpeg](media://9d53da28-2006-4d6e-8687-93deff0e73e3)

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.

![QAQC Dashboard v2.2.0 - Userguide  - Step 37.jpeg](media://90ab7502-8111-43a7-b780-be3d2573b584)

Additional filter by Sample Type

![QAQC Dashboard v2.2.0 - Userguide  - Step 38.jpeg](media://efd6544f-92d6-41a0-aeca-be8cd4c0e461)


## 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.

Element  
Here you switch between different Elements (only one element can be selected at the time)

**Section Number 2**

Hole / Site ID

- Depends on the Sample Table selection, here you can display only repeats for specific Hole or Site ID.

**Section Number 3**

- QC Category

**Section Number 4**

Advanced Filters

![QAQC Dashboard v2.2.0 - Userguide  - Step 47.jpeg](media://0c42b265-e3fb-4818-9b07-2a8996b7928f)

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


![QAQC Dashboard v2.2.0 - Userguide  - Step 48.jpeg](media://706ea19a-120a-416d-b2a5-3167f4159a6b)

**Advanced Filters**

**Match Method**  
This filter will sync across all pages under Repeats (excluding “by Method” and “by Element Name”)  
“**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 v2.2.0 - Userguide  - Step 49.jpeg](media://5692cd53-07d8-4e03-a352-91cc57349511)


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

![QAQC Dashboard v2.2.0 - Userguide  - Step 50.jpeg](media://c566f37e-d804-41b1-adc8-d8f52032fb2c)


## 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 v2.2.0 - Userguide  - Step 55.jpeg](media://b1ce0062-c093-4862-9eef-f4d6d6783c94)

The Scatter Plot can be displayed by:

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 56.jpeg](media://5abbff6b-d4e1-4924-af93-eba60634b2b1)



The Scatter Plot can be limited to QC Category.

![QAQC Dashboard v2.2.0 - Userguide  - Step 57.jpeg](media://e89e7d93-4de8-4b70-9725-e9b42bcd809d)

Sample Types can be selected.

![QAQC Dashboard v2.2.0 - Userguide  - Step 58.jpeg](media://9e4778fa-8177-41df-9b0c-5d314c593c39)

The Element can be selected.

![QAQC Dashboard v2.2.0 - Userguide  - Step 59.jpeg](media://f09de53b-1b75-440b-8186-c852e81e2131)

The Scatter Plot can be set to a Linear Axis.

![QAQC Dashboard v2.2.0 - Userguide  - Step 60.jpeg](media://2b20519c-1386-4787-9953-f40b019aca04)

Or a Log Axis.

![QAQC Dashboard v2.2.0 - Userguide  - Step 61.jpeg](media://c56768f8-eb6d-42e4-9476-ba67e234e239)

The chart can be filtered by selected Holes.

![QAQC Dashboard v2.2.0 - Userguide  - Step 62.jpeg](media://60b128f5-d63c-43cd-bed9-5ca7d47dc6d5)


## 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 v2.2.0 - Userguide  - Step 64.jpeg](media://d907be97-97cb-485b-92eb-14d69d2b7225)


## 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 v2.2.0 - Userguide  - Step 66.jpeg](media://30610d3b-3bb8-469f-9b76-193f26aa2bca)

## 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://1d178b0c-07d3-4c7e-bac2-234f982cb735)

![QAQC Dashboard v2.2.0 - Userguide  - Step 69.jpeg](media://76a6cc86-c927-4a98-a9fb-3b8ed40ec384)

## 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 v2.2.0 - Userguide  - Step 71.jpeg](media://94b25737-33c2-4911-850f-f49148c153f6)

## 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 v2.2.0 - Userguide  - Step 73.jpeg](media://c7b2055a-33eb-474f-808b-740f2909a972)

## Raw Results

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 75.jpeg](media://50916d1d-859c-46f7-bbc5-0553ece02cfc)

## 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.

**Element**  
Here you switch between different Elements (only one element can be selected at the time)

**Section Number 2**

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

**Section Number 3**

**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 4**

**Advanced Filters**

![QAQC Dashboard v2.2.0 - Userguide  - Step 77.jpeg](media://fdcfa95d-3618-4fdd-83bc-1a23744a6565)

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 78.jpeg](media://919dd78f-5ad5-48aa-821e-03e80ec0270b)

**Advanced Filters**

**Match Method**  
This filter will sync across all pages under Repeats (excluding **by Method** and **by Element Name**)  
**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 v2.2.0 - Userguide  - Step 79.jpeg](media://1d7afa1f-b77e-48ad-aa54-4d3ebf48b2dd)


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

![QAQC Dashboard v2.2.0 - Userguide  - Step 80.jpeg](media://9c5deabb-9a9a-4f60-9a48-22b3a5f9faeb)


## 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 v2.2.0 - Userguide  - Step 82.jpeg](media://e8951129-dd26-4456-bd79-cdc1dabb121f)


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, Sample Category, Sample Method, Sample Type and TEST.  
x and y axis on these charts are independent.

![QAQC Dashboard v2.2.0 - Userguide  - Step 83.jpeg](media://308c1929-f5ce-459c-a85f-b7088f793d2a)




## 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 v2.2.0 - Userguide  - Step 86.jpeg](media://bdcd9829-933c-43cf-bcce-b184dfaa48c7)

## 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 v2.2.0 - Userguide  - Step 88.jpeg](media://493548d4-9c20-468d-9a06-c94ff0f66ab4)

## 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://8fd04638-c9c2-4340-85b3-89732d3ec160)

![QAQC Dashboard v2.2.0 - Userguide  - Step 91.jpeg](media://62547583-a5d8-46f3-90d1-361bfa8ac88b)

## 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 v2.2.0 - Userguide  - Step 93.jpeg](media://e22ae186-f9eb-4af1-9899-86d68bce6c5c)

## 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 v2.2.0 - Userguide  - Step 95.jpeg](media://983f4163-373e-4f83-b196-fdaeaf94aaf9)

## Raw Results

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 97.jpeg](media://69349ae4-a8ad-4926-8766-04bf28e5b0b5)

## 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.

**Element**  
Here you switch between different Elements (only one element can be selected at the time)

**Section Number 2**

**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.

**Section Number 4**

Advanced Filters

![QAQC Dashboard v2.2.0 - Userguide  - Step 99.jpeg](media://7843867f-80b6-4408-8624-09633e866463)

**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.

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 100.jpeg](media://12f2946f-8bf3-46ac-9905-887af8cd757a)

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 101.jpeg](media://62d7d467-6e2d-466d-a447-cfbb3415b203)


## 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 v2.2.0 - Userguide  - Step 103.jpeg](media://1c1716e6-6f1a-4314-971d-d036fbe45af2)


## Raw Results

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 105.jpeg](media://4a0535fb-360d-4fe1-9800-6f7f2f882b0d)


## CRM - Certified Reference Material

## **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**

**Standard Type**  
Use this filter to select one or more Standard Type.  
**Standard ID / Alias**  
List of Standard IDs or Aliases – you can use that filter to select one or more specific **Standard ID / Alias.**

**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 v2.2.0 - Userguide  - Step 108.jpeg](media://dfefb94d-a04f-4407-8bfb-f11fb4008365)



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 v2.2.0 - Userguide  - Step 109.jpeg](media://c441f6f0-c492-4885-8454-ae0b2d520d5e)


**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 v2.2.0 - Userguide  - Step 110.jpeg](media://690b18fa-02e3-4d67-8941-1b2d50f32d11)


### **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 v2.2.0 - Userguide  - Step 111.jpeg](media://ace0748d-498e-426a-8b55-8669d6015f8c)


## CRM Summary Stats

The CRM Summary Stats tab shows the Standards performance by Standard Type.

![QAQC Dashboard v2.2.0 - Userguide  - Step 113.jpeg](media://86777bd6-03b6-46da-ba39-5e25a8ec5ac6)


## 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 v2.2.0 - Userguide  - Step 115.jpeg](media://e17f0e6e-63ce-47ba-ad2e-0c06e986fae0)

**Samples by Date**

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 116.jpeg](media://f15394e4-1833-470d-8dc5-f7bd2351205a)

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

![QAQC Dashboard v2.2.0 - Userguide  - Step 117.jpeg](media://1f785637-0fcf-4223-b774-fb19761c916b)

**Table** – Raw Data

![QAQC Dashboard v2.2.0 - Userguide  - Step 118.jpeg](media://16c491d4-129e-41c3-942c-dce0a9a4ef16)

## 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 v2.2.0 - Userguide  - Step 120.jpeg](media://6d3aafcd-3140-4bdb-ad2f-b68576a402b7)

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
- Table

![QAQC Dashboard v2.2.0 - Userguide  - Step 121.jpeg](media://0ef118c1-ea8e-4ce3-8d45-28befc9c9d80)


By Sample ID example.

![QAQC Dashboard v2.2.0 - Userguide  - Step 122.jpeg](media://d7d08a5f-a7ab-40e6-b1de-95e844cebff3)


By Date example

![QAQC Dashboard v2.2.0 - Userguide  - Step 123.jpeg](media://a6fa7583-a849-4a14-addf-44b3bd6806f4)

By Batch No example

![QAQC Dashboard v2.2.0 - Userguide  - Step 124.jpeg](media://197dd52a-3563-4384-bb50-d4d4b0b473b0)

By Batch No/LabCode example

![QAQC Dashboard v2.2.0 - Userguide  - Step 126.jpeg](media://269bc6bf-7e36-4630-bdc5-786f49cafdac)

## Lab Turnaround

Lab Turnaround shows Lab performance with Total Batches and Samples.

![QAQC Dashboard v2.2.0 - Userguide  - Step 133.jpeg](media://1caeb2c1-fff9-40b8-bfb6-82035e23d851)