---
title: "QAQC v2.0 PowerBI Dashboard"
canonical: "https://servicedesk.maxgeo.com/space/DS/1503461423/QAQC%20v2.0%20PowerBI%20Dashboard"
format: markdown
---
# maxgeo.com
# TABLE OF CONTENTS
> Macro (toc)
# Introduction
QAQCR, Quality Assurance Quality Control Power BI Report, is a reporting tool developed to view, analyse and report Assay Standard and Repeat Data for the mining and minerals exploration industries. It has been developed by Maxwell GeoServices (“maxgeo”), using software developers and geologists who understand the importance of managing assay databases. There is a need for the minerals industry to be vigilant in the way they use and report assay data. This can only be achieved by careful and complete storage of all data. QAQCR allows the user to assess the relative accuracy of assay data returned from the assay laboratory.
QAQCR’s main features include.
- Check the accuracy of assay data for standards and blanks against expected ranges and values for certified standards and blanks, laboratory standards and blanks, and company reference standards and blanks;
- Plot original assay values against all repeat-type assay results or selected repeat-type assay results (e.g. laboratory checks or field duplicates). Chart original versus repeat assay values using scatter plots, box-plots, QQ plots, Thompson-Howarth plots, %MAPD percentile plots, AVRD plots or Horwitz Trumpet plots;
- Plot summary sizing results showing the number of samples which meet specified requirements by month or by DataSet;
- Plot lab turnaround times by batch and show monthly averages on the same chart.
- Select assay data by Project; DataSet; LabCode; Batch_Status & Hole_ID/Site_ID;
- Select assay data by date range;
- Compare variation in assay standard results between laboratories;
- Track repeat and duplicate samples by batch and sample type;
- Compare standard assay results with all historical data;
- Report assay standard data in chart format and table format by standard name, method and / or element;
- Report “Bad” standard assay results with configurable expected limits;
- Report “Bad” repeat results
# Global Filters
## Basic Information
Changes made on this page will impact all pages in this report, including all charts, tables, and calculations.
Global Filters are connected to others table by special key “Key_LabCode” (DataSet-LabCode-Batch_No) so basically every table is filtered at the Batch level.
## Filters
Global Filter page is divided into three sections.
1. First Section shows the incoming data (without any filters)
2. Second Section is the section where you filter the data
3. Third Section – Here you can see how filters from Section Two affected your data.
The number next to each filter indicates how many items each filter has.
Date Filter will filter data based on Lab_Final_Date.
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Related Batches
Using “Related Batches Processing” filter you can decide to include or exclude related batches in Repeat analyses.
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QAQC Summary
The summary page – Total number of Samples and Batches for each section of the Report.
Additionally, in the table below (left bottom) you can see total number of samples for each section by Batch Number.
You can limit number of Batches (or select specific one) using filter on the top of the page.
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QC Results
This page presents Total number of Repeats and Standards / Blanks.
Additions Filter can apply – Please check “Filter” of the left side of the page.
1. **Section 1 – Overall table**
The table presents total number of Repeats, Standards and Blanks by Test.
How we calculate the Test can be found at the beginning of each section for Repeats and CRMs.
2. **Section 2 – Repeats Distribution.**
Please note that only samples by Sample Name (QC categories in related QC sample tables) and Repeat Code (Repeat value located in the normalized assay table, where the Sample IDs are the same for different repeat values) are presented.** **
3. **Section 3 – Standards / Blanks**
For the best performance only one Standard_Type should be selected at the time. For each standard type, you can select one of more standards.
You can switch between Standards and Blanks using buttons on the left side.
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Repeats
## Navigation
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How we calculate Repeats Test
- **Alert** : ABS( OriginalResult - RepeatResult ) >= 0.1 * OriginalResult
- **OK** : Rest of Results
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Summary
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Filters
Section Number 1
- **Sample Table**
Using this filter you switch between different type of Samples .. DrillHole, DrillHole QC, Linear, pXRF etc. Only one type 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.
- **QC_Category**
Please note that this filter will work for Repeats by Sample Name only.
For the rest of the Samples (by Laboratory, Method etc), the QC_Category will be (Blank)
- **Element**
Here you can select specific Element, for example AU
- **Batch_No**
Display Samples for specific Batch_No
- **LabCode**
Display Samples for specific LabCode
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Total Samples Summary
This section shows the Total Number of Samples by Compare Type & Sample Table regardless of the Sample Table Filter chosen (Sample Table Filter will not affect this section).
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Repeats by Sample Name
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Basic Information
Please note that this page is affected by [Global Filters](#_Global_Filters)
This comparison is used when comparing samples with different names. These are usually repeats initiated by the clients (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 the 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.
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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, pXRF 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](#_Navigation)
- **QC_Category**
Here you can pick one or multiple QC Categories – separating sample categories is recommended
Section Number 3
- **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
- **Match Method**
This filter will sync across all pages under Repeats (excluding “by Method” and “by “by Element Name”)
- **“Do Not Process Negative 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”)
- **pXRF Filters**
Additional pXRF filters may apply to this page. If the pXRF filter is turned on, you will see ** In Use ** icon under this section.
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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 LabCode or by QC Category)
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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.
Under this section you can select different types of Scatter Plot
- **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.
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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:
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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
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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 described [here](#).
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TH – Thompson-Howarth Plot
Thompson-Howarth plots are a good way to showing 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.
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QQ - Quantile-Quantile Plots
QQ 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. QQ 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 QQ plots should be interpreted using a single population. For instance field duplicates should be assessed separately to Lab Check QC samples.
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Raw Results
Tabular version of data where all samples are presented in one table.
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Repeats by Repeat Code
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Basic Information
Please note that this page is affected by [Global Filters](#_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.
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> Macro (anchor)
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, pXRF 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](#_Navigation)
- **RepeatType (x axis)**
- **RepeatType (y axis)**
Section Number 3
- **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
- **Match Method**
This filter will sync across all pages under Repeats (excluding “by Method” and “by “by Element Name”
- **“Do Not Process Negative Values”**
If you do not want to process negative values, please activate the filter.
This filter will sync across all pages under Repeats (excluding “by Element Name”
- **pXRF Filters**
Additional pXRF filters may apply to this page. If the pXRF filter is turned on, you will see ** In Use ** icon under this section.
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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 LabCode or by RepeatType (y axis)
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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.
Under this section you can select different types of Scatter Plot
- **Combined (symlog)** - Symmetric log scales
- **Combined** – Linear scale
- **By RepeatType (y)**.
x and y axis on these charts are independent.
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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:
### > Macro (anchor)
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
> Macro (anchor)
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 described [here](#).
TH – Thompson-Howarth Plot
Thompson-Howarth plots are a good way to showing 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.
> Macro (anchor)
QQ - Quantile-Quantile Plots
QQ 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. QQ 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 QQ plots should be interpreted using a single population. For instance field duplicates should be assessed separately to Lab Check QC samples.
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Raw Results
Tabular version of data where all samples are presented in one table.
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Repeats By Element Name
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Basic Information
Please note that this page is affected by [Global Filters](#_Global_Filters)
This comparison is used when comparing different elements for the same sample. While not a QA/QC requirement this option can be used to compare any two elements (e.g. Au vs Ag; Ni vs Cu). It may be of use when comparing expected assays for assay-pill samples versus the measured assay result (this assumes that the element names differ). It is also used where element names contain information about the repeat code for the sample.
Please note that because of number of potential samples, this is a separate table in Power BI. That table is connected to SQL Server using DirectQuery and the connection can be very slow. We recommend limiting number of samples to minimum (by selecting specific Batch_No, Date range, LabCode etc.)
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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, pXRF etc. Only one type can be selected at the time.
Section Number 2
- **Element (x axis)**
- **Element (y axis**
Section Number 3
- **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
- **“Do Not Process Negative Values”**
If you do not want to process negative values, please activate the filter.
- **pXRF Filters**
Additional pXRF filters may apply to this page. If the pXRF filter is turned on, you will see ** In Use ** icon under this section.
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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.
Under this section you can select different types of Scatter Plot
- **Combined (symlog)** - Symmetric log scales
- **Combined** – Linear scale
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Raw Results
Tabular version of data where all samples are presented in one table.
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Repeats by Method
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Basic Information
Please note that this page is affected by [Global Filters](#_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 nor to be confused with umpire samples.
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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, pXRF 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](#_Navigation)
- **Method (x axis)**
- **Method (y axis)**
Section Number 3
- **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
- **“Do Not Process Negative Values”**
If you do not want to process negative values, please activate the filter.
This filter will sync across all pages under Repeats (excluding “by Element Name”
- **pXRF Filters**
Additional pXRF filters may apply to this page. If the pXRF filter is turned on, you will see ** In Use ** icon under this section.
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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.
Under this section you can select different types of Scatter Plot
- **Combined (symlog)** - Symmetric log scales
- **Combined** – Linear scale
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Raw Results
Tabular version of data where all samples are presented in one table.
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Repeats by Preferred
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Basic Information
Please note that this page is affected by [Global Filters](#_Global_Filters)
(Scatter plot only) Useful in comparing preferred assay results (typically 0) against other preferred values. Can be useful when comparing re-assayed batches, where original batch will be Preferred = 1 comapred to the re-assay batch of Preferred = 0.
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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, pXRF 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](#_Navigation)
- **Preferred (x axis)**
- **Preferred (y axis)**
Section Number 3
- **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
- **“Do Not Process Negative Values”**
If you do not want to process negative values, please activate the filter.
This filter will sync across all pages under Repeats (excluding “by Element Name”
- **pXRF Filters**
Additional pXRF filters may apply to this page. If the pXRF filter is turned on, you will see ** In Use ** icon under this section.
### > Macro (anchor)
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.
Under this section you can select different types of Scatter Plot
- **Combined (symlog)** - Symmetric log scales
- **Combined** – Linear scale
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Raw Results
Tabular version of data where all samples are presented in one table.
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Portable X-ray fluorescence (pXRF)
The pXRF page is divided into two sections.
1. **Section 1 – pXRF Settings**
In this section you can set min and max time (sec) for specific time Type (Live or Elapsed).
This setting will affect other pages like Repeats (by Sample Name and Repeat Code) and CRMs.
On the bottom of this sections you can see the Total Number of Repeats, Standards and Blanks affected by the settings.
If the pXRF settings are in use, a special indicator
will appear.
2. **Section 2 – Analyse pXRF Samples**
Here you can analyse pXRF samples. You can switch between “Elapsed” and “Live” time, select different Times (Time 1, Time 2, Time 3) presented on the charts, or select different chart.
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CRM - Certified Reference Material
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Basic Information
Please note that all pages under this section (CRMs) are affected by [Global Filters](#_Global_Filters)
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How we calculate Test
- **Action** : ABS( sysResult - ExpectedValue ) > 3 * ExpStdDev || sysResult >= ExpectedMax
- **Alert** : ABS( sysResult - ExpectedValue ) > 2 * ExpStdDev
- **OK** : Rest of Results
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How we calculate Blanks
If **Standard_Type **contains "**blank"** or "**blk".**
The size of the characters does not matter.
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Navigation
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Switch between Standards and Blanks
Using the buttons below, you can easily switch between Standards and Blanks.
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Filters
This page has some additional filters.
Filters from all 4 sections (below) will sync across all pages under CRMs.
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
- **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
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Statistics
Page shows total number of Standards or Blanks by DataSet, Standard Type, StandardID and Element. You can easily spot number of Standards or Blanks for each Element.
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CRM Summary
Summary page for Selected CRM Type (Standards or Blank) and specific Element. Only one element can be selected at the time.
Data presented below shows basic information like number of samples, min, max, expected standard deviation, expected min, expected max, expected value and status for each Standard and selected Element.
| | |
| --- | --- |
| StandardID | Standard Name – exactly as it is listed in tblSYSQCReference |
| Standard_Type | Standard type is used to group similar types of standards. For example BLANK for blank standards |
| Element | Element Name |
| Method | Assay method for the result listed in the expected results |
| ExpectedValue | Ideal Expected Assay Value for that Element |
| ExpectedMin | Expected acceptable minimum assay value |
| ExpectedMax | Expected acceptable maximum assay value |
| ExpectedStDev | Expect standard deviation |
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CRM Performance
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Standards by LabCode
Charts below present sysResult for all Samples (for selected Element and Standard ID / Standard Type) separated by LabCode.
It is a easy way to spot some patterns for each LabCode.
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Combined – Control Chart
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.
#### Additional Chart features.
Using the checkbox above you can show/hide additional support lines: Expected value, expected min and max, expected standard deviation calculated mean.
To show / hide specific Standard on the chart, please click on the Standard ID on the legend.
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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.
### > Macro (anchor)
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
### > Macro (anchor)
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)
# > Macro (anchor)
> Macro (anchor)
Lab Turnaround
Summary Table on the top presents:
- Total Batches
- Total Samples
- Avg DaysToLab
- Avg DaysAtLab
- Avg DayToMerge
by LabCode
On the lower section of the page, we can see different types of charts:
- **Jobs by Month & Year**
Shows Total number of Job and Average Days at Lab by Month & Year.
- **Lab Turnaround**
Average Days at Lab by Date (Year / Month / Day)
- **Batches & Samples by Date**
Total Number of Batches and Total Number of Samples by Date (Year / Month / Day)
- **Batches & Samples by LabCode**
Total Number of Samples and Total Number of Samples by LabCode
# Screen Test
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Basic Information
Please note that this page is affected by [Global Filters](#_Global_Filters)
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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 show individual samples by Sample Type.
x-axis is a random value to avoid overlapping points.
## > Macro (anchor)
Samples by Date
The bar chart shows Total Number of Samples by Date (Year / Month / Day).
Use button above to expand Data
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Samples by Sample Type
The bar chart shows Total Number of Samples by Sample Type.
Use button above to expand Data
## > Macro (anchor)
Table – Raw Data
# Weights
## > Macro (anchor)
Basic Information
Please note that this page is affected by [Global Filters](#_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.
Page has 4 sections:
1. **Section 1 – Main Filters**
Here you can limit number of samples to specific Element or Sample ID.
Multiple selection is allowed.
2. **Section 2 – Samples / Weights distribution.**
Here you can see how the data is distributed across different weight (kg)
3. **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**
## > Macro (anchor)
Raw Data Table
This tab has three sections / tables:
- **Samples by Batch_No**
Percentage of bad samples in each Batch. You can check Weights Distribution for specific Batch by clicking the row.
- **Samples by LabCode**
Percentage of bad samples by LabCode. You can check Weights Distribution for specific LabCode by clicking the row.
- **All Weights**
List of all samples
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