Rang control chart

These can be estimated fom the repeatability with dflerent matrices (range control chart)... 

If we don t have such an ideal control sample, but only one with a matrix different from the routine sample (e.g. a standard solution) than we have to consider also the uncertainty component arising from changes in the matrix. For this purpose we use the (repeatability) standard deviation calculated from repeated measurements of our routine samples (performed e.g. for a range control chart). When we estimate the reproducibility within laboratory we now have to combine both contributions by calculating the square root of the sum of squares. 

It is possible only to estimate uncertainty components f om repeatability ia the range control chart... 

We can convert all types of classical control chart (X-chart, blank value, recovery, range control chart etc.) into target control charts. 

Figure 19-18 Shewhart mean and range control charts.

For the estimation of trueness, ENV-ISO 13530 recommends regular participation in external quality procedures such as interlaboratory trials and proficiency schemes for the control of trueness (bias). For internal routine action, the use of control charts, based on the mean, spiking recovery, and analysis of blanks, is recommended. In addition, the standard recommends the use of a mean and/or a range control chart and the execution of a minimum of six replicate determinations of the test sample for the calculation of the standard deviation for the control of the precision. 

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Determine the characteristics of the mean and range control charts for a process in which the target value is 57, the process capability is 5, and the sample size is 4. 

Range Control Chart Without Tab - Overall Width... 

Some measure of dispersion of the subgroup data should also be plotted as a parallel control chart. The most reliable measure of scatter is the standard deviation. For small groups, the range becomes increasingly significant as a measure of scatter, and it is usually a simple matter to plot the range as a vertical line and the mean as a point on this line for each group of observations. 

Control charts were originally developed in the 1920s as a quality assurance tool for the control of manufactured products.Two types of control charts are commonly used in quality assurance a property control chart in which results for single measurements, or the means for several replicate measurements, are plotted sequentially and a precision control chart in which ranges or standard deviations are plotted sequentially. In either case, the control chart consists of a line representing the mean value for the measured property or the precision, and two or more boundary lines whose positions are determined by the precision of the measurement process. The position of the data points about the boundary lines determines whether the system is in statistical control. 

Constructing a Precision Control Chart The most common measure of precision used in constructing a precision control chart is the range, R, between the largest and smallest results for a set of j replicate analyses on a sample. 

To construct the control chart, ranges for a minimum of 15-20 samples (preferably 30 or more samples) are obtained while the system is known to be in statistical control. The line for the average range, R, is determined by the mean of these n samples... 

Construct a precision control chart using the following 20 ranges, each determined from a duplicate analysis of a 10-ppm calibration standard... 

Example of the use of subrange precision control charts for samples that span a range of analyte concentrations. The precision control charts are used for... 

Finally, the laboratory expends significant effort communicating results to both internal and external customers. Production, quaUty assurance, and purchasing all have various information needs ranging from the simple pass /fail decisions to statistical summaries of the data and suppHer product quahty. Customers expect to receive lot analyses in the form of a COA and often also want their own product-specific information on the document as well. This information can automatically be appHed to the COA if entered into the LIMS. Often, a quaUty-conscious customer wants information about the product in the form of process capabiUty or control charts. Using LIMS, these charts can be provided on demand. 

One can apply a similar approach to samples drawn from a process over time to determine whether a process is in control (stable) or out of control (unstable). For both kinds of control chart, it may be desirable to obtain estimates of the mean and standard deviation over a range of concentrations. The precision of an HPLC method is frequently lower at concentrations much higher or lower than the midrange of measurement. The act of drawing the control chart often helps to identify variability in the method and, given that variability in the method is less than that of the process, the control chart can help to identify variability in the process. Trends can be observed as sequences of points above or below the mean, as a non-zero slope of the least squares fit of the mean vs. batch number, or by means of autocorrelation.106... 

Here the concentration range of the analyte in the ran is relatively small, so a common value of standard deviation can be assumed. Insert a control material at least once per ran. Plot either the individual values obtained, or the mean value, on an appropriate control chart. Analyse in duplicate at least half of the test materials, selected at random. Insert at least one blank determination. 

Statistical process control charts (such as the x-bar and range charts) plot measurements as a function of time [Grant and Leavenworth (1988)]. With reference to the current day, what part of these charts approximates an enumerative study What part of these charts approximates an analytic study Are the parts different Are the uses different ... 

Figure 10.2 Statistical process control charts for clearings. Top panel runs chart showing clearings as a function of measurement number. Middle panel x-bar chart with dashed upper control limit (UCL) and lower control limit (LCL) solid horizontal line is the grand mean, X. Bottom panel range chart with dashed upper control limit (UCL) solid horizontal line is the average range, r.

Often analyses are carried ont more than once. In this case the range between the repeated measnrements can also be nsed in a control chart. 

The Difference Chart is very similar to the range chart bnt it nses the difference between donble measurements together with its sign. The sample is measnred at the beginning and at the end of a series. The difference between the two measnrements is marked in the control chart with its sign. 

The chart is constracted with an upper and lower limit. A pre-period is not necessary. The target control chart of the range and in some cases also of the blank only needs the upper limit. 

Repeat chromatograms in GPC should agree within 2%. However, the chromatogram is sensitive to such experimental conditions as (a) resolution of the columns, (b) range of porosities of the column packings, (c) flow rate of solvent, and (d) age of detectors. It is recommended that the laboratory use control charts to determine the optimal conditions of the instrument. 

Control charts based on variable sample data include the x chart and the. v chart. When dealing with a numerically measurable quality characteristic, the x chart is usually employed to monitor the process average and the s chart is used to monitor the process variability. When there is only one observation in each sample, the individual measurement chart (I chart) and moving range chart (MR chart) are used to monitor the process average and variability. It should be noted that due to the poor... 

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