Median xl sigma 1.2.3 download files

Recherches scientifiques et technologiques pluridisciplinaires. Politique de la ville. Protection judiciaire de la jeunesse. Aide juridictionnlle qui transite par une association. Subvention de fonctionnement application de l'article L.

Acquisitions et commandes publiques dans le secteur des arts plastiques. Subvention de fonctionnement Accompagnement des public. Insertion sociale. Collecting new data means recording new observations it may involve looking at an existing metric but with new operational definitions.

Existing data is best used to establish historical patterns and to supplement new data. Select specific data and factors to be included 2. At each process step, the operator enters the appropriate data. The trade-off is faster data collection because you only have to sample vs. No time element. Ex: Customers, complaints, items in warehouse Process — Sampling from a changing flow of items moving through the business.

Has a time element. In contrast, quality and business process improvement tends to focus more often on processes, where change is a constant. Process sampling techniques are also the foundation of process monitoring and control. Sampling terms Sampling event — The act of extracting items from the population or process to measure.

Subgroup — The number of consecutive units extracted for measurement in each sampling event. Sampling Frequency — The number of times a day or week a sample is taken Ex: twice per day, once per week.

Applies only to process sampling. Ex: collecting VOC data from people you know, or when you go for coffee. Use a random number table or random function in Excel or other software, or draw numbers from a hat that will tell you which items from the population to select. The risk of bias comes when the selection of the sample matches a pattern in the process. To sample from a stable process… 1. Who will do it? Determine the minimum sample size see p. Making inferences about a population based on a sample of an unstable process is ill-advised.

Establish stability before making inferences. For additional details refer to Minitab Help. NOTE: Having uncalibrated measurement devices can affect all of these factors. Calibration is not covered in this book since it varies considerably depending on the device. Be sure to follow established procedures to calibrate any devices used in data collection. Be sure to represent the entire range of process variation. Good and Bad over the entire specification plus slightly out of spec on both the high and low sides.

Select 2 or 3 operators to participate in the study. Identify 5 to 10 items to be measured. Have each operator measure each item 2 to 3 times in random sequence. Gather data and analyze. See pp. Watch for unplanned influences. This takes into account variability due to the gage, the operators, and the operator by part interaction.

Part-to-Part: An estimate of the variation between the parts being measured. Specifically, the calculation divides the standard deviation of the gage component by the total observed standard deviation then multiplies by This chart shows the variation in the measurements made by each operator on each part.

Review control chart guidelines, pp. The control limits are determined by gage variance and these plots should show that gage variance is much smaller than variability within the parts.

By Part chart The By Part graph shows the data for the parts for all operators plotted together. It displays the raw data and highlights the average of those measurements.

This chart shows the measurements taken by three different operators for each of 10 parts. Whether the difference is enough to be significant depends on the allowable amount of variation. In this example, each of three operators measured the same 10 parts. The 10 data points for each operator are stacked. Whether that is significant will depend on the allowable level of variation. It is the best chart for exposing operator-and-part interaction meaning differences in how different people measure different parts.

This is not good and needs to be investigated. MSA: Evaluating bias Accuracy vs. If the answer is no, the measurement system is inaccurate.

Bias effects include: Operator bias — Different operators get detectable different averages for the same value. Instrument bias — Different instruments get detectably different averages for the same measurement on the same part. If instrument bias is suspected, set up a specific test where one operator uses multiple devices to measure the same parts under otherwise identical conditions.

Other forms of bias — Day-to-day environment , customer and supplier sites. Talk to data experts such as a Master Black Belt to determine how to detect these forms of bias and counteract or eliminate them.

Testing overall measurement bias 1. Assemble a set of parts to be used for the test. Calculate the difference between the measured values and the master value.

Test the hypothesis see p. In the boxplot below; the confidence interval overlaps the H0 value, so we cannot reject the null hypothesis that the sample is the same as the master value. MSA: Evaluating stability If measurements do not change or drift over time, the instrument is considered to be stable. Measurement System stability can be tested by maintaining a control chart on the measurement system see charts below.

In concept, the measurement system should be able to divide the smaller of the tolerance or six standard deviations into at least five data categories. A good way to evaluate discrimination graphically is to study a range chart. Ex: Rating features as good or bad, rating wine bouquet, taste, and aftertaste; rating employee performance from 1 to 5; scoring gymnastics The Measurement System Analysis procedures described previously in this book are useful only for continuous data.

When there is no alter-native—when you cannot change an attribute metric to a continuous data type—a calculation called Kappa is used. All differences are treated the same. Select sample items for the study. Have each rater evaluate the same unit at least twice. Calculate a Kappa for each rater by creating separate Kappa tables, one per rater.

See instructions on next page. Calculate a between-rater Kappa by creating a Kappa table from the first judgment of each rater. One rater with low repeatability skews the comparison with other raters. It means that these two raters grade the items differently too often.

Calculate these values manually for any set of continuous data if not provided by software. You will need these calculations for many types of statistical tools control charts, hypothesis tests, etc. You will rarely generate one by hand, but will see them often if you use statistical software programs.

Essential for evaluating the normality; recommended for any set of continuous data. Statistical term conventions The field of statistics is typically divided into two areas of study: 1 Descriptive statistics represent a characteristic of a large group of observations a population or a sample representing a population. Ex: Mean and standard deviation are descriptive statistics about a set of data 2 Inferential Statistics draw conclusions about a population based upon analysis of sample data.

A small set of numbers a sample is used to make inferences about a much larger set of numbers the population. However, a mean is required to calculate some of the statistical measures of variation.

To determine the median, arrange the data in ascending or descending order. The median is the value at the center if there is an odd number of data points , or the average of the two middle values if there is an even number of data points. In that instance, the median would be far more representative of the data set as a whole.

Range Range is the difference between the largest and smallest values in a data set. Variance for a population uses a sigma as shown here. That means, for example, that the total variance for a process can be determined by adding together the variances for all the process steps.

Do not add together the standard deviations of each step. A drawback to using variance is that it is not in the same units of measure as the data points. But as noted above, you CANNOT add standard deviations together to get a combined standard deviation for multiple process steps. You will be evaluating the distribution for normality see p. Ex: When dealing with data collected at different times, first plot them on a time series plot p.

If there are multiple occurrences of an observation, or if observations are too close together, then dots will be stacked vertically. Larger data sets use histograms see below and box plots see p. The groups represent non-overlapping segments in the range of data. Ex: All the values between 0. How to create a histogram 1. Take the difference between the min and max values in your observations to get the range of observed values 2. Having too many intervals will exaggerate the variation; too few intervals will obscure the amount of variation.

Count the number of observations in each interval 4. Create bars whose heights represent the count in each interval Interpreting histogram patterns Histograms and dot plots tell you about the underlying distribution of the data, which in turn tells you what kind of statistical tests you can perform and also point out potential improvement opportunities.

This usually indicates that there are two distinct pathways through the process. You need to define customer requirements for this process, investigate what accounts for the systematic differences, and improve the pathways to shift both paths towards the requirements.

The pattern is common with data such as time measurements where a relatively small number of jobs can take much longer than the majority. This type of patterns occurs when the data have an underlying distribution that is not normal or when measurement devices or methods are inadequate.

If a non-normal distribution is at work, you cannot use hypothesis tests or calculate control limits for this kind of data unless you take subgroup averages see Central Limit Theorem, p. Normal distribution In many situations, data follow a normal distribution bell- shaped curve.

To use these probabilities, your data must be random, independent, and normally distributed. However, they are better at detecting several kinds of special cause variation.

Review of variation concepts Variation is the term applied to any differences that occur in products, services, and processes. There are two types of variation: 1 Common cause—the variation due to random shifts in factors that are always present in the process. One that ALSO has special cause variation is said to be out of control. Note that there are different strategies for dealing with the two types of variation: To reduce common cause variation, you have to develop new methods for doing the work everyday.

To eliminate special cause variation, you have to look for something that was temporary or that has changed in the process, and find ways to prevent that cause from affecting the process again. Collect data and be sure to track the order in which the data were generated by the process.

Mark off the data units on the vertical y axis and mark the sequence 1, 2, 3… or time unit 11 Mar, 12 Mar, 13 Mar… on the horizontal X axis. Plot the data points on the chart and draw a line connecting them in sequence.

If this is the case… 4. Determine the median see p. Count the number of points not on the median. Circle then count the number of runs. Use the Run Chart Table next page to interpret the results. Control limits are based on data and tell you how a process is actually performing. Spec limits are based on customer requirements and tell you how you want a process to perform.

See below for more details on selecting charts for continuous data and see p. Control charts for continuous data In most cases, you will be creating two charts for each set of continuous data. The first chart shows the actual data points or averages, the second chart shows the ranges or standard deviations.

Why use both? You can often do a quick chart by hand then use it to build a different or more elaborate chart later. It is also more sensitive than the ImR to process shifts. Convert attribute data to length, area, volume, etc. Add a measure for leading indicators such as days between near misses.

Control limit formulas for continuous data The constants in these formulas will change as the subgroup size changes see second table on next page. Determine sampling plan 2. Take a sample at each specified time or production interval 3. Plot the data the original data values on one chart and the and moving ranges on another 5.

After 20 or more sets of measurements, calculate control limits for moving Range chart 6. If the Range chart is not in control, take appropriate action 7.

If the Range chart is in control, calculate control limits for the Individuals chart 8. If the Individuals chart is not in control, take appropriate action Creating ,R charts or ,S charts 1. Determine an appropriate subgroup size and sampling plan 2.

Collect the samples at specified intervals of time or production 3. Calculate the mean and range or standard deviation for each subgroup 4. Plot the data. The subgroup means go on one chart and the subgroup ranges or standard deviations on another 5.

After 20 or more sets of measurements, calculate control limits for the Range chart 6. If the Range chart is in control, calculate control limits for the Xbar chart 8. If the sample size varies use the u- chart. In contrast, charts for attribute data use only the chart of the count or percentage.

Determine an appropriate sampling plan 2. Collect the sample data: Take a set of readings at each specified interval of time 3. Calculate the relevant metric n, np, c, or u 4. Calculate the appropriate centerline 5. Plot the data 6. After 20 or more measurements, calculate control limits 7.

Check your R-chart to rule out increases in variation. Small trends will be signaled by this test before the first test.

Any two out of three points provide a positive test. Any four out of five points provide a positive test. The proportion of current values that fall inside specification limits tells us whether the process is capable of meeting customer expectations. When to use process capability calculations Can be done on any process that has a specification established, whether manufacturing or transactional, and that has a capable measuring system. Check with data experts in your company to see what standards they follow.

Refer to any good statistics textbook for capability analysis on attribute data. The choice: Cp vs. The calculations include the mean, so are best used when the mean is not easily adjusted. If unacceptable, implement fixes. If acceptable, then run a long-term capability analysis. Repeat customers, after all, experience the long-term capability of the process. Evaluating results against written specifications when people are using unwritten specifications can lead to false conclusions.

The tools in this chapter fall into two very different categories: a Tools for identifying potential causes starts below are techniques for sparking creative thinking about the causes of observed problems. Here the emphasis is on rigorous data analysis or specific statistical tests used to verify whether a cause-and-effect relationship exists and how strong it is.

Part A: Identifying potential causes Purpose of these tools To help you consider a wide range of potential causes when trying to find explanations for patterns in your data. Your team should simply review any of those charts created as part of your investigative efforts.

You can then focus your cause-identification efforts on the areas where your work will have the biggest impact. Very quick and focused. Encourages broad thinking. Similar in function to a fishbone diagram, but more targeted in showing the input-output linkages. Collect data on different types or categories of problems.

Tabulate the scores. Also determine the counts or impact for each category. Sort the problems by frequency or by level of impact. Draw a vertical axis and divide into increments equal to the total number you observed. Draw bars for each category, starting with the largest and working down. Convert the raw counts to percentages of the total, then draw a vertical axis on the right that represents percentage.

Plot a point above the first bar at the percentage represented by that bar, then another above the second bar representing the combined percentage, and so on. Connect the points. Interpret the results see next page.

When possible, construct two Pareto charts on a set of data, one that uses count or frequency data and another that looks at impact time required to fix the problem, dollar impact, etc. You may end up targeting both the most frequent problems and the ones with the biggest impact.

Select any cause from a cause-and-effect diagram, or a tall bar on a Pareto chart. Make sure everyone has a common understanding of what that cause means. Why 2 3. Why 3 4. Sometimes you may reach a root cause after two or three whys, sometimes you may have to go more than five layers down. Name the problem or effect of interest. Be as specific as possible. Decide the major categories for causes and create the basic diagram on a flip chart or whiteboard.

Brainstorm for more detailed causes and create the diagram. See 5 Whys, p. Review the diagram for completeness. Discuss the final diagram. Identify causes you think are most critical for follow-up investigation. This will help you keep track of team decisions and explain them to your sponsor or other advisors.

Develop plans for confirming that the potential causes are actual causes. Identify key customer requirements outputs from the process map or Voice of the Customer VOC studies. This should be a relatively small number, say 5 or fewer outputs. List the outputs across the top of a matrix. Assign a priority score to each output according to importance to the customer. Identify all process steps and key inputs from the process map.

List down the side of the matrix. Cross-multiply correlation scores with priority scores and add across for each input. Create a Pareto chart and focus on the variables relationships with the highest total scores. Especially focus on those where there are acknowledged performance gaps shortfalls.

Part B: Confirming causal effects and results Purpose of these tools To confirm whether a potential cause contributes to the problem. The tools in this section will help you confirm a cause-and-effect relationship and quantify the magnitude of the effect.

In such cases, try confirming the effect by creating stratified data plots p. However, there are times when more rigor, precision, or sophistication is needed.

The basic statistical calculations for determining whether two values are statistically different within a certain range of probability. The choice depends in part on what kinds of data you have see table below. It is an excellent choice whenever there are a number of factors that may be affecting the outcome of interest, or when you suspect there are interactions between different causal factors.

See stratification factors, p. Collect the stratification information at the same time as you collect the basic data 3.

Option 2: Color code or use symbols for different strata This chart uses symbols to show performance differences between people from different work teams. Training seems to have paid off for Team D all its top performers are in the upper right corner ; Team C has high performers who received little training they are in the lower right corner.

Confirm the potential cause you want to experiment with, and document the expected impact on the process output. Develop a plan for the experiment. Present your plan to the process owner and get approval for conducting the experiment. Train data collectors. Alert process staff of the impending experiment; get their involvement if possible.

Conduct the experiment and gather data. Analyze results and develop a plan for the next steps. Were problems reduced or eliminated?

Multiple genes contribute to anhydrobiosis tolerance to extreme desiccation in the nematode Panagrolaimus superbus. Genet Mol Biol. Mol Biol Evol. Loss of the HIF pathway in a widely distributed intertidal crustacean, the copepod Tigriopus californicus. Battaglia M, Covarrubias AA. Front Plant Sci. Identification of a novel LEA protein involved in freezing tolerance in wheat.

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Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. Swiss-Prot: juggling between evolution and stability. Brief Bioinform. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. KAAS: an automatic genome annotation and pathway reconstruction server.

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Interactive tree of life iTOL : an online tool for phylogenetic tree display and annotation. Probability-based protein identification by searching sequence databases using mass spectrometry data. FoldIndex: a simple tool to predict whether a given protein sequence is intrinsically unfolded. Download references. Ii IAB for collecting E. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

You can also search for this author in PubMed Google Scholar. T and K. A performed genome sequencing. The study was supervised by K. The manuscript was written by Y. U and K. A, and all authors reviewed and approved the final manuscripts.

Correspondence to Kazuharu Arakawa. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Blobplot analysis of the E. The assembled genome was subjected to BlobTools for possible contamination identification and visualize as blopblot. This information was analyzed by BlobTools.

The assembled transcriptome was subjected to BlobTools for possible contamination identification and visualize as blobplot. Phylogenetic tree of duplicated genes. These phylogenetic trees contain each gene of R. Bootstraps are showed under the branch. M-A plot of DEGs and plot of fold change-gene expression in anhydrobiosis state.

The vertical axis shows log2 fold-change and the horizontal axis shows log10 baseMean, with positive change indicating up-regulated genes and a negative change indicating ting the down-regulated genes.

The vertical axis shows expression level Transcript per million; TPM in anhydrobiosis state and the horizontal axis shows fold change between active and anhydrobiosis state.

Screening of genome assembly. Table S2. Table S3. Overview of transcriptome assembly. Table S4. Gene conservation of anhydrobiosis related genes. Table S5.

Annotation, expression level, and fold index of DEGs. Table S6. Table S7. Gene list of tardigrade-specific anhydrobiosis related candidate. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and Permissions. Murai, Y. Multiomics study of a heterotardigrade, Echinisicus testudo , suggests the possibility of convergent evolution of abundant heat-soluble proteins in Tardigrada. BMC Genomics 22, Download citation. Received : 20 November Accepted : 28 October Published : 11 November Anyone you share the following link with will be able to read this content:.

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