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Design expert manual pdf free
The Design Wizard will ask a series of questions to quickly provide a design to accomplish the goal of the experiment, while staying within budget limits. It is intended to insulate experimenters that are new to Design-Expert from the myriad of design choices, and act as an informative guide. This feature can also benefit the experienced DOE practitioner. Experienced users of the software can start by building a New Design or loading an existing design with the Open Design button.
Searchable, context-sensitive help can be called by clicking the question mark icon or F1 on the keyboard. It provides assistance on what to do next. Click on a number in a cell of the report and right-click in the cell to request help to learn more about its contents.
Screen tips provide instructions for what to do or what to look for on the current screen. Stat-Ease Tutorials are available online and included in the installer. Use the tutorials to get more familiar with design of experiments DOE.
This includes building , analyzing, and interpreting the results of a designed experiment. Stat-Ease Academy offers a dynamic selection of on-demand, online training. All content is free to access. Check back regularly as our offerings may change.
Stat-Ease Training provides comprehensive instructor-led workshops.
Design expert manual pdf free. Design-Expert 5.0 Reference Manual – Statease.info
Need an account? Click here to sign up. Download Free PDF. Ganesh Kumar. A short summary of this design expert manual pdf free. Download Download PDF. Translate PDF. This class of designs is design expert manual pdf free at process optimization.
A case study provides a real- life feel to the exercise. If you are in a design expert manual pdf free to get the gist on design and analysis of RSM, hop past all the sidebars. If you have design expert manual pdf free completed all these tutorials, consider doing so before starting this one.
We will presume that you are knowledgeable of the statistical aspects of RSM. Call Stat-Ease or visit our website for a schedule at www. The case study in this tutorial involves production of a chemical. The experimenter chose three process factors to study.
Their names and levels are shown in the following table. The stars represent axial points. How far out from the cube these should go is a matter for much discussion between statisticians. As you will see, Design- Expert offers a variety of options for alpha. Twelve runs: composed of eight factorial points, plus four center points.
Eight runs: composed of six axial star points, plus two more center points. Design the Experiment Start the program by finding and double clicking the Design-Expert software icon.
Welcome screen Press Design expert manual pdf free источник the welcome screen. Now go back and re-select Central Composite design. Click the down design expert manual pdf free in the Numeric Factors entry box and Select 3 as shown design expert manual pdf free. Notice that it defaults to a Rotatable design with the axial star points set at 1.
Press OK to accept the rotatable value. Using the information provided in the table on page 1 of this tutorial or on the screen capture belowtype in the details for factor Name A, B, CUnits, and Нажмите чтобы увидеть больше and High levels. Now return to the bottom of the central composite design form. You will need two blocks for this design, one for each day, so click design expert manual pdf free Blocks field and select 2.
You now have the option of identifying Block Names. Enter Day 1 and Day 2 as shown below. Block names Press Continue to enter Responses.
Select 2 from the pull down list. Now enter the response Name and Units for each response as shown below. Completed response form At any time in the design-building phase, you can return to the previous page by pressing посмотреть еще Back button.
Then you can revise your selections. Press Continue to view the design layout your run order may differ due to randomization. Click the Tips button for a refresher.
Click the File menu item and select Save As. Obviously at this stage the responses must be entered into Design-Expert. We see no benefit to making you type all the numbers, particularly with the potential confusion due to differences in randomized run orders. Click Open to load the data. Move your cursor to Std column header and right-click to bring up a menu from which to select Sort Ascending this could also be done via the View menu.
Notice how the factorial points align only to the Day 1 block. Then in Day 2 the axial points are run. Center points are divided between the two blocks. Unless you change the default setting for the Select option, do not expect the Type column to appear the next time you run Design-Expert. It is only on temporarily at this stage for your information. Before focusing on modeling the response as a function of the factors varied in this RSM experiment, it will be good to assess the impact of the blocking via a simple scatter plot.
You should see a scatter plot with factor A:Time on the X-axis and the Conversion response on перейти Y-axis. Block versus run or, conversely, run vs autodesk 2019 hardware free is also highly correlated due to this restriction in randomization runs having to be done for day 1 before day 2. It is good to see so many white squares because these indicate little or no correlation between factors, thus they can be estimated independently.
For now it is most useful to produce a plot showing the impact of blocks because this will be literally blocked out in the analysis.
Therefore, on the floating Graph Columns tool click the button where Conversion intersects with Block as shown below. Plotting the effect of Block on Conversion The graph shows a slight correlation 0. Whether this is something to design expert manual pdf free concerned about would be a matter of judgment by the experimenter. However it may in this case be such a slight difference that it merits no further discussion.
Bear in mind that whatever the difference may be it will be filtered out mathematically so as not to bias the estimation of factor effects. Changing response resulting graph not shown Finally, to see how the responses correlate with each other, change the X Axis to Conversion.
For example, choose Color by Block to see which points were run in block 1 black and block 2 red. Under the Analysis branch click the node design expert manual pdf free Conversion. A new set of tabs appears at the top design expert manual pdf free your screen.
They are arranged from left to right in the order needed to complete the analysis. What could be simpler? Click Tips for details. For now, accept the default transformation design expert manual pdf free of None. Now click the Fit Summary tab.
At this point Design-Expert fits linear, two-factor interaction 2FIquadratic, and cubic polynomials to the response. By design, the central composite matrix provides too few unique design points to determine all the terms in the cubic model. Next you will see several extremely useful tables for model selection. Each table is discussed briefly via sidebars in this tutorial on RSM. So far, Design-Expert is indicating via underline the quadratic model looks best — these terms acdsee ultimate 10 (64-bit) key free download significant, but adding the cubic order terms will not significantly improve the fit.
Use the handy Bookmarks tool to advance to the next table for Lack of Fit tests on the various model orders. The quadratic model, identified earlier as the likely model, does not show significant lack of fit. Remember that the cubic model is aliased, so it should not be chosen. Always confirm this suggestion by viewing these tables. Design-Expert allows you to select a model for in-depth statistical study. Click the Model tab at the top of the screen to see the terms in the model.
Be sure to try this in the rare cases when Design-Expert suggests more than one model. The options for process order At this stage you could make use of the Add Term feature.
Also, you could now manually reduce the model by clicking off insignificant effects. For example, you will see in a moment that several terms design expert manual pdf free this case are marginally significant at best. You can also see probability values for each individual term in the model. You may want to consider removing terms with probability values greater than 0. Use process knowledge to guide your decisions.
The R-Squared statistics are very good — near to 1. Post-ANOVA statistics Press forward to For windows free download to bring the following details to your screen, including the mean effect-shift for each block, that is, the difference from Day 1 to Day 1 in the response. Block terms are left out. These terms can be used to re-create the results of this experiment, but they cannot be used for modeling future responses.
However, you can copy and paste the data to your favorite Windows word processor or spreadsheet. This might design expert manual pdf free handy for client who are phobic about statistics. The most important diagnostic — normal probability plot of the residuals — appears by default.
Lecture6-Design Expert Software – Tutorial | PDF | Design Of Experiments | Experiment
Stat-Ease Tutorials are available online and included in the installer. Use the tutorials to get more familiar with design of experiments DOE.
This includes building , analyzing, and interpreting the results of a designed experiment. Stat-Ease Academy offers a dynamic selection of on-demand, online training. All content is free to access. Check back regularly as our offerings may change. Stat-Ease Training provides comprehensive instructor-led workshops. You can click it also — it is interactive!
In this case, at the center point, you see that factor A time produces a relatively small effect as it changes from the reference point. Therefore, because you can only plot contours for two factors at a time, it makes sense to choose B and C — and slice on A. Start by clicking Contour on the floating Graphs tool. Then in the Factors Tool right click the Catalyst bar and select X1 axis by left clicking it. Making factor C the x1-axis You now see a catalyst versus temperature plot of conversion, with time held as a constant at its midpoint.
Contour plot of B:temperature versus C:catalyst Design-Expert contour plots are highly interactive. For example, right-click up in the hot spot at the upper middle and select Add Flag. Right click and Delete flag to clean the slate.
Deleting the flag 3D surface plot Now to really get a feel for how the response varies as a function of the two factors chosen for display, select from the floating Graphs Tool the 3D Surface. You then will see three- dimensional display of the response surface.
If the coordinates encompass actual design points, these will be displayed. On the Factors Tool move the slide bar for A:time to the right. This presents a very compelling picture of how the response can be maximized. Right click at the peak to set a flag. Move your mouse over the graph. Seeing a point beneath the surface See an actual result predicted so closely lends credence to the model. Things are really looking up at this point! Rotation tool Move your cursor over the tool.
The pointer changes to a hand. Now use the hand to rotate the vertical or horizontal wheel. Whether you use the rotation tool or simply grab the plot with your mouse, watch the 3D surface change. Notice how the points below the surface are shown with a lighter shade. The Stat-Ease program developers thought of everything! Before moving on from here, go back to the Rotation tool and press Default to put the graph back in its original angle. Analyze the data for the second response, activity.
Be sure you find the appropriate polynomial to fit the data, examine the residuals and plot the response surface. Hint: The correct model is linear. Design-Expert will save your models. To leave Design-Expert, use the File, Exit menu selection. You should go back to that tutorial if you’ve not completed it. For details on optimization, see our on-line program help. Call or visit our web site for information on content and schedules. In this section, you will work with predictive models for two responses, yield and activity, as a function of three factors: time, temperature, and catalyst.
These models are based on results from a central composite design CCD on a chemical reaction. Click the open design icon see below and load the case study data modeled by Stat-Ease and saved to a file named RSM-a.
Open design icon To see a description of the file contents, click the Summary node under the Design branch at the left of your screen. Within the design status screen you can see we modeled conversion with a quadratic model and activity with a linear model, as shown below. You can also re-size columns with your mouse. Click on the Coefficients Table node at the bottom branch. For instance, notice that the coefficient for AC This shows, for the region studied, that the AC interaction influences conversion more than Factor B.
In our example, we chose to use the full quadratic model. Therefore, some less significant terms shown in black are retained, even though they are not significant at the 0. Right click any cell to export this report to PowerPoint or Word for your presentation or report. Check it out: This is very handy!
Under the Optimization branch to the left of the screen, click the Numerical node to start. We will detail POE later. The program restricts factor ranges to factorial levels plus one to minus one in coded values — the region for which this experimental design provides the most precise predictions.
Response limits default to observed extremes. In this case, you should leave the settings for time, temperature, and catalyst factors alone, but you will need to make some changes to the response criteria.
Desirabilities range from zero to one for any given response. The program combines individual desirabilities into a single number and then searches for the greatest overall desirability. A value of one represents the ideal case. A zero indicates that one or more responses fall outside desirable limits. For this tutorial case study, assume you need to increase conversion. Click Conversion and set its Goal to maximize. As shown below, set Lower Limit to 80 the lowest acceptable value, and Upper Limit to , the theoretical high.
Conversion criteria settings You must provide both these thresholds so the desirability equation works properly. By default, thresholds will be set at the observed response range, in this case 51 to Otherwise we may come up short of the potential optimum. Now click the second response, Activity.
Enter Lower Limits and Upper Limits of 60 and 66, respectively. Values outside that range are not acceptable. Activity criteria settings The above settings create the following desirability functions: 1.
Close out Screen Tips by pressing X at the upper-right corner of its screen. Weights give added emphasis to upper or lower bounds or emphasize target values. With a weight of 1, di varies from 0 to 1 in linear fashion.
Weights greater than 1 maximum weight is 10 give more emphasis to goals. Weights less than 1 minimum weight is 0. Try pulling the square on the left down and the square on the right up as shown below. Before moving on from here, re- enter the Lower and Upper Weights at their default values of 1 and 1; respectively. If you want to emphasize one over the rest, set its importance higher. By leaving all importance criteria at their defaults, no goals are favored over others.
Then click Contents. From here you can open various topics and look for any details you need. Now click the Options button to see what you can control for the numerical optimization.
After doing your first search for the optimum, go back to this Option and slide it one way and the other. Observe what happens to the solutions presented by Design-Expert. If you move the Filter bar to the right, you decrease the number. Conversely, moving the bar to the left increases the solutions. Click OK to close Optimization Options. Running the optimization Start the optimization by clicking the Solutions tab.
It defaults to the Ramps view so you get a good visual on the best factor settings and the desirability of the predicted responses. Numerical Optimization Ramps view for Solutions Your results may differ The program randomly picks a set of conditions from which to start its search for desirable results — your results may differ.
Multiple cycles improve the odds of finding multiple local optimums, some of which are higher in desirability than others. Due to random starting conditions, your results are likely to be slightly different from those in the report above. The colored dot on each ramp reflects the factor setting or response prediction for that solution.
The height of the dot shows how desirable it is. Press the different solution buttons 1, 2, 3,… and watch the dots. They may move only very slightly from one solution to the next. However, if you look closely at temperature, you should find two distinct optimums, the first few near 90 degrees; further down the solution list, others near 80 degrees.
You may see slight differences in results due to variations in approach from different random starting points. For example, click the last solution on your screen. Does it look something like the one below?
Second optimum at lower temperature, but conversion drops, so it is inferior If your search also uncovered this local optimum, note that conversion falls off, thus making it less desirable than the higher-temperature option.
The Solutions Tool provides three views of the same optimization. Drag the tool to a convenient location on the screen. Click the Solutions Tool view option Report. Desirability A:time 1 B:temperature 1 C:catalyst 1 Conversion 0. Optimization Graphs Press Graphs near the top of your screen to view a contour graph of overall desirability.
On the Factors Tool palette, right-click C:Catalyst. Make it the X2 axis. Temperature then becomes a constant factor at 90 degrees. Design-Expert software sets a flag at the optimal point. To view the responses associated with the desirability, select the desired Response from its droplist. Take a look at the Conversion plot. Then go to Surface Graphs and click Show contour grid lines. Show contour grid lines option Grid lines help locate the optimum, but for a more precise locator right-click the flag and Toggle Size to see the coordinates plus many more predicted outcome details.
To get just what you want on the flag, right-click it again and select Edit Info. Flag size toggled to see select detail By returning to Toggle size, you can change back to the smaller flag. If you like, view optimal activity response as well. To look at the desirability surface in three dimensions, again click Response and choose Desirability. Then, on the floating Graphs Tool, press 3D Surface. Next select View, Show Rotation and change horizontal control h to Press your Tab key or click the graph.
What a spectacular view! In other words, the solution is relatively robust to factor C. Do this by pressing the Default button Surface Graphs and any other Graph Preference screens you experimented on. Design-Expert offers a very high Graph resolution option. Try this if you like, but you may find that the processing time taken to render this, particularly while rotating the 3D graph, can be a bit bothersome.
This, of course, depends on the speed of your computer and the graphics-card capability. To see a broader operating window, click the Graphical node.
You need not enter a high limit for graphical optimization to function properly. Graphical optimization: Conversion criteria Click Activity response. If not already entered, type in 60 for the Lower Limit and 66 for the Upper Limit. Notice that regions not meeting your specifications are shaded out, leaving hopefully!
Temperature then becomes a constant factor at 90 degrees as before for Solution 1. This Design-Expert display may not look as fancy as 3D desirability but it can be very useful to show windows of operability where requirements simultaneously meet critical properties. Shaded areas on the graphical optimization plot do not meet the selection criteria.
This provides a measure of uncertainty on the boundaries predicted by the models — a buffer of sorts. Confidence intervals CI superimposed on operating window After looking at this, go back and turn off the intervals to re-set the graph to the default settings. If you are subject to FDA regulation and participate in their quality by design QBD initiative, the CI-bounded window can be considered to be a functional design space, that is, a safe operating region for any particular unit operations.
However, to establish a manufacturing design space on must impose tolerance intervals. This tutorial experiment provided too few runs to support imposition of TIs. What will this do to the operation window? Find out by dragging the 80 conversion contour until it reaches a value near Then right-click it and Set contour value to 90 on the nose. Changing the conversion specification to 90 minimum It appears that the more ambitious goal of 90 percent conversion is feasible.
This requirement change would make the lower activity specification superfluous as evidenced by it no longer being a limiting level, that is, not a boundary condition on the operating window. Graphical optimization works great for two factors, but as factors increase, optimization becomes more and more tedious.
You will find solutions much more quickly by using the numerical optimization feature. Then return to the graphical optimization and produce outputs for presentation purposes. Response Prediction and Confirmation This feature in Design-Expert software allows you to generate predicted response s for any set of factors.
To see how this works, click on the Point Prediction node lower left on your screen. Click the Point Prediction node left on your screen. Notice it now defaults to the first solution. Be thankful Design-Expert programmers thought of this, because it saves you the trouble of dialing it up on the Factors Tool. Go ahead and play with them now if you like. You can either move the slider controls, or switch to the Sheet view and enter values.
Take a moment now to study the screen tips on all the statistical intervals that come up when you press the light- bulb icon. Confirmation After finding the optimum settings based on your RSM models, the next step is to confirm that they actually work.
To do this, click the Confirmation node left side of your screen. You might be surprised at the level of variability, but it will help you manage expectations. Note: block effects, in this case day-by-day, cannot be accounted for in the prediction. Of course you would not convince many people by doing only one confirmations run. Doing several would be better. Go to the Confirmation Tool and enter for n the number 3.
Click the Enter Data option and type for Activity 62, 63 and Entering confirmation run results Notice that the prediction interval PI narrows as n increases. Does the Data Mean displayed in red fall within this range? If so, the model is confirmed. Observe the diminishing returns in terms of the precision, that is, the PI approaches a limit — the confidence interval CI that you saw in Point Prediction.
The CI is a function of the number of experimental runs from which the model is derived. That is done is this stage, so one can only go so far with the number of confirmation runs.
Perhaps half a dozen of these may suffice. If you are not worn out yet, you will need this file in Part 3 of this series of tutorials. Summary Numerical optimization becomes essential when you investigate many factors with many responses. It provides powerful insights when combined with graphical analysis. However, subject-matter knowledge is essential to success. For example, a naive user may define impossible optimization criteria that results in zero desirability everywhere!
To avoid this, try setting broad acceptable ranges. Narrow them down as you gain knowledge about how changing factor levels affect the responses. Move on to the next tutorial on advanced topics for more detailing of what the software can do. If you want to learn more about response surface methods not the software per se , attend our Stat-Ease workshop Response Surface Methods for Process Optimization. We appreciate your questions and comments on Design-Expert software.
E-mail these to stathelp statease. Then under the Analysis branch click the R1:Conversion node and go to Model Graphs to bring up the contour plot. In the vacant region of the AB contour plot right click and select Add contour.
Then drag the contour around it will become highlighted. You may get two contours from one click like those with the same response value shown below. This pattern indicates a shallow valley, which becomes apparent when we get to the 3D view later. Adding a contour Click the new contour line to highlight it. Then drag it place the mouse cursor on the contour and hold down the left button while moving the mouse to as near to 81 as you can.
Now to obtain the precise contour level, right-click the contour you just dragged, choose Set contour value and enter Then choose Contours. Now select the Incremental option and fill in Start at 66, Step at 3, and Levels at 8. That gives you a clue on where to start and how big to step on the contour values.
Zooming in on a region of interest by roping off a box Notice how the graph coordinates change. Obviously you would now want to add more contours using the tools you learned earlier in this tutorial.
However, do not spend time on this now: Right-click over the graph and select Default View Window. On the Graphs Tool go to 3D Surface view. Change the Low to 80 and the High to Notice how this makes the graph far more colorful and thus informative on the relative heights.
Edit Legend dialog box to change the color gradient Now click the design point sticking up in the middle. See how this is identified in the legend at the left by run number and conditions. On the Factors Tool select off the Run down-list number 1. However the colors are not ideal now. So right-click over the gradient and in the Edit Legend dialog box press the Defaults button. Your graph should now match the one shown below. This can be very useful to document unusual happenings during any given run.
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