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DA chapter 1
Design and Analysis of Experiments Chapter 1
| Question | Answer |
|---|---|
| Experiment | A test or series of test in which purposeful changes are made to the input variables of a process or system so that we may observe and identify the reasons for changes that maybe observe and identify the reason for changes in the output response |
| Technology Commercialization | everyday electronics |
| Product Realization Activities | new product design and formulation, manufacturing process development, and process improvement |
| Robust | strong |
| Robust Process | a process affected minimally by external sources of variability |
| Sceintific (engineering) method | A body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating past knowledge |
| Mechanistic Models | models that follow directly from the physical mechanism e.g. Ohm's law. (understood so well, you can use math) |
| What is a vital part of the scientific method? | Experimentation |
| Empirical Models | Models made after observations and experimentation of a system. |
| Experimenter | person conducting the experiment |
| Objective of the experimenter | to determine the influences that controllable and uncontrollable factors have on the output response of the system |
| Strategy of experimentation | The general approach to planning and conducting the experiment |
| Best-guess approach | strategy of experimentation in which you randomly pick a list/series of factors to experiment |
| Disadvantages of Best-guess approach | Time consuming, there is no guarantee that the BEST solution will be found |
| One-factor-at-a-time approach | strategy of experimentation in which a baseline is set, and a single factor is experimented. |
| Disadvantage of One-factor-at-a-time approach | Fail to notice interactions between factors |
| Interaction | is the failure of one factor to produce the same effect on the response at different levels of another factor |
| Factorial experiment | the correct approach to dealing with several factors. factors are varied TOGETHER instead of one at a time |
| Fractional factorial experiment | a variation of the factorial design in which only a subset of the runs is used. |
| One-half fraction | the design requires only half of the original runs |
| Experimental design techniques early in process development can result in | improved process yields, reduced development time, reduced overall costs, reduced variability and closer conformance to nomial or targer requirements |
| Engineering design activities | where new products are developed and existing ones improved |
| Applications of experimental design in engineering design include | evaluation and comparison of basic design configurations, material alternatives, formulation of new prods, determ of key prod design parameters that impact prod perf. make the prod more robust. |
| Characterizing and screening experiment | to determine which factors (both controllable and uncontrollable) affect the output. used to identify the critical process factors and to determine the direction of adj for these factors to reduce/increase the (un)desirable outcome |
| Process variables | control variables |
| Optimize | to determine the region in the important factors that leads to the best possible response |
| Response surface methodology | to develop an empirical model of the process and obtain a more precise estimate of the optimum operating conditions |
| Computer model | a model that allows us to experiment on the key design parameters of the design without building it. |
| Mixture experiments | various ingredients are combined to form a diagnostic make up 100 percent of the mixture composition. biotech products, pharmaceuticals, paints and coatings, consumer products use this. |
| Statistical design of experiments | the process of planning the experiment so that appropriate data will be collected and analyzed by statistical methods, resulting in valid, objective conclusion |
| The two aspects to any experimental problem: | the design of the experiment and the statistical analysis of the date |
| The three basic principles of experimental design | Randomization, replication, blocking |
| Randomization | all variables/tests are independent from each other. no relations. |
| Complete randomization | absolutely random; all variables/tests are independent from each other. no relations. |
| Replication | an independent repeat of each factor combination |
| Replicate | a run |
| The two important properties of replication | 1. it allows the experimenter to obtain an estimate of experimental error.2. if the y is used to estimate the true mean, replication permits to obtain a more precise est. of the parameter. |
| The difference between replication and repeated measurements | measuring an objected more than once--repeated measurements. Replication reflects sources of variability both between runs and within runs |
| Blocking | design technique used to improve the precision among the factors of interest. used to reduce or eliminate the variability transmitted from nuisance factors |
| Nuisance factors | factors that may influence the experimental response but in which are not directly interested |
| Guidelines for desgning experiments | 1.Recognition of and statement of the problem 2.Selection of the response variable 3.Choice of factors, levels, and range 4.Choice of experimental design 5. Performing experiment 6. Stat analysis of data 7.Conclusions & Recommendations |
| Sequential approach | a series of smaller experiments, each with a specific objective |
| Recognition of and statement of the problem | First step. must make a clear statement of why experimenting (characterization? optimization? confirmation? discovery? stability or robustness?) |
| Selection of the response variable | pick a variable that really provides useful information about the process under study. Also gauge capability (measurement error)is an important factor |
| Choice of factors, levels and range | Classifying potential design factors and nuisance factors, specific levels, region of interest. |
| Design factors | the factors actually selected for study in the experiment |
| Held-constant factors | variables that may exert some effect on the response but for purpose of the present experiment, are not of interest so are held constant. has little effect |
| Allowed-to-vary factors | experimental units or the "materials" to which the design factors are applied are usually nonhomogeneous, yet we often ignore this unit-to-unit variability and rely on randomization to balance out any material or experimental unit effect.has little effect |
| Nuisance factors are often classified as | Controllable, uncontrollable, noise factors |
| Controllable nuisance factor | one whose levels may be set by the experimenter |
| If a nuisance factor is uncontrollable it can be | measured by an analysis procedure called the analysis of convariance can often be used to compensate for its effect |
| Noise factor | a factor that varies naturally and uncontrollably in the process can be controlled for purposes of an experiment. try to minimize it.` |
| Process of knowledge | a combination of practical experience and theoretical understanding |
| Cause-and-effect diagram (fishbone diagram) | useful technique for organizing some of the information generated in pre-experimental planning |
| Pre-experimental planning | steps 1-3. it is crucial to bring out all points of view and process information in steps 1-3. |
| Choice of experimental design | consideration of sample size, selection of a suitable run order for trials, determination of whether or not blocking or other randomization restrictions are involved. |
| Performing the experiment | vital to monitor the process carefully to ensure that everything is being done according to plan. errors will destroy experimental validity.do pilot runs. revisit steps 1-4 if necessary |
| Statistical analysis of the data | conclusions are objective rather than judgmental. often use graphical methods to interprete as well as empirical method for an equation. |
| Conclusions and recommendations | once the data have been analyzed, the experimenter must draw PRACTICAL conclusions about the results and recommend a course of action.Follow-up runs and confirmation testing should also be perf to validate the conclusions from experiment |
| How many eras in the modern development of stats experimental design have there been? | Four |
| The agricultural era was led by | Sir Ronald A. FIsher |
| The industrial era was led by | Box and Wilson |
| Late 1970s era was led by | Genichi Tagushi |
| This era | consists or aerospace manufactoring, electronics, semiconductors |
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