Designed as a pin-to-pin compatible replacement for the TMP, the TMP provides bits of temperature data with each reading, translating to a temperature resolution of 7.

The evaluation module and GUI are designed to provide the user with a quick setup to evaluate the TMP and gain familiarity with the device down to the bit-by-bit register level. Transducer compatibility is expanded to open- or closed-top transducers operating between kHz and kHz.

The receiver can be tuned to filter and process returned echo data for post-measurement analysis. A 5-V 2. TI recommends purchasing an external power supply that complies with applicable regional safety standards. The evaluation board includes onboard emulation for programming and debugging, onboard buttons, and LEDs for quick integration of a simple-user interface.

The standard pin BoosterPack headers make the evaluation board compatible with a wide variety of MCU LaunchPad development kits and enable easy prototyping. These sensors change their output states as magnets reverse polarity or magnetic fields reach certain thresholds to trigger the sensor logic.

Prices Specs. Refresh Prices. Refresh Prices Manufacturer Page. Features: Compatible with ultrasonic transducers operating at a center frequency between kHz and kHz. Able to generate maximum sound pressure level using either transformer driven and direct driven methods. Swappable daughtercard allows custom driver-type and transducer combination pair to be evaulated.The Article Library brings you the latest technology news and trends, product information, supplier updates, and design how-tos.

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Product Training Modules PTMs from Digi-Key and supplier partners offer electronic component tutorials based on the latest products and technologies. Use the Development Tools Product Selector to access thousands of development and evaluation tools in ready to go or prototyping categories. Access pre-curated content, solutions, and resources from industry professionals, as well as your peers, to help guide you from concept through production.

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Learn More Back Articles The Article Library brings you the latest technology news and trends, product information, supplier updates, and design how-tos. Back Automation and Control Digi-Key carries a broad line of Industrial Automation, Control and Safety products from some of the most recognized and trusted industry suppliers.

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Back Engineering Resources Browse through our resource collection including design tools, videos, articles, reference designs, conversion calculators, product selectors, and blogs.Making the World of Electronics More Accessible. Based on the concept of Customer-orientation and Innovation, a good process control system, professional management team, advanced inventory management technology, we can provide one-stop electronic component supporting services that NIS is the preferred partner for all the enterprises and research institutions.

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Breakout board.Making the World of Electronics More Accessible. Based on the concept of Customer-orientation and Innovation, a good process control system, professional management team, advanced inventory management technology, we can provide one-stop electronic component supporting services that NIS is the preferred partner for all the enterprises and research institutions.

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It appears that you have disabled your Javascript.If you do a large number of tests, falsely significant results are a problem. If you took a totally random, meaningless set of data and did 100 significance tests, the odds are that five tests would be falsely reported significant. As you can see, the more tests you do, the more of a problem these false positives are. You cannot tell which the false results are - you just know they are there. Limiting the number of tests to a small group chosen before the data is collected is one way to reduce the problem.

If this isn't practical, there are other ways of solving this problem. The best approach from a statistical point of view is to repeat the study and see if you get the same results. If something is statistically significant in two separate studies, it is probably true.

In real life it is not usually practical to repeat a survey, but you can use the "split halves" technique of dividing your sample randomly into two halves and do the tests on each.

If something is significant in both halves, it is probably true. The main problem with this technique is that when you halve the sample size, a difference has to be larger to be statistically significant.

The last common error is also important. Most significance tests assume you have a truly random sample. If your sample is not truly random, a significance test may overstate the accuracy of the results, because it only considers random error. The test cannot consider biases resulting from non-random error (for example a badly selected sample).

The preceding discussion recommends reading probability values in reverse (1 - p). Doing so will normally lead to correct decision making, but it is something of an over-simplification from the technical point of view. A more complex, technically correct discussion is presented here.

Unfortunately, statistical significance numbers do not directly tell us exactly what we want to know.

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They tell us how likely we would be to get differences between groups in our sample that are as large or larger than those we see, if there were no differences between the corresponding groups in the population represented by our sample. In other words, these numbers tell us how likely is our data, given the assumption that there are no differences in the population.

What we want to know is how likely there are differences in the population, given our data. Logically, if we are sufficiently unlikely to get a difference found in our sample, if there were no difference in the population, then it is likely that there is a difference in the population.

We used this logic in the first part of this article when we said that you can interpret significance numbers by considering 1-p as the probability that there is a difference in the population (where p is the significance number produced by the program). For example, if the significance level is. While this logic passes the common sense test, the mathematics behind statistical significance do not actually guarantee that 1-p gives the exact probability that there is a difference is the population.

Even so, many researchers treat 1-p as that probability anyway for two reasons. One is that no one has devised a better general-purpose measure. The other is that using this calculation will usually lead one to a useful interpretation of statistical significance numbers. In some non-survey fields of research, the possibility that 1-p is not the exact probability that there is a difference in the population may be more important.

In these fields, the use of statistical significance numbers may be controversial. This is the only product in our lineup that offers all features and tools we considered. For these reasons, The Survey System earns our TopTenREVIEWS Gold Award. A recent book by two of our faculty honors a third.


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