In this video, I will teach you about the error analysis of the Electrical Measurement. Error analysis is one of the most important topics, so many questions are framed from this topic, so it is always advisable to have at least some basic idea of this topic. I am sure after watching this video, you will be able to solve any question asked in any exam from this topic whatever I have taught to you in this video. I have prepared an online video course for Junior Engineer examinations, visit this page for more information. https://www.engitechakademy.com/p/online-paid-video-course-on-control.html Please check out our blog, https://www.engitechakademy.com/ Please like my facebook page. https://www.facebook.com/engiTechakademy/ Solve one question with 6 different methods https://youtu.be/gU3ioYyqitw Job updates playlist https://www.youtube.com/playlist?list=PLIsqkyLucjKl6Luz1qAlCu8TE7A5z2YPl Cut Off marks Details https://www.youtube.com/playlist?list=PLIsqkyLucjKmWmxFWc0NT8ek00TvA-MXg SSC JE Playlist https://www.youtube.com/playlist?list=PLIsqkyLucjKlWc_sKPwd4f9tGfPjKx-gy Basic MCQ Playlist https://www.youtube.com/playlist?list=PLIsqkyLucjKnfRWVgB7vXLbLxNCEvnt-J LMRC Playlist https://www.youtube.com/playlist?list=PLIsqkyLucjKkn9Ju3RtJgfqodhM57cNId DMRC Playlist https://www.youtube.com/playlist?list=PLIsqkyLucjKmJB7JGfG0P0oSGPRmXKAn_ Power System Playlist https://www.youtube.com/playlist?list=PLIsqkyLucjKljSSJo5A1CpXOE43YD1cG9 Measurement Playlist https://www.youtube.com/playlist?list=PLIsqkyLucjKmrsmFXsEIOQiEt04a5idpO #erroranalyisiinhindi
Views: 16413 engiTechakademy
Numerical analysis tutorial Video Chapter: Error Topic: Absolute Error, Relative Error, Relative Percentage Error.. *Though 22/7 is not equal to pi. its also an approximation. this example is only to understand the concept.
Views: 37298 Sutanu Paul
Types of Error in Pharmaceutical Analysis, Types of Error in Analytical Chemistry, Determinate Error, Systematic Error, Indeterminate Error, Random Error, Non Systematic Error, Accidental Error, Personal Error, Instrumental Error, Reagent Error, Additive Error, Constant Error, Proportional Error, Error in Method
Views: 9701 Dr. Puspendra Classes
In this video you will understand Error, classification of error, Gross error, Systematic error, Instrumental Error, Environmental Error, Observational error, Random error Lec-01 | Measurement, Instrument, True Value, Accuracy & Precision https://youtu.be/AJt3ysBQb1Y Know the weightage of Electronic Measurements and Instrumentation(EC)/Electrical and Electronic Measurements(EE)/Measurements(IN) https://youtu.be/0SJ2_03V1Xw General Studies & Engineering Aptitude ( Paper-1) ESE 2017 Weightage Analysis https://youtu.be/WzDozX3ehCI This channel is free. We are dealing in Technical as well non-technical related to GATE/ESE/PSUs. Also helpful for all engineering government exams including DMRC/NMRC/SAIL/Coal India Limited/UPPCL/UPRVUNL/Mahatransco/IBPS-Specialist Officer, SBI-Specialist Officer, SSC-JE/SSC CGL Exams/ SSC CHSL / NDA /State PSC etc. Join the facebook group for discussion https://www.facebook.com/groups/avinashsir Like our facebook page for pdf material https://www.facebook.com/avinashsinghsir
Views: 20597 ESE/GATE/PSUs Short e-Lecture
Html url? Q webcache. One example of such a flaw is bad calibration in the instrumentation error analysis branch applied linguistics. Corder that error analysis (ea) owes its place as a scientific method in definition of ca is the study and comparison two languages, learners' identifying factors contribute to characteristics learner discourse began earnest with field 1970s, developing into what Basic concepts webassignan introduction concept. A quantity such as the concept of error needs to be well understood. Students learning a foreign language error analysis these inaccuracies could all be called errors of definition. Full text of 'contrastive analysis and error a general professor jack c. 2012 1250 1217 nanda aulia 2. 2012 1250 may 21, 2012 this is a presentation i used for practicum course. They tend to produce values either consistently above the true value, or below value. Error analysis glottopedia. Error analysis in a written composition scielo colombia. May 13, 2015 definition and history of error analysis created by group 6 1. 2012 1250 1227 adelya daniyah 3. Closely related to error analysis is the concept of interlanguage an introduction m. What is error? error analysis? Describe the types of analysis. Basic concepts of error analysis webassignan introduction to the concept. An introduction to the concept of error analysis robert wetzorke definition and history slidesharelanguage linguistics slideshare. Define error analysis at dictionary. Publish your science fair error analysis. Error analysis (linguistics) wikipedia. Where the latter holds that more precisely, there has been a shift from formerly applied 'contrastive analysis' (cah) toward occupation with 'error (ea). A of education robert wetzorke term paper english pedagogy, didactics, literature studies publish error analysis. It is concerned with the compilation, study and analysis of errors made by second language learners aims at investigating aspects acquisition. Discuss the concept of error analysis in second language wolfs. Illustrate how these errors can be managed. Error analysis the center for advanced research on language error dictionary definition of. The purpose of this section is to explain how and why the results deviate from according j. Modifying the position of l2 grammar categories, affecting meaning, and indicating interference it is to s. Error analysis glottopedia basic concepts of error webassign question error_analysis manual. Googleusercontent searchthis class of error is commonly caused by a flaw in the experimental apparatus. It's useful for new teachers to know about error correction during a students oral presentation dec 30, 2015 the concept of analysis has been applied both sll and sla sharwood smith (1994), gass selinker (1994) so errors their is advantageous learners teachersthe present researcher uses term 'error' refer definition, systematic study deviations from target language norms in course second acquisition, especially terms let me st
Views: 748 Another Question II
Error analysis and interlanguage. Error analysis and interlanguage linguistics. Error analysis and interlanguage Applied linguistics. Pit Corder and Larry Selinker Error Analysis vs Contrastive Analysis Error Analysis vs Contrastive Analysis Error Analysis vs Contrastive Analysis Error Vs Mistake Error Vs Mistake Error Vs Mistake Interlanguage Learner Language Learner Language Error Analysis ERROR ANALYSIS Error Analysis Error analysis Error analysis Idiosyncratic system
Views: 5415 Applied Linguistics & English Language Teaching
Hello guys, today I will be talking about Transfer Theory, Contrastive Analysis, Discourse Analysis and Error Analysis
Views: 317 Linguistics
A 17 minute video I would like all PHY131 students to watch before coming to class 3. Based on http://www.physics.utoronto.ca/~jharlow/teaching/phy131f13/errorMini.pdf . Excel spreadsheet with calculations used: http://www.physics.utoronto.ca/~jharlow/teaching/phy131f13/errorMinicalc.xls . Powerpoint slides: http://www.physics.utoronto.ca/~jharlow/teaching/phy131f13/cl03vidslides.pdf
Views: 13636 Jason Harlow
There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention. This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship. For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?" The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out." Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout." The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary. In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables. Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis. A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one. A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it. To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred. A type II error happens when you decide your prediction is wrong when you are actually right. One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules. It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study. When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level. Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists. The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...
Views: 91284 NurseKillam
An example of how to calculate the standard error of the estimate (Mean Square Error) used in simple linear regression analysis. This typically taught in statistics. Like us on: http://www.facebook.com/PartyMoreStud... Link to Playlist on Regression Analysis http://www.youtube.com/course?list=EC... Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongs...
Views: 253922 statisticsfun
SKIP AHEAD: 0:39 – Null Hypothesis Definition 1:42 – Alternative Hypothesis Definition 3:12 – Type 1 Error (Type I Error) 4:16 – Type 2 Error (Type II Error) 4:43 – Power and beta 6:33 – p-Value 8:39 – Alpha and statistical significance 14:15 – Statistical hypothesis testing (t-test, ANOVA & Chi Squared) For the text of this video click here http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/ For my video on Confidence Intervals click here http://www.stomponstep1.com/confidence-interval-interpretation-95-confidence-interval-90-99/
Views: 404886 Stomp On Step 1
SLL Error Analysis Second Language Acquisition
Views: 128 Online Lectures In Hindi - Urdu
Kindly hit like, share and subscribe.
Views: 24 Ashish Bait
CS 205A: Mathematical Methods for Robotics, Vision, and Graphics
Views: 61301 Justin Solomon
What is ERROR LEVEL ANALYSIS? What does ERROR LEVEL ANALYSIS mean? ERROR LEVEL ANALYSIS meaning - ERROR LEVEL ANALYSIS definition - ERROR LEVEL ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Error Level Analysis is the analysis of compression artifacts in digital data with lossy compression such as JPEG. When used, lossy compression is normally applied uniformly to a set of data, such as an image, resulting in a uniform level of compression artifacts. Alternatively, the data may consist of parts with different levels of compression artifacts. This difference may arise from the different parts having been repeatedly subjected to the same lossy compression a different number of times, or the different parts having been subjected to different kinds of lossy compression. A difference in the level of compression artifacts in different parts of the data may therefore indicate that the data have been edited. In the case of JPEG, even a composite with parts subjected to matching compressions will have a difference in the compression artifacts. In order to make the typically faint compression artifacts more readily visible, the data to be analyzed is subjected to an additional round of lossy compression, this time at a known, uniform level, and the result is subtracted from the original data under investigation. The resulting difference image is then inspected manually for any variation in the level of compression artifacts. In 2007, N. Krawetz denoted this method "error level analysis". Additionally, digital data formats such as JPEG sometimes include metadata describing the specific lossy compression used. If in such data the observed compression artifacts differ from those expected from the given metadata description, then the metadata may not describe the actual compressed data, and thus indicate that the data have been edited. By its nature, data without lossy compression, such as a PNG image, cannot be subjected to error level analysis. Consequently, since editing could have been performed on data without lossy compression with lossy compression applied uniformly to the edited, composite data, the presence of a uniform level of compression artifacts does not rule out editing of the data. Additionally, any non-uniform compression artifacts in a composite may be removed by subjecting the composite to repeated, uniform lossy compression. Also, if the image color space is reduced to 256 colors or less, for example, by conversion to GIF, then error level analysis will generate useless results. More significant, the actual interpretation of the level of compression artifacts in a given segment of the data is subjective, and the determination of whether editing has occurred is therefore not robust. In May 2013, N. Krawetz used error level analysis on the 2012 World Press Photo of the Year and concluded on the Hacker Factor blog that it was "a composite" with modifications that "fail to adhere to the acceptable journalism standards used by Reuters, Associated Press, Getty Images, National Press Photographer's Association, and other media outlets". The World Press Photo organizers responded by letting two independent experts analyze the image files of the winning photographer and subsequently confirmed the integrity of the files. One of the experts, Hany Farid, said about error level analysis that "It incorrectly labels altered images as original and incorrectly labels original images as altered with the same likelihood". Krawetz responded by clarifying that "It is up to the user to interpret the results. Any errors in identification rest solely on the viewer". In May 2015, the citizen journalism team Bellingcat wrote that error level analysis revealed that the Russian Ministry of Defense had edited satellite images related to the Malaysia Airlines Flight 17 disaster. In a reaction to this, image forensics expert J. Kriese said about error level analysis: "The method is subjective and not based entirely on science", and that it is "a method used by hobbyists". On his Hacker Factor Blog the inventor of error level analysis N. Krawetz criticized both Bellingcat's use of error level analysis as "misinterpreting the results" but also on several points J. Kriese's "ignorance" regarding error level analysis.
Views: 448 The Audiopedia
Lexical Analysis Lex | Lexical Errors | Syntax Error on Token Examples | Recognition of Tokens, Lex - A Lexical Analyzer Generator - The LEX & YACC, Lexical Analysis and the Lex Scanner Generator, lexical error definition, lexical errors examples, lexical errors in compiler, study of lexical errors, lexical errors in compiler design, lexical errors the translation lexical errors the original text lexical errors java syntax error on token ( expected syntax error on token void @ expected syntax error on token int dimensions expected after this token syntax error on token variabledeclaratorid expected after this token syntax error on token string strictfp expected syntax error on token else delete this token syntax error on token class @ expected syntax error on token else Transition diagram for recognition of tokens token recognition Compiler Construction
Views: 18361 Gate Instructors
Can you find the mistakes in these English sentences? In today's lesson, you'll review 8 grammar rules of correct English sentences. You'll get to practice correcting sentences with me in the video. Once you learn these easy grammar rules, you'll avoid making common mistakes and improve your marks on English essays and exams like IELTS, TOEFL, and TOEIC. To test if you really understand these rules, take the quiz. Good luck with your English! http://www.engvid.com/8-english-sentences-find-the-mistakes/ TRANSCRIPT Hi, my name's Rebecca. For the next few minutes, let's pretend you are the English teacher and you're correcting your student's homework. Let's look at some of these sentences and see if you can find some of the errors in these English sentences. Okay, the first sentence: "My mother she works in a bank." Is that okay? Well, let me tell you right now that actually none of these sentences are okay; there is a mistake in every sentence. So see if you can find the mistake. Okay? "My mother she works in a bank." What's the mistake? Okay... Here, "she", all right? I'm just going to grab a different marker. So what happened here is we said: "My mother she works in a bank." So we cannot repeat the subject. The mistake here is that we had a double subject; the subject was mentioned twice. In English, you can't do that. You just mention the subject once. So this sentence, in order to be correct, would need to be: "My mother works in a bank." Or: "She works in a bank." If you know who "she" is. Right? But you can't say both. So no double subjects. Number two: "John is an engineer" What's wrong with that? Look carefully. Well, what's wrong is that it's missing the punctuation. All right? Part of a correct sentence is correct punctuation. So here, there was no period at the end of the sentence, that's what was wrong. Next sentence: "The manager of my department" What's wrong with that? Well, what's wrong is that it's not a sentence because it doesn't have any verb, there's no verb there. Okay? And, of course, you need to continue this sentence, and then eventually you'd need to have some punctuation as well. But basically, there is no... This is a sentence fragment. This is called only a part of a sentence. It is not a complete English sentence or a correct English sentence. There is no verb. Missing verb. Next one: "we enjoy watching old movies." Okay? Again, look carefully. What's wrong there? Well, it has a subject, it has a verb, but this is the problem. The first letter in the first word of an English sentence has to be capitalized and that's what was missing here. You see, we didn't have that problem before. Okay. Next one: "I like very much Chinese food." Okay? Maybe that sounds okay to you, but doesn't sound okay to me. It's close, but not quite. What's wrong? Well, what's wrong here is this, the word order. Not only do you need to have certain elements, you need to have the words in the right order. So in English, the correct order for this sentence would be: "I like Chinese food very much." Okay? Not: "very much Chinese food." "I like Chinese food very much." Okay? Next: "Maria need help with her hw." "Maria need help with her homework." What's wrong there? Okay? So the mistake is here, the mistake is in subject-verb agreement. The verb has to agree with the subject. Right? And if we say: "Maria", it's like: "she", and we would have to say: "She needs". "Maria needs help with her hw." So the error here was in subject-verb agreement. Next one: "delivered the package yesterday" Okay? "delivered the package yesterday" What's wrong here? Well, it's similar to this one, except here, we had a sentence fragment and we had the subject. Here, we have a sentence fragment, and we have a verb, but we don't have a subject. We have a missing subject. So this is also a sentence fragment. "Fragment" means only part. It is not a complete sentence. Next one: "We recieved your letter." "We recieved your letter." Sounds fine, but if you're an English teacher, you're going to look really carefully at each of the words. And what's wrong is here, the mistake is here. It's a spelling mistake. Okay? The word "received" is one of those tricky words with the "e" and the "i", and the "i" and the "e" that you have to learn very well. So spelling mistakes will also bring down your marks. If you're doing the IELTS, if you're bring... Doing the TOEFL, any errors of this kind will bring your marks down. Okay? So even though they seem very basic, I know from experience that students make all of these mistakes. Be very careful not to make them. Let's look at what principles apply to correct English sentences. Okay? So, an English sentence must express a complete thought and it must express it with certain elements. Now, just because a sentence must express a complete thought, it doesn't have to have a lot of words; it doesn't have to be a very long sentence.
Views: 764979 Learn English with Rebecca [engVid]
Scientific measurements are characterized by inaccuracy and imprecision due to experimental errors. This video introduces error analysis by showing the normal distribution, defining accuracy and precision, and illustrating how systematic and random errors introduce inaccuracy and imprecision.
Views: 3777 Michael Evans
CS 205A: Mathematical Methods for Robotics, Vision, and Graphics
Views: 13177 Justin Solomon
We are going to be explaining 12 cognitive biases in this video and presenting them in a format that you can easily understand to help you make better decision in your life. Cognitive biases are flaws in logical thinking that clear the path to bad decisions, so learning about these ideas can reduce errors in your thought process, leading to a more successful life. These biases are very closely related to logical fallacies, which may help you win an argument or present information better. Ismonoff: https://www.youtube.com/user/ismonofftv 1)Anchoring Bias 2)Availability Heuristic bias 3)Bandwagon Bias 4)Choice Supportive Bias 5)Confirmation Bias 6)Ostrich Bias 7)Outcome Bias 8)Overconfidence 9)Placebo bias 10)Survivorship Bias 11)Selective Perception Bias 12)Blind Spot Bias What I make my videos with: http://bit.ly/2fPakuK Insta: https://www.instagram.com/practical_psych/ Twitter: https://twitter.com/practical_psych Facebook: https://www.facebook.com/practicalpsych Check out MY Passive Income Ebook: http://bit.ly/PsychologyIncome
Views: 614343 Practical Psychology
Here I explain the whole concept of errors including why relative errors get added in the quotient formula. If you have any questions OR if you want me to make an interesting video on any topic of your choice, Please post them in the comment section or Email me. Email: [email protected] Facebook: www.facebook.com/vj.prateek Website: www.physicsaholics.com
Views: 631 PHYSICSAHOLICS
If you hang out around statisticians long enough, sooner or later someone is going to mumble "maximum likelihood" and everyone will knowingly nod. After this video, so can you! Also, some viewers asked for a worked out example that includes the math. Here it is! (you may need to click on the "Show More" button below to see the link) https://youtu.be/cDlNsHUBmw4 For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Views: 166173 StatQuest with Josh Starmer
In engineering and science, dimensional analysis is the analysis of the relationships between different physical quantities by identifying their fundamental dimensions (such as length, mass, time, and electric charge) and units of measure (such as miles vs. kilometers, or pounds vs. kilograms vs. grams) and tracking these dimensions as calculations or comparisons are performed. Converting from one dimensional unit to another is often somewhat complex. Dimensional analysis, or more specifically the factor-label method, also known as the unit-factor method, is a widely used technique for such conversions using the rules of algebra. The concept of physical dimension was introduced by Joseph Fourier in 1822. Physical quantities that are measurable (commensurable) have the same dimension (length, time, mass) and can be directly compared to each other, even if they are originally expressed in differing units of measure (inches and meters, pounds and newtons). If physical quantities have different dimensions (length verses mass), they cannot be compared by similar units (are incommensurable) and cannot be compared in quantity. Hence, it is meaningless to ask whether a kilogram is greater than, equal to, or less than an hour. Any physically meaningful equation (and likewise any inequality and inequation) will have the same dimensions on their left and right sides, a property known as "dimensional homogeneity". Checking for dimensional homogeneity is a common application of dimensional analysis. Dimensional analysis is also routinely used as a check of the plausibility of derived equations and computations. It is generally used to categorize types of physical quantities and units based on their relationship to or dependence on other units.[clarification needed] Contents [hide] 1 Concrete numbers and base units 1.1 Percentages and derivatives 2 Conversion factor 3 Dimensional homogeneity 4 The factor-label method for converting units 4.1 Checking equations that involve dimensions 4.2 Limitations 5 Applications 5.1 Mathematics 5.2 Finance, economics, and accounting 5.3 Fluid mechanics 6 History 7 Mathematical examples 7.1 Definition 7.2 Mathematical properties 7.3 Mechanics 7.4 Other fields of physics and chemistry 7.5 Polynomials and transcendental functions 7.6 Incorporating units 7.7 Position vs displacement 7.8 Orientation and frame of reference 8 Examples 8.1 A simple example: period of a harmonic oscillator 8.2 A more complex example: energy of a vibrating wire 9 Extensions 9.1 Huntley's extension: directed dimensions 9.2 Siano's extension: orientational analysis 10 Dimensionless concepts 10.1 Constants 10.2 Formalisms 11 Dimensional equivalences 11.1 SI units 11.2 Natural units 12 See also 12.1 Related areas of math 13 Notes 14 References 15 External links 15.1 Converting units Concrete numbers and base units Many parameters and measurements in the physical sciences and engineering are expressed as a concrete number – a numerical quantity and a corresponding dimensional unit. Often a quantity is expressed in terms of several other quantities; for example, speed is a combination of length and time, e.g. 60 miles per hour or 1.4 km per second. Compound relations with "per" are expressed with division, e.g. 60 mi/1 h. Other relations can involve multiplication (often shown with · or juxtaposition), powers (like m2 for square meters), or combinations thereof. A base unit is a unit that cannot be expressed as a combination of other units. For example, units for length and time are normally chosen as base units. Units for volume, however, can be factored into the base units of length (m3), thus they are considered derived or compound units. Sometimes the names of units obscure that they are derived units. For example, an ampere is a unit of electric current, which is equivalent to electric charge per unit time and is measured in coulombs (a unit of electrical charge) per second, so 1 A = 1 C/s. Similarly, one newton is 1 kg⋅m/s2 Conversion factor Main article: Conversion factor In dimensional analysis, a ratio which converts one unit of measure into another without changing the quantity is called a conversion factor. For example, kPa and bar are both units of pressure, and 100 kPa = 1 bar. The rules of algebra allow both sides of an equation to be divided by the same expression, so this is equivalent to 100 kPa / 1 bar = 1. Since any quantity can be multiplied by 1 without changing it, the expression "100 kPa / 1 bar" can be used to convert from bars to kPa by multiplying it with the quantity to be converted, including units. For example, 5 bar × 100 kPa / 1 bar = 500 kPa because 5 × 100 / 1 = 500, and bar/bar cancels out, so 5 bar = 500 kPa.
Views: 399 MeraTutor
Numerical analysis tutorial Video Chapter: Error Topic: Relationship between relative error and significant digits Examples
Views: 7769 Sutanu Paul
Physics video lecture for class 11 in Hindi for the preparation of CBSE, IIT JEE, JEE, NEET, AIPMT, ICSE and all other Boards and entrance examinations. This video is about Units and Measurements - NCERT Chapter 2 - Units and measurements - Error Analysis. Topics covered in this video are: 1) What is an Error in Physics? 2) How can we define Error in Physics? 3) What are different types of Error in Physics? 4) Algebra of Errors in Physics. 5) How to add and subtract quantities with errors involved? 6) How to multiply and divide quantities with errors involved? 7) What is the Difference between accuracy and precision? Rewodu - https://www.youtube.com/c/rewodu Class 12 playlist - https://goo.gl/3XjOzu Class 11 playlist - https://goo.gl/ruOFNM Facebook - https://www.facebook.com/rewodutech Twitter - https://twitter.com/rewodu
Views: 20162 Rewodu
This is an important matter. It refers to how errors at the beginning and in later steps (roundoff, for example) propagate into the computation and affect accuracy, sometimes very drastically. We state here what happens to error bounds. Namely, bounds for the error add under addition and subtraction, whereas bounds for the relative error add under multiplication and division. You do well to keep this in mind. -~-~~-~~~-~~-~- Please watch: "Exercise 4.1 Question 19 and 20" https://www.youtube.com/watch?v=bVWNoXrdsc8 -~-~~-~~~-~~-~- educationforall430
Views: 1278 Education For All
Video transcript: "Have we discovered a new particle in physics? Is a manufacturing process out of control? What percentage of men are taller than Lebron James? How about taller than Yao Ming? All of these questions can be answered using the concept of standard deviation. For any set of data, the mean and standard deviation can be calculated. For example, five people may have the following amounts of money in their wallets: 21, 50, 62, 85, and 90. The mean is $61.60 and the standard deviation is $28.01. How much does the data vary from the average? Standard deviation is a measure of spread, that is, how spread out a set of data is. A low standard deviation tells us that the data is closely clustered around the mean (or average), while a high standard deviation indicates that the data is dispersed over a wider range of values. It is used when the distribution of data is approximately normal, resembling a bell curve. Standard deviation is commonly used to understand whether a specific data point is “standard” and expected or unusual and unexpected. Standard deviation is represented by the lowercase greek letter sigma. A data point’s distance from the mean can be measured by the number of standard deviations that it is above or below the mean. A data point that is beyond a certain number of standard deviations from the mean represents an outcome that is significantly above or below the average. This can be used to determine whether a result is statistically significant or part of expected variation, such as whether a bottle with an extra ounce of soda is to be expected or warrants further investigation into the production line. The 68-95-99.7 rule tells us that about 68% of the data fall within one standard deviation of the mean. About 95% of data fall within two standard deviations of the mean. And about 99.7% of data fall within 3 standard deviations of the mean. The average height of an American adult male is 5’10, with a standard deviation of 3 inches. Using the 68-95-99.7 rule, this means that 68% of American men are 5’10 plus or minus 3 inches, 95% of American men are 5’10 plus or minus 6 inches, and 99.7% of American men are 5’10 plus or minus 9 inches. So, this means only about .3% of American men deviate more than 9 inches from the average, with .15% taller than 6’7 and .15% shorter than 5’1. This reasoning suggests that Lebron James is 1 in 2500 and Yao Ming is 1 in 450 million. In particle physics, scientists have what are called 5-sigma results, results that are five standard deviations above or below the mean. A result that varies this much can signify a discovery as it has only a 1 in 3.5 million chance that it is due to random fluctuation. In summary, standard deviation is a measure of spread. Along with the mean, the standard deviation allows us to determine whether a value is statistically significant or part of expected variation."
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http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Modeling quantization error as uncorrelated noise. Signal to quantization noise ratio as a function of the number of bits used to represent the signal.
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