Minimizing Error
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Minimize Sum Of Squared Errors
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How To Minimize Systematic Error
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Minimizing Random Error
skills, and videos Main content To log in and use all the features of Khan Academy, please enable JavaScript in your browser. Statistics and probability Describing relationships in quantitative dataResiduals, least-squares regression, and r-squaredIntroduction to residualsSquared error of regression lineRegression line exampleSecond how to minimize experimental errors regression exampleProof (part 1) minimizing squared error to regression lineProof (part 2) minimizing squared error to regression lineProof (part 3) minimizing squared error to regression lineProof (part 4) minimizing squared error to regression lineR-squared or coefficient of determinationCalculating R-squaredCovariance and the regression lineCurrent time:0:00Total duration:10:350 energy pointsStatistics and probability|Describing relationships in quantitative data|Residuals, least-squares regression, and r-squaredProof (part 1) minimizing squared error to regression lineAboutProof (Part 1) Minimizing Squared Error to Regression Line. Created by Sal Khan.ShareTweetEmailResiduals, least-squares regression, and r-squaredIntroduction to residualsSquared error of regression lineRegression line exampleSecond regression exampleProof (part 1) minimizing squared error to regression lineProof (part 2) minimizing squared error to regression lineProof (part 3) minimizing squared error to regression lineProof (part 4) minimizing squared error to regres
which is a common measure of estimator quality, of the fitted values of a dependent variable. In the Bayesian setting, the term MMSE more specifically refers to estimation with minimization of errors in analytical chemistry quadratic cost function. In such case, the MMSE estimator is given by methods of minimizing errors the posterior mean of the parameter to be estimated. Since the posterior mean is cumbersome to calculate, the form how to reduce measurement error of the MMSE estimator is usually constrained to be within a certain class of functions. Linear MMSE estimators are a popular choice since they are easy to use, calculate, and very versatile. https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/residuals-least-squares-rsquared/v/proof-part-1-minimizing-squared-error-to-regression-line It has given rise to many popular estimators such as the Wiener-Kolmogorov filter and Kalman filter. Contents 1 Motivation 2 Definition 3 Properties 4 Linear MMSE estimator 4.1 Computation 5 Linear MMSE estimator for linear observation process 5.1 Alternative form 6 Sequential linear MMSE estimation 6.1 Special Case: Scalar Observations 7 Examples 7.1 Example 1 7.2 Example 2 7.3 Example 3 7.4 Example 4 https://en.wikipedia.org/wiki/Minimum_mean_square_error 8 See also 9 Notes 10 Further reading Motivation[edit] The term MMSE more specifically refers to estimation in a Bayesian setting with quadratic cost function. The basic idea behind the Bayesian approach to estimation stems from practical situations where we often have some prior information about the parameter to be estimated. For instance, we may have prior information about the range that the parameter can assume; or we may have an old estimate of the parameter that we want to modify when a new observation is made available; or the statistics of an actual random signal such as speech. This is in contrast to the non-Bayesian approach like minimum-variance unbiased estimator (MVUE) where absolutely nothing is assumed to be known about the parameter in advance and which does not account for such situations. In the Bayesian approach, such prior information is captured by the prior probability density function of the parameters; and based directly on Bayes theorem, it allows us to make better posterior estimates as more observations become available. Thus unlike non-Bayesian approach where parameters of interest are assumed to be deterministic, but unknown constants, the Bayesian
Minimize Errors And Create More Usable Websites: Part I Errors happen. It's inevitable that any system will fail at times. Often failures are attributed to human error. Most, though are really due http://vanseodesign.com/web-design/minimize-errors-part-i/ to design errors. As designers what can we do to minimize errors and their effects on our sites and in our applications? Fortunately we can communicate through a variety design strategies in a way that reduce both the frequency of errors on the sites we build and the magnitude of those errors that inevitably occur. There are a number of strategies and a lot to cover. We'll begin today with a look at errors in how to general, the different types and causes of errors, and offer a quick mention of the strategies at our disposal. Then we'll look in more detail at the first of these strategies for countering errors, affordance. The 2 Types of Errors Errors are actions taken or not taken that lead to unintended results. There are two main types of errors that can occur, slips and mistakes, each with their own subtypes of errors. Slips Slips are how to minimize errors of action or errors of execution. An example I'm sure you're familiar with is when you find yourself driving to a common destination when you meant to drive somewhere else. Another example is when you're adding a series of numbers in a column and the phone rings and you temporarily lose your place and skip a number before continuing to add again. Slips occur when the action taken was not the one intended and they're usually the result of the unconscious. They frequently result from a change in the usual routine or the interruption of an action you were taking. There are two types of slips. Action slips result from changes to repetitive tasks. The driving example above is an action slip (PDF), since the error comes from varying the routine. We help prevent action slips by providing clear and distinctive feedback and through the use of confirmations, constraints, affordance, and mappings (all to be covered later in this post or parts ii or iii). Sticking to conventions can also help prevent action slips as they maintain the status quo. Attention slips result from distractions and interruptions. The number adding example is an attention slip since the slip occurs due to an interruption of your current activity. We help prevent attention slips through clear orientation and status cues. You might use highlight