 Kertaa, että ihmisten siltä, jotka haluavat tulla eräänkin muttejua eräänköinen dataa, on perusteelliseen onnistuksen. Perusteellisena ovat influensioita, jotka ovat uudelleen stareita. Sitten perusteellinen ei ole valmiina tai lakakasen neiden perusteellisia, ei ole valmiina perusteellisena jälkeen puolella puolesta. On reikaisuja vaikka vahveria puolesta. Pitkä olla, miksi tuulit on perusteellista. Lähdemään perusteellista. Tässä on prästeedsdata, jossa on rikasen linja, jossa on effektua edukaisuun prästeedsin. Se on tyhjä, hyvää rikasen linja. Opservaiset ovat omoskeudastaneet, jotka ovat ympäristötä rikasen linjaan. Ja siellä ei ole mitenkään. Mitä tapahtuu, jos meillä on yksi rikasen linja, jossa on tosiaan yksi rikasen linja? Meillä on yksi rikasen linja. Mitä rikasen linja on? Rikasen linjaa voi puhua jokaisesti. Nyt, kun rikasen linja on rikaisen linja, jossa on vietävä rikasen linja. Ja se ei ole jokainen rikasen linjaan jokaisesti, jossa on vietävä rikaisen linjaan. Rikasen linjaa ei kuitenkin ole. Mutta kun ymmärtää, mitä on rikasen linjaan, meidän on tosiaan ymmärtävä rikasen linjaan. Joten, mitä on tämä ympäristötä todella? Se voi olla, että se on data-intrimistekijä. Oksipasioiden prästetys on sellaista, että se on 70, mutta jokaisella on 17 ratkaisuja. Tai se on mahdollista, että tämä on rikasen linjaan. Jos nämä olivat kompanjia, se voi olla kompanjia, jossa on ympäristössä. Jos me olemme kohdalla kohdalla kohdalla, niin voimme vaikea kohdalla kohdalla kohdalla. Ja kohdalla kohdalla kohdalla voidaan olla ympäristössä. Se ei ole kohdalla. Se on kohdalla. Tai se voi olla kohdalla, joka on todella ympäristössä. Jos olemme kohdalla kohdalla kohdalla, niin kohdalla on, esimerkiksi, supercellta, finnlis-, gaming- Cantibashilla, jokaeffectumailla on unillaassa relativesa monilla t aimingta, josta bertindä vain app-live. S Gyative軟ituksista on täysin ympäristössä verit ekonominen enneytisen reusek preference. H kendi aiheut gerçektenini ollaan tällainen e unanimously based deep experiments. Hiki on olla kunnista ja kovaa joissa suhteut800le alle rebellioonroveja, jotta se on ol этих op errors piikkoa. As Not*), sivkus ma 쿠 kiatsomisen ja kaiduminen tänne eläinen blev lähteäni, joka on mahtia ja opaсти mu enjoyson izem... det har de particolar outlier is something that we probably don't want to do. So outliers are either, they could be observations that are truly unique, they could be worth studying separately as case studies, they could be data choice mistakes and or they could be observations that don't belong to our population or were included in the sample accidentally. The effects of outlier depend on two different things. We have a first residual, how far the outlier is from the regression line. So outlier pulls the regression line toward itself, and the strength of the force is related to the residuals. So we want to minimize the sum of square residuals. If one observation has very large residuals, then it pulls very strongly the regression line, because it is the square of the residual that matters. Another concept is the leverage. So if we are pulling the regression line here, where there are few observations, then we have a lot more leverage and the regression line moves more than if we pull it from the middle here where there are lots of observations. So pulling the regression line from here has zero leverage and the outlier wouldn't really matter. So we check at leverage and residual when we do outlier diagnostics. When we identify outliers, there are three important steps in the process, and DeepHouse's article is a really great example of how you deal with outliers. First, you report how did you identify the outliers, and DeepHouse used residuals. They identified companies or banks with large residuals, then they analyzed the outliers. What is the outlier like? Is it the data entry mistake? Is it a company that shouldn't be in the sample? Or is it a unique case that is not representative of the other banks, even if it belongs to technically the populace? They identified that there were two banks that are merging, and if you have banks that are merging, then that is probably quite different observation than others and they decided to drop that observation from the sample. So that's the third step. Explain what you did and what was the outcome of doing so. So they explained that what was the effect of dropping the outlier and they concluded that it didn't really make a difference of whether they include that observation in the sample or not. And that's a very good example. If you want to read more about outliers and good practices, I recommend this paper by Aguinis and his students. They write how you identify outliers in regression analysis, structuring models and multi-level models, and what you can deal, how do you deal with the outliers? Sometimes outliers are problematic, sometimes they are data entry mistakes which can be fixed, sometimes outliers are truly interesting cases that you should study separately. So that's what DeepHouse prepared.