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[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,♪ [music] ♪
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,- [Thomas Stratmann] Hi!
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,In the upcoming series of videos
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,we're going to give you\Na shiny new tool
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,to put into your\NUnderstanding Data toolbox:
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,linear regression.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Say you've got this theory.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,You've witnessed\Nhow good-looking people
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,seem to get special perks.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,You're wondering -- "Where else\Nmight we see this phenomenon?"
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,What about full professors?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Is it possible\Ngood-looking professors
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,might get special perks too?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Is it possible\Nstudents treat them better
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,by showering them\Nwith better student evaluations?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,If so, is the effect of looks
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,on evaluation score\Nbig or [inaudible]?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,And say there is a new professor\Nstarting at the university.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,What can we predict\Nabout his evaluation
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,simply by his looks?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Given that these evaluations\Ncan determine pay raises,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,if this theory were true\Nwe might see professors resort
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,to some surprising tactics\Nto boost their scores.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Suppose you wanted to find out
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,if evaluations really improve\Nwith better looks.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,How would you go about\Ntesting this hypothesis?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,You could collect data.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,First you would have students rate\Non a scale from 1 to 10
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,how good looking a professor was,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,which gives you\Nan average beauty score.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Then you could retrieve\Nthe teacher's teaching evaluations
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,from 25 students.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Let's look at these two variables\Nat the same time
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,by using a scatterplot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,We'll put beauty\Non the horizontal axis,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,and teacher evaluations\Non the vertical axis.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,For example, this dot\Nrepresents Professor Peate,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,who received a beauty score of 3
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,and an evaluation of 8.425.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,This one way out here\Nis Professor Helmchen.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,- [Professor Helmchen]\NRidiculously good-looking!
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,- [Thomas] Who got\Na very high beauty score,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,but not such a good evaluation.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Can you see a trend?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,As we move from left to right\Non the horizontal axis,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,from the ugly to the gorgeous,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,we see a trend upwards\Nin evaluation scores.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,By the way, the data\Nwe're exploring in this series
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,is not made up --\Nit comes from a real study
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,done at the University of Texas.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,If you're wondering, "pulchritude"\Nis just the fancy academic way
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,of saying beauty.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,With scatterplots\Nit can sometimes be hard
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,to make out the exact relationship\Nbetween two variables --
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,especially when the variables\Nbounce around quite a bit
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,as we go from left to right.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,One way to cut through\Nthis bounciness
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,is to draw a straight line\Nthrough the data cloud
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,in such a way that this line\Nsummarizes the data
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,as closely as possible.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,The technical term for this\Nis "linear regression."
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Later on we'll talk about\Nhow this line is created,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,but for now we can assume\Nthat the line fits the data
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,as closely as possible.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,So, what can this line tell us?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,First, we immediately see
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,if the line is sloping\Nupward or downward.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,In our data set we see\Nthe [fitted] line slopes upward.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,It thus confirms what\Nwe have conjectured earlier
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,by just looking at the scatterplot.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,The upward slope means\Nthat there is a positive association
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,between looks\Nand evaluation scores.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,In other words, on average,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,better-looking professors\Nare getting better evaluations.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,For other data sets we might see\Na stronger positive association.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Or, you might see\Na negative association.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Or perhaps no association at all.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,And our lines\Ndon't have to be straight.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,They can curve to fit the data\Nwhen necessary.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,This line also gives us\Na way to predict outcomes.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,We can simply take a beauty score\Nand read off the line
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,what the predicted\Nevaluation score would be.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,So, back to our new professor.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,- [Professor Lloyd] Look familiar?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,- [Thomas] We can precisely\Npredict his evaluation score.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,"But wait! Wait!" you might say.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,"Can we trust this prediction?"
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,How well does\Nthis one beauty variable
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,really predict evaluations?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Linear regression gives us\Nsome useful measures
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,to answer those questions
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,which we'll cover\Nin a future video.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,We also have to be aware\Nof other pitfalls
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,before we draw\Nany definite conclusions.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,You could imagine a scenario
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,where what is driving\Nthe association
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,we see is really a third variable\Nthat we have left out.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,For example,\Nthe difficulty of the course
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,might be behind\Nthe positive association
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,between beauty ratings\Nand evaluation scores.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Easy intro. courses\Nget good evaluations.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Harder, more advanced courses\Nget bad evaluations.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,And younger professors might\Nget assigned to intro. courses.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Then, if students judge\Nyounger professors more attractive,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,you will find\Na positive association
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,between beauty ratings\Nand evaluation scores.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,But it's really\Nthe difficulty of the course.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,The variable that we've left out,\Nnot beauty,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,that is driving evaluation scores.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,In that case, all the primping\Nwould be for naught --
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,a case of mistaken correlation\Nfor causation,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,something we'll talk about further\Nin a later video.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,And what if there were\Nother important variables
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,that affect both beauty ratings\Nand evaluation scores?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,You might want to add\Nconsiderations like skill,
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,race, sex, and whether English\Nis the teacher's native language
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,to isolate more cleanly the effect\Nof beauty on evaluations.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,When we get\Ninto multiple regression
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,we will be able to measure\Nthe impact of beauty
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,on teacher evaluations
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,while accounting\Nfor other variables
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,that might confound\Nthis association.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Next up, we'll get our hands dirty\Nby playing with this data
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,to gain a better understanding\Nof what this line can tell us.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,- [Narrator] Congratulations!
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,You're one step closer\Nto being a data ninja!
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,However, to master this you'll need\Nto strengthen your skills
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,with some practice questions.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Ready for your next mission?\NClick "Next Video."
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Still here?
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,Move from understanding eata\Nto understanding your world
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,by checking out MRU's\Nother popular economics videos.
Dialogue: 0,9:59:59.99,9:59:59.99,Default,,0000,0000,0000,,♪ [music] ♪