linear regression is the most basic and commonly
use predictive analysis These linear models are
used to show or predict the relationship between
two or more variables Understanding the importance Awfully
new regression We have come for this linear
regression tutorial Now before we go ahead with
session I'd like to inform that we have
launched a completely free platform Calorically planning academy
We have access to free courses such as
the iCloud and they still marketing You can
check out the details in the description below
No that's how quick glance of the agenda
we'll start by understanding about correlation Then we
will learn about regression After that they will
comprehensively look at the mathematical concept softly New
regression So no let's get back from the
digital world Okay so the analytics education Okay
so who will define regression for me Dependency
on independence Security What are basically what that
is independent dependent variables input and output Okay
so that's good Way up with the what
else What are we doing here And with
the help of the subject we're going to
explain things yes or no Right We're tryingto
unfold the hidden batons Yes or no Okay
That's our objective today so I'm kind off
explaining things Okay So you have in starting
and you have in understanding things that in
everything great we talk about PC would offer
but others needs there is something one thing
is common of this court Obedience Yes or
no Yes So you always try to explain
millions Okay Yes I know Hey everyone Same
page millions So So if I want to
put in that domes I can put something
has explained and something as explain Italy Okay
so something will explain and something explanatory So
my objective here is to explain something but
the elbow X 20 Yes I know Okay
Okay So something that just explain But that
waxman d So my explain will be what
right my explain will be by and my
free delivery x So you remember those or
school days y equals mx plus e right
So I'm looking for why always available pics
Great So same day If what regression helps
me to explain variance and white with the
help of Aryan cynics in simple terms acceleration
It's a technique which helps me to explain
variance in way Bedell book various things That's
a simple no You can you know make
this as dependent You can get to the
normal but from the court That's what division
is Okay remember this Okay Simple here So
So this is coordination You already know right
It's ah major off association between two men
New medical variables was different Negative example Some
more temperature increases for us also increase utopias
Okay negative As the rate of Oprah protect
increases number of items sold decrease is yes
or no But it gets away from its
different So sadistic goes toward us there Yes
I know Ban Lloyd is more than so
You should always find a reason why Like
an iPhone it's nor did as they make
more expensive lines are bigger I would say
this Yes I know Okay So that's how
it is uh outlines or And this is
the sort of does Okay how Relationship is
measured basically with the Pearson coefficient which is
called smaller So Okay so what is the
way to analyze correlations at the plot Okay
Everybody knows Although Roy no correlation Random or
circular assortment of darts positive and lives leaning
towards right GPS sat score If your sat
score is high The GP will be also
high smoke and lung damage That's what getting
you know daily miss Other ring It'll if
you stay huh At eight It's a recipe
Good Wouldn't say if you're staying daily You're
obviously a life insurance has to be You
know more today Demon ways Okay negative Correlation
lifts learning to its left depression SST your
this depressed It says you low straining intestine
or usually errorless value One is perfect Zero
is no minus One is perfect Negative So
it lies between one and minus one So
this is how you should from the graph
Perfect Linear positive No correlation interpretations Strong association
moderate The positive negative again Same thing closer
to him Stronger linear relationship Okay so this
is what I was talking about So is
a tool for finding relationship with explain variable
and one or more explanatory variables in a
straight Okay So relationship can Bolivia nonlinear The
basic functional relationship identified statistically Significant explanatory variables
and s play estimator Mortal para meters Okay
definition raise everybody's same page Good So why
we do regulation and illnesses so there might
never use cases Right You need to identify
how mrp affects the pressure decision Right So
that will help me Which promotion is more
effective What is the risk associated with uprising
grease One customer retention Which customer is likely
rooted for what person Days of loan is
likely to result in a loss How to
identify the most preferred customers Okay these are
all relation Use cases Very It is used
everywhere mostly everywhere Great So no new domains
are coming up like but high level finance
marketing operations HR health care types of regulation
one and more again you already know this
Okay A simple aeration is retainers Why equals
to better not less bitter One x same
in the former bike will do and makes
place See Okay So guys let's to a
problem Okay Let's see how much uh we
can do Okay So problem here is a
B school that says you be school like
great legs Okay Once you start a new
batch of analytics course Okay It would like
to estimate in the more of application it
can expect for next year Admission Okay so
that's the problem So below other data points
given by the school here which year First
year Second year like that application number number
of applications whenever a new course will introduce
Right So there The problem is they want
to launch a new analytics course or an
ethics course But they have not done that
before Okay so now what best later They
have that in the past If they have
launched a new course one of a number
of applications okay as a brand or as
a college they want to see Okay second
one is average placement rate So basically they
are launching Our upscaling goes on this killing
in off so they will first check on
that They're the people who graduated from the
college What was the black placement rate for
that How many of them Gorgeous Sometimes you
are so motivated that instead of placement also
you go for education Sometimes you will say
let me work some time and then I
go for this killing you know So this
that's one data point is and then number
off undergraduate good food would graduate in the
current year How much You know students are
getting free from the college this year so
I can't have that But I get signal
OK so So this is the data You
all have elements lovin So there is that
he doesn't call the student You can see
that CS me shouldn't have said so Just
toe Get that into your working directory or
right now So problem here is C given
the record for last 15 years predict the
number of application using the placement rate You
understood the problem Given the record for last
15 years Predict the number of applications using
the place memory And then what is expected
Number of application given this year Placement rate
WAAS 60% Okay Understood So this is a
data OK And the problem is given the
record of last 15 years predict the number
of applications using that placement Read Okay And
what I have to find what is expected
number application given this year's placement rate was
60 Okay so first we'll do it in
excel Okay Have you done regression Excel before
So you were do it yourself huh Yes
I know No that I have been really
good I'm getting any good So when you
say it all way you saw that He
doesn't So what are the columns Yeah policemen
read obligations What else Serial numbers here First
year Imagine Okay Okay Okay Application number Everything
It okay Our graduation number eight policemen Great
Then traditional But this is one that your
days Okay So here is something like you
know you can consider this is 15 years
later Okay Think that way It's not a
variable for us It's 15 years later Okay
So how you will do this New dialysis
Okay go to regression Okay so now you
will give you input What is my by
here Application number Okay This is my way
What is my ex Every is read off
policemen That's why eggs my first Roy's label
Okay that's it Hello Doughboy Oh get ignition
Done So none shared the ones more de
bickering Can you say something So now do
this in an easy way Okay All we
will do this and easy there in So
now how to do Did I So this
is how you do it Not I've given
you the court Just follow it and let
me know Once it's done our launcher Our
session said W Balkan territory for that data
set into a working territory And I hope
you all know how to work that American
military What is the function for setting up
regulatory Said w really good Floyd You can
do it to them Any option Also in
our studio there is something called session and
then there's changer treated looking to do so
The energy do they not quickly done so
What I'm doing here model is mine has
resulted in a model in a limb Underscored
number regressing on average rate placement data Goto
s which is which I have read here
Okay I'll get this Somebody off deserts with
somebody function so OK you had already have
output in exhilarate Okay everyone had output Excell
Excell bantering with small porta accelerator RG III
Alberta Don't know them better go right So
I have come to this Ok so this
is output interpretation toget So it's me What
other things we look for Okay one is
the intercepts Okay So what are the intercepts
here huh This is 13 So it's system
may induce Abdullah MSC everybody agree etc Ticket
Right So do you see here That is
my industry An extra camera exes on m
Golda What is it I am here minus
months of the 8.0 do That's why I
am now You OK Why equal to beat
an old place be Devon ex body ticket
So why is my word application number Because
what is what in December 1 is ex
body to get every understand Good So let's
go Uh so partial regression coefficients What are
these Partial relation Coffee Shays Basically these values
Okay so So I've put that like you're
part simple or by very low energy has
single relation coffee shop So basically things emulation
will have unconditionally which is our m yet
basically become one right Right So in case
a multiple it will increase We'll talk about
that so again when you have more than
them we call them its partial relation conditions
Constantly Where Tom is called bias What in
the septum Okay Changing pretty value for you
No change in one independent variable Assuming all
other held constant That's how we interpret Okay
this isn't eerie Okay Well dog no Okay
So invested Also known as Why intercept Ok
so it's a It's basically why we call
it as wine the same So why an
X rated X Y So then I save
I equal Do a mix Bless See So
what will be the vying to say point
at which it hits the by access yes
or no That would be correct And how
my will here then exes 00 into em
will be zero Correct Bless See So this
is see or did find a tradition but
they So now in our example this is
where it hits Likes is okay Things from
that perspective Good So you are here with
the industry No standard it It represents the
average distance that jobs are value false from
the regression line So conveniently it tells you
how wrong the regression model in average Okay
Using on every using the units of response
variable which is our why smaller values are
better because it indicates the observation that closer
to the front lines So this is Let's
listen to it for dear life So what
can we started it right Small is very
close That is big Good All right So
you know more doing We have Senator Data
You can see that right center Okay These
statistics estimated coefficient divided by its own center
That's mathematical 87 6 But this is what
tells us it measures how many standard division
from zero The estimated coefficient is and it
is used to test the hypothesis that the
true value of the coefficient is non zero
You understand what decided This means See Okay
Why equals m X plus e What Simple
So why you called them express E I
need to understand whether the m okay the
M is non zero Because one day off
putting by equal to see is what my
ex become zero today There's another way of
putting Michael to see also when my m
becomes you Yes or no In both cases
there is no use off running this Yes
or no So I will hypothesize that my
coefficients I non zero Yeah negative Negative fight
But initially I need to test this battle
These are non zero north there Two ways
of making this Michael to see right one
is X becoming zero other can be am
becoming zero Okay so So there is a
major thes statistics is basically tells me no
to confirm the independent variable really belongs to
the morning Okay so in our case what
was independent variable here Placement rate It really
belongs to the model or not I have
guessed that Also know So he sets it
will tell me that So in this case
yes I did think Temple stunts All right
you have to look for the start This
is a visual signal that whether it's statistically
seen you know three means more do means
less oneness And then he misses that And
also you should not Okay placement rate is
what uh X in your in variable So
x 20 spontaneous So beatable represented difference in
predictive value of by for each one unit
difference in Excellent So basically very simple sees
concert Basically we don't know this constant So
one unit increase in X beat that one
units increasing like very simple Okay As you
mean it's positive but did not get this
negative So it will be and of degrees
no sooner Sandra is estimate perimeter as I'm
Basically it tells me that those standard error
whatever is coming is follows normal distribution and
what is normal distribution Ok so instead of
six helps me to understand that all the
independent variables okay all the expletive very bills
taken together are able to explain the variance
invite above chance levels around in a limit
limited Is that what after six R B
Scott said Islamic is with referred cargo complex
without that But there's so much neighborly where
they live Simple to understand this What Why
I wanted statistics It helps me to understand
whether all independent variables together are able to
predict why above chance levels or not because
in case this case only one producer is
there So but in my generation it's useful
Every single metal gear Obviously for me it's
obviously gonna tell us about our square with
some bad coring But if they're multiple variables
taken all that together help me to understand
whether it's able to explain the variance and
white basically able to predict why today above
chance levels are not now the sick Next
question is what chance limits So So tell
me OK tell me but very simple like
have you guys have you ever thought We
always say people you should be as less
as possible Yes I know it's a probability
re so far by property should be as
less as possible in what scenario tells me
if you say even less It is better
We say now even less That is better
And it should be less than 5% That's
all you know We all has made it
a special in industry but that's not true
But okay so less that is better So
5% is something between but why We always
think evil use illicit Have you been hard
So rest operators She said that right So
what do you mean by rest areas means
that you know we're dealing with data OK
And I'm going multiple competitions to come to
this number Okay which is mouth So I
always say my people you should be really
still less of them Listen means what There
is one chance Okay There are some scenarios
that this number has come out because of
multiple competitions Yes I know Sometimes you compute
some numbers Okay So many times that it
comes out to bear some realistic number but
you're looking for But there can be a
scenario It just came because off you multiple
iterations So it's kind of a fluke You
understand What the look in the middle of
the school look a right So you say
it can So that's why I want my
P value to be so less that I
minimize that Driskel's Okay So men s a
p value It's probably did that This even
does the areas to dearest right So that
way you know I want people who will
be less impossible If it zero is good
which I'm not possible there's always something that
you don't consider So now when I say
whether the printer variables Children to get to
predict the response well above chance levels means
above a P value which is very low
right Probably leave It is very low right
That's what I was And this will consider
every scenario every more than every right You
see this be here Even the mortar will
also be there You're seeing this serious It's
how much points for you know for something
So this means that since it is a
signal buddy there really is coming realistic and
definitely policemen read has impact on a number
of applications It means that Okay so the
next one is what coefficient of variation which
is our square What is our square It's
true person What does it mean Interpretation ways
off output variable which is explained variable by
the input Very big bus in the other
or knew medically Galata What simple egg numerator
rotate Dominant role They show me Don't need
to make it a go total variants Our
new minister Nicaragua Explain variance But yeah it's
good He made it total variance for more
than two variables My hair radio job I
mean who's missing kidney Explain who We Bedell
Plavix But the other thing about the movie
adjusted R squared you goto if I'm adding
multiple variables right Correct in the morning Let's
assume I start with X one Then I
already next to a directory explode x five
x six So there will be a point
that it you know right on let's as
you held in another is very simple One
I had X one next to right What
will happen Oliver said in my ask with
the shooter They That's all because I'm adding
a new variable into it So ask the
question it should shoot up But I just
said Out square tells me what whether it
added some statistical significance to it or not
That's way when you compare more distrustful better
imagery Because you're dealing with data Ari So
numerically when you add one more variable obviously
asking will increase Okay But it genuinely explained
the variants or not that I just heard
that scribble You see this formula Yeah One
minus R squared and minuscule So basically case
number of independent variables okay And is the
number of the guards Okay so So let's
says you I have Okay So in this
example what we were having I'm having every
place mentally Right So now once we add
a new variables in case somebody liberation Okay
you will come to know whether the explained
variance more or not So now next day
if I want you to do predict the
applications using the place Monday a number of
short of graduating Can you do that please
Quickie the same order as another variable which
is number of students graduating Do you need
our No You do it yourself Uh they
call you nothing on board Hey this is
interview Cushion What is a different issue That's
good enough Squid What happened here This is
all pretty All got so before this let
me explain Same similarly liberation Michael would have
been on the next one I just did
That's how it becomes my dealing there No
Okay From definition race That's how we change
Okay so before that So this one was
output earlier Correct Remember proficient 1317 500 So
this is how I'm gonna get the equation
for that And I can get this output
at the 60% rate Let's says you okay
This is manipulated data So don't worry how
you do it something important today So we
discussed this So now put ways you got
a fair idea over litigation or put Okay
so this is basically another important leading association
So for example if I ask you is
87 point favor It indicates December 5% of
the variation in body weight explained by the
independent variable ex Soviet my which we call
great Okay So correlation and proficient on immigration
OK so coordination commission visit the strength of
vibrated association Alright Regulation is a prediction equation
that estimates the value of X value of
I before any given X So that's the
difference Okay so this is again a oceana
and devotion It is Damn it is very
simple somebody can ask you what a different
in correlation innovation by video At this immigration
least squares You know what is least grizzly
relational And it's also car leased groveling And
there's something for oil is Have you heard
about it Or less ordinary least squares So
that is also immigration Only I'm giving you
different names so that you can you know
if you get your requirement document and the
sick Well it's more than you should know
What is it with this morning Okay it's
called ordinary least So why we call the
least Squares Huh Said Edits Kwamie Many may
scare off many mates gets you could basically
get again Yeah so it's yes or no
release Okay The square people think if you
have errors Kokoshin prevented me pass actually pretty
quickly For example let's have you This is
a bottle which is orange color This is
ridiculous Right In the local difference Other medical
when we got Virgilio Yeah that was your
audience Could anyone I never get over here
Yeah today to go anywhere together getting it
So you enter zoo in its formal minimize
cannot I want to make both radar board
on it So that is explained in great
unexplained the unexplained What is unexplained predicted once
is actually understand Have you heard of the
stone One is why another is Why had
What does that by predicted one right Why
is actually right And what is that Uh
minus Off this That's we call absolute inside
the 6 to 8 regarding this absolute eight
Okay So how good is high R squared
OK so I ask what may not always
be good There is arrested real issue We'll
talk about that Okay so there is something
called assumptions off winner Marlee Right What are
those Salumi Askar maybe better model Okay You
can add more predictors or interaction tones We'll
talk about that This isn't a good fit
Is this'd goodness off It is not solely
undetermined Basket Okay Always remember ask where is
not the only criteria to pick up a
model Okay There are other things which you
have to validate What are those We talk
So this is again fine today No matter
Asked his work it even let it was
how much six years ago today And uh
this is again everything is significant No intercept
becomes insignificant This observed this in December Games
insignificant because off graduation number coming in the
morning These kind of things you have to
pick and you're morally Okay So again same
odor interpretation one unit in grease and all
those things by academics that they know being
by experience No business So that is kind
off That that this kind of impossible Okay
Next one days Uh same thing Forget about
how to create a table between fitted value
investor tools so you can get a data
for him Okay You remember we you didn't
offer him didn't vote for him Student is
very Does it fitted values off the model
arrested illegally Dressing off the modern If you
give that this is how it comes right
that's arrested ill fated value Okay Actual differences
that I have what they defended to sit
out squares I have some data Okay On
on some Very well One Okay I'm using
that variable one to predict my okay Very
simple I'm able to reach to a prediction
All it which I say explaining the variance
with the help of our school Now I
want to improve that one way to improve
that is I increase the number of records
right For example of 100 records right now
I'll make you do one like records Let's
is you together with the same variable Okay
so but there will be a point there
My prediction power will be again 45% more
Something again That has to be done Adobes
I'm not able to explain Variance Invite with
the help of explained variance in next right
I should include more dicks into memory Okay
so what else do I'll bring another veto
Very Bill do Okay All the very booty
Let's is you OK So now what I
see my Askar just weeks So it was
60% I would with any deeper Let's assume
example Right So now this is one is
because I've increased the variables Okay so my
regulation equation they'll give me a bit about
squid because more things to compute by duplication
Maradiaga minus two Do one issue three How
regulation has been defined that I was missile
But you don't have to go there because
you are using soffit I'm heart circulation Injury
ladies to people used to do computation of
this So now that is one thing Secondly
Joe McKerrow whatever competition But it is really
valuable to the prediction Power or not for
that adjusted out squid is there And that's
why I have okay As a number of
independent variables in that formula over dissidents remember
one bio and minus game I just want
something That formula was there Hey no Medically
was there So now Hey it's elevating what
you do OK increase key ahead Was the
prediction power by anybody A glass The other
Okay I want to see other things but
I may have to explain the whole You
know how this regression equation has been derived
Guarded Very simple See Again But I know
I know they're multiple sources are like you
will find on Internet on books and all
that You explain in their own way But
the at corps level what adjusted Oscar tells
you that whether adding a new variable help
me toe increase my prediction power of the
model or not that is the Bottomley right
That you should always be looking for Yeah
you can do that Yes That's why we
were step place You've heard about step ways
Forward selection backward selection All right This is
how you do Mortally so if I have
100 variables right Just to see if I
wanted variable straight on not 100 have low
pc OK so let's say you have 10
variables right And I have found it I
started with two variables Bella model Then I
got a third one right Make it a
better one I get to a prediction power
Then I go to fourth One fifth one
I started with 10 together and then I
started removing one Women just significant nails could
remove here Fredricka back enormously Just ignoring We
get any closer to securing the yoga Great
was gonna move here It came about the
same tradition RV it was given Make equal
Make what I can try again Interaction domes
What that interaction does Have you heard about
this time before No you would have I
don't explain to you and we'll say yes
sir we have Okay hold on It is
though Okay So example See interactions on what
our interaction does Okay For example guys listen
to this You have employed you ws Everybody
knows about inflated of this right So you
can have him by name Okay employees edge
Okay Employing nationality Okay gender headache Right Okay
Or qualification Let's is in one moon How
much qualified the employees Okay Bachelor Masters post
graduate public a PSD or whatever Right Okay
so now see uh let's as you I'll
see Graduate Yes or no Or master yes
or no Right I'm Cal didn't like that
So if it to person is masters I'll
give one If person is not master I
give zero What does it mean What does
these kind of a big score Categorical But
I'm making it a numerical to compute what
I'm doing Making it my making it dummy
Something called dummy variable Have you heard that
done before Everyone understand What a dummy variable
If you don't I'll explain Very simple gender
Great male female rate Let's assume females One
male is zero Okay fine So female is
10 Okay So if I have data if
itself emails later I'll show one there if
it's merely a natural zero in that column
So basically earlier it was what male and
female It would have been difficult for me
to compute in the bottom So what I
did I convert it I don't know No
medical in nature One n zero Okay so
what I'm doing here Standing masters Yes or
no If Masters Yes One If Masters No
Zero name is fighting age again It's a
number I can again do this also If
it's young middle age or senior citizen categorical
classes differently Right You can have 1230 into
like that Okay Again What is this Employee
We just eat nationality Nationality Right So let's
is an Indian on Indian okay Indian or
foreign Make little You Okay So Indian one
foreign zero Okay so I have a one
zero Next issue Gender female one male zero
What It Okay I'll make whatever you Aldo
modeling Let's assume I'm predicting something Let's do
I'm relating efficiency off employees that says you
every chance of employed So what I'm going
I'm regressing this with all these today Logic
So at one point I get saturated with
my variables I'm able to explain maybe 70
personal villians Let's as you I'm just taking
hypothetical number here Okay So no To improve
my various other things which I can try
his interaction terms What does it mean I
would like to see employees who are muster's
on who are Indians How I can get
that I can multiply these $2 and make
another problem yes or no Right So one
in one hit make this also one zero
anywhere will make this a zero Yes I
know And then I'll regress again Another educational
model I'll see what This This new column
which is a master underscored Indian comes out
significant or not Yes I know So there
can be scenario that this can explain me
You get the efficiency of employed by the
on Indian who is master Why didn't Who's
mustard again I can go with the gender
Also females were masters Steam is mustard Indian
I want to see levels giving it so
basically I'm increasing number of variables So now
our swivel definitely agrees But adjusted Oscar will
tell me whether it helped me to explain
billions or not that they have the significance
confused over here Examples of little I can
take thousands of temple like this But you
do all these things they go simply Data
minibus Hey you remember that user Don't It's
art and science book Yes I know What
This is this artistic Yes I know you're
creating the river's for yourself You're being I
distinctive your data Science is what relation Morley
occupies a mortal El Gordo may already proven
bottom chemical number Nikolai I'll use regulation Take
a lickin was number go all Gordon Gordon
again Input Cadena that is art So never
stop yourself for trying anything in regulation world
because it's completely experimental in nature and there
is no yes or no for a perfect
model you can spend your age You create
the perfect model You might not be So
that's why I said you remember Stop Remember
you have this also Okay That's why people
lost get lost Integration with this is because
it's so injured If you're so against the
perfect relationship there sometimes you you know overdo
things that then there comes a problem off
Okay We'll explain toget socially start assumptions So
enter the studio times that normally distributed Okay
so you know normal distribution Ok second one
this constant variance off the items recorded Most
audacity Okay Independent The rebels have not coordinated
That is no medical charity If my dignity
existed to problem okay Every times are not
calling any of the independent variables What does
this mean So OK so the album did
good You're right Right Right So it basically
tells me that something is lived When I
see a pattern there it means something is
left which can explain variance for me Catch
it when you are better Shows a pattern
with your ex means what Okay something is
left in the data which can explain more
variants for exactly whatever makes you have considered
is not even look over there Very simple
from a simple Adams right No statistical very
simple terms What does it means That there
is something left in their data which I
still need to evaluate and improve My morning
How well do that We'll talk later Okay
Uh independent independent independent river should fall a
normal distribution again Relationship is linear Okay so
how you check whether other rested rhythms are
normally distributed Basically with the help off Souness
for the normal distribution skin it should be
zero k Ok Goodall says uh covers neither
too big nor do flag and there should
be no colors Okay so how you can
do that with the help of his two
grand box Sport you blood Hey these are
the blocks which helps you do that Yes
it does are independent of X So basically
what you will do you will plot Yes
You do its more little bit Forget values
of the morning It should come at this
back in this Is that nominated right This
is expected over Okay that no one is
you They should not fall apart I mean
that you should not be nearly nearby 10
or cordite take on anything Better try toward
that It did happen Okay Whatever Normality Let
says you it's no normal See we discussed
favorable cases Great What happens if people Because
they're not dead What do you should do
Yes Transformation And how you will decide which
transfer Michigan book Okay so if uh all
steps Tunis is there Long will work If
negative is this square would Look I'm still
giving you rule of thumb You can use
this one other V off coming up Which
transformation of what variable What is that in
your sacks Last you started This which transformation
for which variable How you decide that you
should always try to find a data distribution
off that variable on a cardigan You decide
which transformational And I'm sure they had a
distribution you have covered in your stats class
The next one is a gross audacity Okay
So word had rested as he comes from
Pedros means what different Okay that draws me
is different Okay Sagacity means conditional variance So
when we combine this it becomes different Conditional
variance Okay So our original assumption was work
There should be constrained villians which is almost
audacity So almost audacity is desired Correct Had
told his philosophies a problem So So these
are had close tenacity problems Obedience is not
clustering So see this This is Boston This
is Quadratic So it's casement Which Niigata s
case may not be a good number He
had a post from an energy If Hydra
solarcity's is there you should read that Okay
So had trusted us Steven Avery era villians
changes in systematic pattern that changes in the
X value Okay so sources there are other
learning models as income increases saving not only
English but the variability in stealing also increases
yes or no Let's see who makes is
income saving more different options Yes or no
was the time I really like I was
a fresher I only Stephen fix it Was
it Once I became a man is very
I start I made by the very house
right So I said putting money for my
house So So that is there that that
will always with them So we have to
identify As for domain where the electricity is
good for my domain or not Okay sometimes
ask for your meat will be there You
can't do anything Okay So reason for that
can be There can be some omitted variable
that you have not consider some variable That's
why it's there Que nous and distribution on
the interrogator transformation So you might have you
know you some in credited expression It's a
linear X instead of quadratic x Okay so
how did I will just tell you so
First of all detection so one is scheduled
block today Other way is that you can
plot squared restrictions each of the independent variables
Already the number of regressive is large Let's
assume you're addresses a very large lord the
square residues against a predicted way that will
also tell you because it will take effect
off all this because you have protected by
an already all the variances there Imperative You
can check that So again Possible batons So
what we learn Linear model assumption First assumption
is what And it should be normally distributed
Right How to go about their bottom angrily
Take it unless Kunitz technique produces or the
naked wants outlasting Good Okay So getting in
with the held ball Mr Graham Cube Lord
and both sports Right Ticket So that's whatever
second is I want Was it really good
indeed Brendan Dolf you know and you know
x to get right that 2nd 3rd 1
is conditional Variance Juanita Human Lucky across the
dusty inhumanity home most of Stotz Okay They
should be home with celestic but it to
get and how we do that So before
that date someone can ask Why is it
so important to take a personality right so
important Okay so one is that see bias
standard estimation inferences Ways on oil s estimation
becomes inaccurate you know So because you will
go and say that you know that this
coefficient disseminating these will get this But you
see still the pattern is there so There
can be scenario which will make your assumption
or the prediction inaccurate A possible solution It
is always there What transformation the best way
to do it when this transformation but really
have to hit the exact transformation How we
will do it Let's consider scenario Suppose in
immigration more model output plots We see that
the value of school wrestling increases with X
Okay To elevate this effect of engaging variance
of X we divide both sides by X
So by doing this there is a new
editor which is absolute Bay X We've considered
that as you right Yeah Other Mexican perhaps
F one bank Santiago was common A critical
year you get Okay so let's look at
the scatter plots So this is what is
anyone So do we see any pattern here
First of all estates according Maybe I have
more diet William or clear But if you
see this it started from here slightly goes
up up up up up up up up
Like this kind of go They Okay so
I remember If a degree of Parliament is
more I can be read by that same
room But it will come yes or No
So now I do it X in there
Okay So once I do it backs what
happen I think he's going So now I'll
do every morning off that very buzz and
that will be the better Morning So my
headdress velocity goes away Mr Short could do
about may be getting okay huh Because I
was progressing with them higher degree Now I've
reduced the degree of the and now I'm
getting in the scene The guy was gonna
sing it Excluding Maybe because excess showing you
this that says you listen Radio Yeah after
placement 800 the over This is maybe what
is religion wagon with Danny Let's have you
the other word and squared 200 death and
other administrative aide care The other minutes produced
for the big reveal in the or many
escorted back inside the restaurant Yes or no
motive I'm lucky given assumption Have also he
had I'm just jesting there No I just
just you don't know So again this is
something that I have shown you which I
have come up with You will not get
into this perfect world Okay You have to
try multiple ways to get to their It's
not that you try one transformation it will
work Okay Other transformation A lot of squared
rescue delegates X might show leadership as opposed
to quadratic Then the square root transformation divide
both sides way good optics Lovers of restoration
regressed logged by on log X compresses a
scale in visual Very much damage it That's
reducing the range of millions You have locker
to come back at their school So the
angel visit reduced so that we can also
help you to get their head restlessly away
whenever you see a problem Okay which is
predictive in nature All this I do get
toe one answer to it You know what
What does now put I'm looking for Okay
so I'll put Can be to base right
one output can be It can be either
a number yes or no Hey sales value
profit value they number efficiency Anything number our
skill One Is that or drank anything like
that right Second open can be What plus
class means what Yes No good bag Fraud
Non fraud Sick Ellie some of that Do
you uh fraud All this class Is it
able to do both regular Misha don't rocky
below me And yet I am number Yeah
yeah About exercise Carletto When you ever hit
a problem and you do this Annelise is
in your brain You're working so easy your
way Depending on what kind of response variable
you have to come toe Your technique will
be decided How Classification yet Ignition So you
have regulation government classifications to say yoga regulation
can go to my leg Classic mission Stop
kidding Mom Make Lisbon Jaggi came with the
deregulation Give me too many businesses Started first
about my general Yasin Phyllis Campano screen group
Koroma or I can go toe even in
determining you will study board this Don't random
for is and all Yes they are declassification
here Promoted largest to system Then I have
to start with nearest neighbor This order terms
by the time I get up Alien But
he is switching him with a kiss particle
for longer But do you hence Simple Very
simple If you're not from the politics work
these If you study like this I'm sure
your life will be easy Complex were likely
to nobody here at this symposium Okay first
foundation stronger This brings us to the end
of this tutorial on linear regression Before you
guys sign off I like to inform that
we have launched a completely free platform called
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