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Finance: What is Regression Analysis? 7 Views
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Description:
What is Regression Analysis? Regression is a form of statistical analysis that goes back to trading history and isolating a particular variable to analyze how it may have affected other variables over a time period in influencing performance of an asset. Interest rates, earnings, volume, and a host of other categories are all prospective regression analysis variables.
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- Life Skills / Personal Finance
- Finance / Finance Definitions
- Life Skills / Finance Definitions
- Finance / Personal Finance
- Courses / Finance Concepts
- Subjects / Finance and Economics
- Finance and Economics / Terms and Concepts
- Terms and Concepts / Banking
- Terms and Concepts / Bonds
- Terms and Concepts / Charts
- Terms and Concepts / Company Valuation
- Terms and Concepts / Credit
- Terms and Concepts / Investing
- Terms and Concepts / Metrics
- Terms and Concepts / Stocks
- Terms and Concepts / Trading
Transcript
- 00:00
Finance allah shmoop what is regression analysis Regression and elses
- 00:08
no it's not a therapy session in which your psychiatrist
- 00:12
tries to figure out why you've gone back to using
- 00:14
passive fires It's simply this the process by which a
- 00:17
siri's have different independent variables are compay haired to a
Full Transcript
- 00:21
dependent variable to see which might have the greatest effect
- 00:25
on the value of the dependent variable All right Well
- 00:28
okay That's The theory of it anyway But what about
- 00:31
some practical examples Well what are these graphs And what
- 00:34
do they tell us Well let's take pete the pizza
- 00:36
joint guy How does he know what's bringing in customers
- 00:40
Is it his new burrito pizza or the virtual skee
- 00:44
ball machines he put in the back Well we can
- 00:46
use some math here to find an equation Usually a
- 00:49
linear one Linear regression Very fine Mathematic sport That best
- 00:53
matches the pattern in the data Then we can see
- 00:56
how close the points are to that line and that
- 00:59
you know will solve our burrito pizza Steve all conundrum
- 01:02
and help pete manage his business better Well the closer
- 01:06
that data points are to the line the more likely
- 01:08
there's some kind of link between the independent and dependent
- 01:12
variables well it doesn't mean one variable causes another It
- 01:16
just means they're linked somehow Like what about the link
- 01:20
between ice cream sales and drownings Death that's a morbid
- 01:23
connection but see how cloaks the data points are to
- 01:26
that special line So yeah there's absolutely some meaningful link
- 01:30
between ice cream sales and drownings deaths greater ice cream
- 01:33
sales on a given day is always linked to mohr
- 01:36
drowning deaths on that day Why what's the linking factor
- 01:40
Flavor of ice cream of the amount of sugar in
- 01:43
the ice cream Too much in ice cream fat and
- 01:46
crap and stuff accessibility to public swimming pools Well clearly
- 01:50
ice cream isn't some insidious killer drowning people who get
- 01:53
in the water without waiting the records that you know
- 01:56
one hour But there is a link between those two
- 01:58
variables Think about it As it turns out hire isis
- 02:01
scream sales happen on hotter days so heat or sunshine
- 02:05
is the linking factor Mohr people go swimming on hotter
- 02:10
days when more people swim while they're going to be
- 02:12
more drowning possibilities anyway so i scream sales in drowning
- 02:16
Deaths are linked but ice cream sales don't cause drowning
- 02:20
death Got it No causal link there Similarly check out
- 02:24
how the points in this graph are not really close
- 02:26
to the line at all There's no link between your
- 02:29
shoe size and your g p a you know unless
- 02:32
you buy huge shoes build a mini computer that fits
- 02:34
in the extra space in your shoes and use that
- 02:36
to help you you know cheat Don't do that by
- 02:39
the way Always cite shmoop anyway back to pete the
- 02:42
owner of zaza pizza Pete almost has more customers lately
- 02:45
than he can handle while the lightning is striking Pete
- 02:48
wants to find a way Teo you know bottle it
- 02:50
The thing is he's made to significant changes to his
- 02:53
restaurant and he's not sure which one is more responsible
- 02:57
for the influx of people tossing money of him Is
- 03:00
it the virtual skee ball machines Or is it his
- 03:03
new burrito pizza Is there in fact any link at
- 03:06
all Well it could be both that are responsible but
- 03:09
that's beyond pete skill and this course to determine he
- 03:12
can only compare one at a time to the increased
- 03:14
Revenue so pete picks different days and plots the number
- 03:17
of burrito pizza orders against the total money made that
- 03:20
day Notice how the data points seem closely to follow
- 03:23
an imaginary line there fromthe lower left to the upper
- 03:27
right In general we can see that low burrito pizza
- 03:30
order numbers are paired with lower daily revenues Also hi
- 03:34
burrito pizza orders are paired with higher daily revenues high
- 03:39
against high low against low will the closer the points
- 03:42
are too that imaginary line the more likely it is
- 03:45
that the independent variable in this case burrito pizza sales
- 03:49
is at least related in some meaningful way to the
- 03:52
dependent variable like it's the pendant on sales of total
- 03:56
daily revenue under our tea i eighty for their or
- 03:59
phone or computer or whatever you're using first week pop
- 04:02
up our data into the list by pressing the stat
- 04:04
button Then enter we put in the ex data in
- 04:07
list one there l won and the y data enlist
- 04:10
to l two Now we press the second key and
- 04:13
the mod key to get out of that menu If
- 04:15
we don't get out of that menu well we're just
- 04:18
begging to screw the pooch here so get out Get
- 04:19
out now we bash stat move over to the cal
- 04:22
commend you and choose option for which is lean wreg
- 04:25
a x plus be all right That's in texas shorthand
- 04:28
for linear regression Yeah on the menu it brings up
- 04:32
moved down to calculator and then press enter if you're
- 04:36
cal doesn't show the r squared and our values Well
- 04:39
you need to hit youtube in search for how to
- 04:41
turn on stat diagnostics t i eighty four there's a
- 04:45
bunch of important info in the results that we need
- 04:47
to check out most importantly for pete's sake is the
- 04:50
value of our the closer that our value is toe
- 04:53
one or negative one The closer the points are two
- 04:57
best fit that possible line Well the closer they are
- 05:00
value is toe one for graphs with positive slopes or
- 05:03
negative one for graphs with negative slope the stronger the
- 05:06
link between the independent independent variables there right That link
- 05:10
is called a correlation right They correlate it doesn't mean
- 05:13
higher daily revenues are absolutely caused by burrito pizza lovers
- 05:17
but it does suggest there somehow correlated and that correlation
- 05:21
is strong anyway The a and b values that you
- 05:23
see on the display happen to be the slope And
- 05:25
why intercept of the equation in the best possible line
- 05:28
pete can use these to predict daily revenues if he
- 05:30
knows the number of burrito pizza sails in a day
- 05:33
But that's a different video Pete still needs to know
- 05:36
if virtual skee ball is so exciting that it might
- 05:39
be more responsible for daily revenue jumps He also plotted
- 05:42
the number of times virtual skee ball was played in
- 05:45
a day versus those same daily revenue figures Well guess
- 05:48
what The points look like a cloud instead of having
- 05:51
any obvious linear pattern Well if we pop that data
- 05:54
into the cal can run the same linear regression process
- 05:57
again we get a very different our value We can
- 06:00
also just see that the points aren't that close to
- 06:02
the line that our value is not close toe one
- 06:05
at all In fact it's cozying up to zero like
- 06:08
it's Ah you know frat boy and zero is well
- 06:11
every girl within a forty meter radius when they are
- 06:14
value is sniffing around zero like that Well it means
- 06:17
there's some kind of very weak correlation between the independent
- 06:20
and deep and it variables We can't stress enough that
- 06:23
this is in proof of any kind of cause no
- 06:25
matter how weak between the two variables just that some
- 06:28
kind of correlation exists and that it's weak pete has
- 06:32
some evidence that the increase daily revenue is almost all
- 06:35
about the burrito pizza and only a tiny bit due
- 06:37
to the virtual skee ball crowd But this is a
- 06:40
big but pete does not have proof they are Value
- 06:43
just suggests that there's some kind of link between the
- 06:46
two variables Not that a change in one variable causes
- 06:49
a change in the other Still with that significant of
- 06:52
a difference in our values pete is pretty safe in
- 06:55
thinking burrito pizza is probably more important in driving higher
- 06:58
revenues than virtual skee ball Pete used a regression analysis
- 07:02
on the two different variables he thought might influence his
- 07:05
bank account the most any decisions he makes killing forward
- 07:08
should probably be menu focused as opposed to you know
- 07:11
attraction focused and still he can't forget the virtual skee
- 07:14
ball entirely It is probably a teeny bit responsible for
- 07:17
the increased mullah in pete's case the correlation between the
- 07:20
variables was positive which means that as burrito pizza sales
- 07:24
or virtual skee ball plays increase well so does daily
- 07:28
revenue there also negative correlations here is well where as
- 07:32
one variable increases the other variable decreases Case in point
- 07:36
carla's customs right next to pete's place carla has customs
- 07:40
takes broken down golf carts and file suits them up
- 07:43
They recently made three distinct changes to their builds and
- 07:46
have noticed a huge decrease in the time it takes
- 07:48
one of their cards to complete the forty r dash
- 07:51
will car lot I wanted to figure out which change
- 07:54
might have been the most responsible for the decreased time's
- 07:57
Carlota plotted forty yard dash times versus the size of
- 08:01
the rims that these things right here they're diameter and
- 08:04
got an r value of negative point one seven nine
- 08:08
when she ran a linear regression of the data then
- 08:11
forty yard dash times versus the cylinder diameter there and
- 08:14
got in our value of negative point six to eight
- 08:18
when she ran a linear regression of that data then
- 08:21
the forty yard dash times versus the nitrous oxide concentration
- 08:24
Is what she ran and she got in our value
- 08:27
of negative point nine four eight when she ran a
- 08:29
linear regression of the data Well guess what The simple
- 08:32
fact here all three plots have some kind of linear
- 08:35
relationship It does mean that there's some kind of correlation
- 08:38
between each of these three variables rim size cylinder diameter
- 08:43
and nitrous oxide concentration you know in the forty yard
- 08:46
dash time of the golf carts with her mostly electric
- 08:49
But we won't get technicals here since all the grafts
- 08:52
have negative slopes and the correlation with nitrous oxide is
- 08:55
the close to the values to negative one The nitrous
- 08:57
oxide concentration has the strongest correlation to decrease forty yard
- 09:02
dash times like it's bad for speed reduced nitrous oxide
- 09:05
in your golf cart it's important to remember that carlotta
- 09:08
can't say that the nitrous oxide concentration is the direct
- 09:11
cause of the faster times All she knows is that
- 09:14
there's a link or a correlation between them Still with
- 09:17
further experimentation carlota could establish a causal relationship Carlotta explored
- 09:22
the relationship between three different variables and their possible effect
- 09:25
on the time to run the forty yard dash using
- 09:27
regression analysis She determined all three variables had some kind
- 09:30
of negative correlation of the times To run the course
- 09:32
as the nitrous concentration or the rim sides or the
- 09:35
cylinder diameter increased well the forty yard dash times decreased
- 09:39
Clearly the nitrous concentration had the strongest correlation Carla should
- 09:44
probably focus on that concentration for the greatest decrease in
- 09:47
times She knows she can't ignore the rim size nor
- 09:50
can she ignore the cylinder diameter as they all contribute
- 09:53
Toe overall Golf cart forty r dash speed times Right
- 09:57
regression analysis will never tell us which variable is the
- 10:00
actual cause It just kind of gives us it's along
- 10:03
the way it's best to make decisions informed by all
- 10:06
the variables that are correlated to the dependent variable And
- 10:09
as kelly clarkson famously saying you know this independent variable 00:10:13.231 --> [endTime] something like that miss independent variable
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