English language

How to pronounce unequal in English?

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okay so what we are going to do this at
this point is to look at t-test so T
test is to test whether is the average
of two samples is statistically
different from each other so then I'll
just write down in this case for example
if I want to do the not everyone test
has now hypothesis this is that the
average marks or English equal to the
average marks or Chinese so the
alternate hypothesis will be the average
max for English is not equal to average
mass of Chinese so we have our set of
ten students here the English Maths and
the Chinese months so are they
comparable now three pairs there are
three different versions let me just
write down the energy three different
types of cheetahs while the three
different types the first one is known
as a cat t-test the second one is your
two samples t-test with or rather
assuming equal variance and then you
have the third one which is your two
samples t-test without assuming equal
variance of assuming non equal variance
so two temple t-test assuming an equal
variance so these are three different
types so how do we do that number one is
you can actually look at the variance so
you can actually look at the variance
it's just a square of standard deviation
so we can't you find a standard
deviation of this English and Chinese
maths so standard deviation use the
deviation s so this to understand
deviation you see that there is a
cheapest difference in standard
deviation but can it us is it
statistically significant or not so what
you can also see is this is the average
average this is what we have done before
so the average for English is 73.3 the
average of in Chinese is sixty seven
point one is this tool that takes me
different from each other now the
easiest way to do your t-test is to use
the formula just to calculate the
p-value so we can calculate the p-value
of really say that two sample t-test
assume equal variance due for two tempos
assuming equal variances all right
so let us put here how do we do that the
function or the formula is using P test
now you realize that did I go here
there's a three four options or rather
form for items that you need to put in
for parameters between array one is one
of the data set or one the samples array
two is another sample
so now array one I have for English
right well for Chinese the tale is do I
want to do it one tailed test or to do
test usually we want to do a two tailed
test number just look at it here it
tells you that one is petty test two is
assuming equal variance and tree is
assuming unequal variance so we want to
use two assuming equal variance to take
two now this is the p-value which means
that although the average is different
there is still average is numerically
different but there is still six percent
chance that these two are indeed the
same the average mines actually supposed
to be same so in this case based on the
threshold of 5% we cannot reject a null
hypothesis so here we say that we fail
to reject
now hypothesis this is the conclusion
okay which then leads do we accept now
hypothesis how about an assuming an
equal variance a assuming unequal
variance we can just do the same method
except now is we change the option from
to the type from 2 to 3 now just give us
the unequal variance and there is 6.3
percent chance that this true the
average transfer English is equal to the
average max for Chinese
so assume you come back to we cannot
reject the null hypothesis you will
realize that when you do t-test if you
can assume equal variance the p-value
the probability value will always be
lower than assuming unequal variance so
how about petit test because this each
grow is one student so there's a pairing
this is John this is this is also John
so there's a parent effect so we can
actually do a pair T test as well so
Pepe test the p-value oops okay we just
changed the type to that one now even on
t-tests it is actually 11 percent chance
that the null hypothesis is correct so
same thing we cannot reject the null
hypothesis okay so this is using formula
can we do the same using data analysis
test cuz there's no problem there so let
me just pull this out a little bit okay
and I just kind of throw this aside here
okay so how do we do that we go to data
analysis the data tab data analysis now
same thing if you scroll down there are
three t-test patty tests two samples
assuming equal variance and two samples
assuming unequal variance if so the
method is identical in fact if I click
on them you see the screen the format is
identical so let us just do an assuming
unequal favorites to see whether do you
get the same result as here so this is
unequal variance and just highlight this
is the unequal variance but the p-value
is assumed
Varun's so now variable 1 range
highlight the entire English so I
remember I highlighted what English okay
so which means that I have to click on
the label because I highlighted a label
so the other one I also make sure that I
had at the label as well all right then
I click on the upper range so I find
somewhere to throw my results they said
pretty here
ok now this is what you get if you see
the average for English and the average
for Chinese is the same as what we have
just now so let me color is like green -
for English or Chinese okay
the variance is just Sami 27 is just a
square of 5.2 and square of 0.3 is 69 so
what you end up with is two till so this
is the p-value when you have a
two-tailed test so this is the same as
what you see here that's it if you want
to do
patty test you can repeat it it will
still give you the same result from here
all right so the question now becomes
should I use - I assume equal variance
or unequal variance how do I make the
decision for that in some other software
this there's other methods that for
example in SPSS you can use the pins
test but in Excel there is known flow
means test the easiest way is to do a
steps okay here in the Dionysius that
there is this F test two samples for
variances okay so we click on that so
two sample variances dialog box is very
similar to your t-test class just with P
the whole thing labels and I put output
so I put over here alright so what does
it mean it means that although there is
a significant variance but there is
still 0.9 percent chance that the null
hypothesis is true which means that the
two variances these two variances may
actually be the same therefore using
this result we can actually do a t-test
assuming equal variance however if you
do not want to go through this step to
test for the variances equality of
variances simply assume any for variance
that is also fine
alright let's offer not see you again

okay so what we are going to do this at

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unequal::adjective | Spocien Dictionary

unequal

adjective

/ən'ikwəl/

lacking the requisite qualities or resources to meet a task

Type	Words
Synonyms	inadequate

she was unequal to the task.

unequal::adjective | Spocien Dictionary

unequal

adjective

/ən'ikwəl/

poorly balanced or matched in quantity or value or measure

Type	Words

How to pronounce unequal in English?

unequal::adjective | Spocien Dictionary

abandonment

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unequal::adjective | Spocien Dictionary

abandonment

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