Now, let us do another example of this is that this we did that to take this.
Now let us see another example.
It is given that for being a building specification, the extent of that's where the spike will be 2500
kg parfit for.
So this is a given for a specification of pipe.
So to test this way that the pipe make the specification are not to take this sample is taken in, a
sewer pipe is taken.
And what the resistance is two thousand five hundred fifty kilograms.
Barfoot with a standard deviation, 120 kg powerful.
See the sample size is less than 30 n in population.
Variance is not given.
It is calculated from the sample.
So we are to test at five percent level of significance whether the pipe meet the specification or not.
So here you see here.
First of all, this is detailed because 10 percent less than 30 and it's still a deviation not given
in the population.
It is.
It is given in the sense that it is given.
It is given in the sample size.
So we have to.
Cities and states, one or two, 230 here have to claim is more than so, it will be one of one till
death.
So how can a formula test, first of all, have to say that it is not more then it is less than or equal?
OK.
Both are right.
You can say equal to 750, 2500, or you can say less than equal 2500 if you like the both.
Right.
So first of all, we in the step one of the hypothesis states you have to formulate null hypothesis
is not about that is it is equal to something that is a state of the pie is two thousand five hundred
and that is the five does not meet the specifications.
Specification and alternate hypothesis will be more than that is the five mista specification.
This is the real empathises and already happened.
The second one we have to find out Lalibela significant is given five percent level of significant.
Who is is one zero five.
So second step is finished.
What is toughest in today's debate is our we have to find the.
Statistics, this is statistics show as it all the time, precisely, the target is 18 and still alive
is not given.
So it will be Thetis for any one sample, only one sample.
And this is a formula.
We know it already.
So now put all the value in the formula is valid.
Two thousand five hundred fifty who who go from the same Bill and Melinda population mean from the population
is given two thousand five hundred and a standard deviation 120 and sample sizes 18.
If you put all the value you will get the value of the one point seven seven four one telltales or don't
need to think that we have to put it as it is.
So now we have to calculate the value of we have to find the value up at every value of these from the
table, and we have to consider digital freedom, which is 70 now.
And remember that our lives are significant for one point five.
OK, now let us is that they will actually, if you go to the table for it to see at the end of the
slide, I put the table for you.
If you go there, see, first of all, you have to first all one dollar to this hour is one because
alternate hypotheses get up then.
So significant one five.
OK, we still have plenty of I don't know, one, two, three, one, two, three, four, five, one,
five.
And first first column.
The Reserve is seventeen because it Sempill on minutes one seventeen.
So if you go there 70 degrees of freedom, that rule you have to stop here.
Where is the significant point, zero five for one day.
So you did one point zero one point seven four zero.
So our values one point seven four zero.
So now let us make the conclusion, what can we make so so we go to the table, the value is.
So let's see.
Uh.
So we got the tabloid value it one point seven four zero zero, what is the key to it is always better
than that.
Well, it's all good to get it into the one point seven.
Now, hard decision.
Can we make this fight since our value capital value one point seventy seven, who is greater than our
average, whether it falls in the category done well in the value falls in the category one one seven
seven greater than one one seven zero.
That is is false in that category, John.
When the value of the dollar falls in the category zone, we do not accept our hypothesis.
We accept the alternative hypothesis.
That is, we are not going to accept that is the pie does not meet the specification, that is, which
is we call 2500 or less than equal to the 500 we are going to accept.
The alternative hypothesis is greater than that is the pie means this is justification.
This is the way you can do.
By commission method of categories, a reasonable justice, how can you do it by P-value?
So what we did for the people who fought to tell us about why I wanted to tell until this, how can
you get so leave in one hour?
I don't happen to get a then two thousand five hundred.
So our people with the probability that we get more than one point seventy seven, if it is less than
a people value, would be probably less than one point seventy seven.
And to tell so we called the probability greater than an invaluable tool for that one telltales for
the right altitude.
And so we have to find the value of probability p value or will they want to get at the one point seventy
seven and have to consider degrees of freedom loses its freedom.
Now we know quite well together more one point seven.
So how can we make it less than one minus Bovril to one point seventy seven?
Now if you go to the Excel, we know how can we get the Excel out to use the Excel for the one point
seventy seven degrees of freedom?
70.
Let go to the Excel if you go to the Excel C for this P value again for the P value.
If you equal distribution p calculated is Altegrity value is what, twelve seven seven degrees of freedom
17 and second condition.
We have to give the comma and you have to through because you are taking that our value is less than
one point seventy.
First we have to do the first one.
OK, then we will get it.
That is the for the probability and less than one point seven seven four seven zero nine five to six
one seven.
So let us c.
So let us see, so we got the same like this one line, five to six, one seven, one minus, we'll
get it, one zero four seven three to nine, which is ten point five four in the Excel.
You can get that P value in this way if we use the R programming to our programming.
Also, I need to one minus all minus the value of T as it is.
We give it only to the degrees of freedom.
We take it so we'll get it.
One it, same value.
One point zero four seven stays in the.
Our program, our programming, we contested.
So let us test into our programming for first on you get it second on one minus, Petey.
OK, Petey, poverty of redistribution and the value is one one seven seven.
This is statistics and the one 17 17.
Get it, you'll get it.
See zero point zero four seven three the same like the Excel.
We got the value.
Now, if you go to our slide, so for our programming, our Excel, we get the table value of people.
Who is one zero four seven?
Who is less than one five.
So it's five we have to make that decision is before we get the P value, p value and it's the five,
we have to make that decision so we know if P value is the point.
Five, we have to reject the null hypothesis, except that this is our P value plan 047, which is almost
five.
But still it is what it is, less than five as it is ten point five.
We're going to raise it.
Then I'll put the accident on, put the same like the conventional method.
What is it then?
I'll have to accept that this is the pipe meets the specification.
So this is the way we can make the calculation by conventional method and by the P value.
So please try to remember the P value method is less than point five.
We have to reject the null hypothesis at five percent level of significant P value is more convenient
and easy to use.
OK, this is a way you can do it in the Excel or in the R you can do in the python.
Is that our other other program as well.
So this is the way.
To find that P-value and make the conclusion for the one test, if it is less than just take the P-value
or less than one percent and seven only to do the minus one, OK, you can get directly from the exit
or from the R programming.
That's all for Ontake test.