Okay, We are now at the labs off module eight.
uh, in this lab's your
task would be to create some people tables out off road dated you were given in file model eight labs that ex Ellis X.
So let's go and see that road data.
And these are results from quality
testing in, ah, company that makes
So we had random testing off finished products.
Ah, total some five different
And we have eight different part makers, people who work in machines.
And we have three different test types. And for each of these test types, we have the expected value, and what we have is a measurement. And then, according to the measurement we declare, the tester declares, the part passed the test or fail.
random tests, not all the parts it tested,
also the type of the test and,
um is also suspected by random. So, um,
so here they are. There is quite a lot of them. And what we need to see
the amount off failed and passed tests per
then for type a per per part name, sorry
and protest type. So we want to see if the testing, because with the test type, it should be pretty much equal.
There should be quite a lot of differences because it's it's testing the ability off the person
and for part name. It's also relevant to see, So
we just think you're let's say, sell it through.
We go to insert, we goto fill a table
and we just take okay because we want it on the new worksheet and, ah, the XL has automatically selected for us the
table arrange, which
this entire data set which goes all the way to road 300 months.
So you just click, Okay, we get the new sheet, we get the
people table feels that we can
put wherever we want. So we go first with part maker. So it goes into Rose
and then we have test results that goes into will use.
And we also want test results to go into cones.
And we got our first
So we see here how many fails Each of these,
and the grand total of testing per person, so This is random Sue seat, where ease from 33 to 41 test Because we picked our parts finished parts. It's random. We subject into testing and some of the people get there.
Parts tested a little bit more, but statistically so we have 300 tests, so it's ah,
it's a reasonable belief, statistically accurate testing. And we see that Bjorn is having the highest number off fails. And the Louisiana and Diego they have the this most number fails.
So this is the first,
uh, people table that we wanted to create. The 2nd 1
we again go back somewhere inside this and we go to insert and people table and we wanted to create the new worksheet again.
So we've got to now work, shoot three,
and now we're going to do pretty much the same thing.
So now we want the part name in the Rose
and we're going to do exactly the same thing. So we're going to put test result here,
just result in combs as well.
we have the biggest number of fails
and but it's not so big difference that it would generate any,
notion that something is wrong with it. So the manufacturing process, this is the way we, for example, see if the manufacturing process has any particular
is prone to generating the fails. So the wrongly manufactured parts
and we see that this is all pretty much
this is this is okay.
And the 3rd 1 we go back
three clicks and we're here. We again go insert
Um, and then we click, okay?
And then what we want to do is to see if the test types generate the equal amount off fails.
So we go wrote to be test types,
and we won't test results here,
and we want test results here,
and we see that we have identical number of fails
and the total number of tests are
around 100. So these this whole depth is a little bit
more there. So we have 110 cases of randomly generated
items that we picked from the whole bunch of new
the manufactured parts.
But it's all around 100 so very close to 1/3. And we have that we see that we have
identical number of failed. So we can conclude from this
that this testing methodology is rather good and that the
the conditions we set for fail or pass
are actually really well done.
Ah, this is all about this.
And if you have done it by yourself, it's good if you have ah seen this test as well. This is this example and did
step by step like I did. It's also okay. I would have appreciated that you have done it by yourself from the beginning. But it's up to you,
so thank you and bye.