Proving a hypothesis

[HarveyH123]

[FONT=&quot]Hello, [/FONT]
[FONT=&quot]I need help!![/FONT]
[FONT=&quot]Im totally new to any time of statistics. This will probably be easier on SPSS or Minitab - I have access to both but would also be open to using excel if i can.[/FONT]
[FONT=&quot]
I have some data.[/FONT]
[FONT=&quot]Its 1500 rows like the following. - The data is from a OEE system. It is the occurrences that an operator clicked adjustment to the machine within 10 minutes of the shift[/FONT]

[TABLE="width: 164"]
<colgroup><col><col></colgroup><tbody>[TR]
[TD]Catagory [/TD]
[TD]Within 10 mins[/TD]
[/TR]
[TR]
[TD]Type 1[/TD]
[TD]Yes[/TD]
[/TR]
[TR]
[TD]Type 2[/TD]
[TD]No[/TD]
[/TR]
[TR]
[TD]Type 1[/TD]
[TD]No[/TD]
[/TR]
[TR]
[TD]Type2[/TD]
[TD]Yes[/TD]
[/TR]
</tbody>[/TABLE]

[FONT=&quot]I want to do a statistical test that tells me much more likely (or some statistical measure) Type 1 is to cause the operator to click the measurement within 10 mins.[/FONT]

[FONT=&quot]Type 1 occurred 215 times and type 2 occurred 987.

[/FONT]
[FONT=&quot]Is this what a Chi 2 test is for?[/FONT]

[FONT=&quot]Ive watched some videos are struggling to understand the results.[/FONT]

In short i want to prove that;

Type 1 is more likely to make the operator click the button (a yes in the second column)

[FONT=&quot]Thank you [/FONT]

 

[Wicker]

We both need to dumb this one down a bit. I see no reason for a Chi 2 test and I think that is why you are confused.

Also I would like some clarification: Type 1 and Yes is far more likely than...? Than Type 1 and No? Than Type 2 and Yes? Both? Than any other combination?
You say that there are 1500 total rows but say Type 1 occurred 215 times and type 2 occurred 987 times. That totals 1202. Using the data provided, I am uncertain as to whether 1500 was an estimation/hyperbole or if the 1202 are if they showed up as "Yes" (or as "No").

As it stands however, if 1500 is a precise total, you have your answer right now.
1500-1202=298.
If we assume the 1202 is the total number of yeses, there are 298 nos. No matter whether those Nos are Type 1 or type 2:
Type 1 occured less than type 2. If against the total, hypothesis fails.
Type 1 yeses are already smaller than type 2 yeses(assumedly), again failure.

What I started making before I realized I did not have all the information necessary for your final hypothesis were these:
[TABLE="width: 768"]
<tbody>[TR]
[TD]all count[/TD]
[TD]Total number of entries[/TD]
[TD]Type 1 Count[/TD]
[TD]Type 1 Total[/TD]
[TD]Type 2 Count[/TD]
[TD]Type Two Total[/TD]
[TD]Yes Count[/TD]
[TD]Yes Total[/TD]
[TD]No Count[/TD]
[TD]No Total[/TD]
[TD]T1Y Count[/TD]
[TD]T1Y total[/TD]
[/TR]
</tbody>[/TABLE]

Each "Total" referenced the prior column. For example total number of entries was the D column, so D2's formula was: =SUM(C:C)

Count columns looked at the first two columns (the ones you provided) and made their equation from there: =IF(A2="Type 1",1,0)
A2/B2 was the cell I was looking at, "Type 1" was what I was looking for (as supposed to "Yes"). The 1s and 0s were for the sums/totals, since I assumed at the end of it all you would want a percentage (number of Type 1 yeses over ... that which you wish to use for comparison).
For the all count my equation was slightly diffferent: =IF(A2="",0,1)
This was in case you wanted to use the absolute total in your final equation.