Later on, we will use contingency tables again. The table displays sample values in relation to two different variables that may be dependent or contingent on one another. The table helps in determining conditional probabilities quite easily. Because our critical value is greater than our test statistic. A contingency table provides a way of portraying data that can facilitate calculating probabilities. So these are going to be our hypotheses that we're testing. However, if it's not equal to zero, that means that there is actually some change that occurs in why as changes in X occur. We have an alternative hypothesis that beta one is not equal to zero, right? Because we want to know whether or not that's statistically significant if it's equal to zero, that whole term can cancel out. In which case we can reject our null hypothesis, which I suppose we should have written initially but are no hypothesis would have been that beta one is equal to zero. And you can see here is that our critical value of 2.131 is greater than our test statistic of negative 1.129. So if we look at our t distribution table for degrees of freedom of 15 and our significance level of 5% using to tail here, we see that we have a critical value go to 2.131. So we have 16 -1 gives us degrees of freedom equal to 15. And then we need to know our degrees of freedom and our degrees of freedom are found by taking our sample size So which is N -1. So let's go ahead and test it at a 5% significance level. We need to know whatever significance level we're going to test it at. So this is our test statistic right here And now we need to go to our T distribution table to find our critical value and to find our critical value. That gives us a value which is equal to negative 1.129. So what we can see is that we have negative 2.2 as our estimate for treadmill Dividing that bites standard air of 1.947. So let's start by finding our t statistic and we can do that by taking our estimate is going to be equal to whatever estimate we have, which then gets divided by the standard air. So we're really doing is testing this beta one coefficient or this negative 2.2 in the actual regression.
Um so looking at this, what we really want to do is determine whether or not there is this statistically significant relationship that exists between skiing and treadmill here.
How to find p value on minitab express plus#
Or in other terms this can be written as why hat is equal to beta zero minus Beta one x 1 Or plus in this case. And using the information in the table, we can create this uh regression function where ski is equal to 90 minus 2.2 times treadmill. This example is taking us through hypothesis testing and using this regression output, where we're told that there was a sample size of 16.
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