What's up, stat? Cats. My name is Aaron, and in this video, we're gonna be looking at an example performing an up test and using the traditional method of hypothesis testing to do so. So we're looking at Is there a difference in the variation of carb content between chocolate and non chocolate Candies? And we're gonna be using the output level 0.1 And our sample size for a chocolate is 13 and our sample size for non chocolate is 11. So the first thing we're gonna do is we're going to stay. Our hypotheses and identify are clean. So are no hypotheses is that the variation between the two types of candy is gonna be equal. So chocolate and non chocolate and our alternative hypothesis is that the variances of the two Candies are not gonna be so because they're not equal. This tells us that we're gonna do a two tailed test and our new out the level it's going to be 0.1 divided by two. That's going to give us 05 as our new off the level and our alternative hypothesis is our clean. We want to know if the carb content in the Candies differ. So that stuff a Now we're gonna go to step be, which is finding are critical value, which we use the H table to dio. So the sample size for chocolate is 13 and the sample size for a non chocolate is 11 But we don't know which we're going to use for a numerator and which we're going to use for a denominator until we figure out the variances and because they gave us raw data, we can use an Excel spreadsheet to go ahead and calculate all that for us. So here's the Excel spread she. So the first thing we're gonna do is we're gonna calculate the standard deviation for each category and then to get the variance. All we do is we take that number and we square. So the variance for our chocolate is 42.23. And for non chocolate, it's 1 25.45 So are non chocolate, because this very intense larger. That's what we're gonna use for our numerator and because of degrees of freedom is our sample size minus one. It's gonna be 11 minus 2010. T minus one equals well So this is the degrees of freedom for our numerator. And this is the degrees of freedom for our denominator. And as we saw before, the variants for our chocolate was 42.23 on our variants for our non chocolate was 1 25.45 So, yes, this one is both in our numerator. So let's pull up the age table for our 0.5 off the level and again, we're gonna be using 10 degrees of freedom for numerator and 12 degrees of freedom for our denominator. Just a slider cursor till we get to tense are critical values to 0.75. All right, so that's step be and Step C is computing our test value. And to do that, we're going to be using our variances from our samples. So the variance of our chocolate waas 42.23 4 hour non chocolate it waas 1 25.45 So what we're gonna do is we're going to put our larger variance over top are smaller variance, and I'm just gonna go ahead and pull up that Excel spreadsheet again so we can go ahead and calculate it. So larger variants divided by the smaller variance. 2.97 So that is our answer or our value. So that's our test value. Step D now is making a decision based on our test statistic and our critical about so are how was 2.97 and are critical value. Waas 2.75 So what we want to ask ourselves is, Is our cow greater than are critical? And if so, that's when we can reject are no hypothesis. So is 2.97 greater than 2.75? Yes, it ISS. So we are going to reject Arnold hypothesis. So that stuff D and step ease our last stuff and it's summarizing our results. So because we rejected Arnold hypothesis, there is enough evidence to support that the variance and our content between chocolate and on chocolate can. All right, so in this video we looked at an example, performed an up test using need judicial, traditional method of hypothesis testing. And we did reject Arnold hypothesis. Um, I hope you guys enjoyed this video. Learned a lot and I can't wait to see you next time