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AaunIcey Slon tcicoJverge person /5thenul hypothesis The dala I V Mque reported 510K5Entet Yoilt HNGAencn alino unsNelboxes...

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AaunIcey Slon tcicoJverge person /5thenul hypothesis The dala I V Mque reported 510K5Entet Yoilt HNGAencn alino unsNelboxes

Aaun Icey Slon tcico Jverge person /5 thenul hypothesis The dala I V Mque reported 510K5 Entet Yoilt HNGA encn alino unsNelboxes



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You are performing a hypothesis testof a single population proportion. You find out that $n p$ is less than five. What must you do to be able to perform a valid hypothesis test?

The variable is tv viewing time. The two populations are married men and married women. The testing population or identifying the pears are married couples were trying to find the difference between the tv viewing times of a married couple. If we let U. One and U. Two. To note that mean, tv viewing times of married men and women, respectively. This means that for the null hypothesis, me one is equal to meWTwo. And if we let men the new one, we hypothesize that men watch less tv. So it's me one is less than you two, which means that we are doing a left tail test.

This question lists a couple of experiments and asks us What is the no hypothesis and what is the ultimate about this? The 1st 1 says, What if it's we have an equal probability of choosing one through five? Well, are no hypothesis just assumes that what we're given is is true. We're saying we have no reason to disbelieve this. So, uh, let's just assume what we have is true. So what it's gonna be is that the probability of choosing any given number one through five is equal, So P of one is equal to p of two, which is equal to p of three and so on. This is how we would write it. And since they're all equal, they will have to add upto one. They're all gonna be 1/5. That's are no hypothesis. We right away that h not now. What's your alternate hypothesis when it comes to multi? No meal experiments like this when we're doing ah chi squared stuff. The alternate hypothesis is a little bit more tricky. We can't just say, Oh well, P is going to be greater than 1/5 because we're working with five peas and they all have to add upto one. So with when we have four degrees of freedom like this, it's hard to just say to condense are often about things down into one sentence. Normally, what we do to get around this as statisticians is we just say, Well, do you? Our alternate hypothesis is that the know about this just isn't true. Will write. Our alternate about this is that the probabilities, the prop's, are not equal. And then, after we, if we do end up rejecting, are null hypothesis. We'll take a look closer at our ultimate hypothesis and look at our data to see which probabilities air higher in which are lower. But right now, before we have her data will just say that the problems are not all equal. The next one talks about, um, multiple choice question. It's a the ratio of responses to a multiple choice question. We're given the expected ratio. Now the expected ratio is going to be are no hypothesis or null hypothesis. Is that the outcomes? This is, um, as if each question was a trial. Uh, so the outcomes of these trials will follow a pattern. Follow a to 3 to 1 that's are null hypothesis pattern. So since we're saying, is there no hypothesis? We don't have any reason to disbelieve what were, uh, what we're being told we'll just go ahead and check this, that the outcomes will follow a 23 to 1 pattern. Now, what is our alternate hypothesis is again, It's hard to know we have four categories that we're testing instead of just one. So it's not super easy to tell right away. What are alternate hypothesis is gonna be. And without the data, we don't really have a way saying this will be higher. This will be lower. Uh huh. So what do we do in this instance? With multi normal experience, we just say, Well, the outcomes won't follow that pattern. So I'm just gonna right a little bit shorthand here. The outcomes will not and then follow a 23 to 1 pattern. So that is our alternate hypothesis. And then once week, if we were to reject the null hypothesis, we look at the data and refiner ultimate hypothesis a little bit, and we go one like that. Keep testing more and more until we're certain of the result. Now, the next one talks about polls about TV ratings. Um, and we're given what we expect. We expect the Poles to look like 16% 30% 41% 5% going from best to worst. Um, And so that's what we expect that's going to be, are no hypothesis. So are not about this. Is that the polls? We'll show a pattern of 16%. 38% 41% 5% pattern. Oops. So what's your alternate hypothesis in this instance? Well, just like before, our alternate hypothesis is simply that, uh, it's not 16 38 41 5 We'll have an alternate hypothesis. That's just pretty much anything other than our know. So we'll say that the polls will not show a 16 30 41 5 pattern, and that is really it. That is how we make it. No, or an alternate about this, isn't it? Null hypothesis with a multi noble experiment are no, is what we would expect, and our alternate is simply anything that we wouldn't expect. And that's your final answer

A variable is fiber density. The first population is the eyepiece, whereas the second population is tv viewing. Um for C. R pairs are gonna be eyepieces. TVs D. The difference is going to be the difference between the two populations or in all hypothesis is that the averages for the two populations are going to be the same, And our alternative hypothesis is whether on average I piece gives a greater fiber density, the eyepiece is the first one, so new one is greater than you two. Therefore, we're doing a right tail test.

Welcome back to another video here we have in the left column all our values that are kevin. And then what I do is I just do an x minus The average wishes five and then we take the absolute value of what that was. Next thing we have to do is rank these numbers from smallest to largest. So that means we're gonna start ourselves strong with number one And then we go to two and two. But this would be two and three so we average them out to be 2.5. I remember now if this is 23 so now we're at four and five So that makes it 4.5, Mhm. So 45, next one is six and then seven and eight. And then over here we're just gonna copy down these values whichever ones were negative so that's gonna be negative and that's gonna be negative 2.5, 4.5. And then all we have to do is just add up all these values. So we're just gonna add up even the negative ones. So eight plus 2.5 plus seven plus six plus .5 Plus 1 -2.5 -4.5. And I get 22 and actually I misread that we're not supposed to add these ones up. So pretty much just add up these ones. And instead of 22, what we get is 29. So then we have to look in the back of the book here uh to find out what are critical value is. So we have a sample size of 12345678, sample size of eight and a significance level of 10%. Getting us a critical value a critical value of 28. Um and 29 is greater than 28. Therefore we can reject the null hypothesis. Um Which was that The average is five in favor of the alternative. The average is greater than five


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