Pearson's chi square test (goodness of fit) Pearson's Chi Square Test (Goodness of Fit)
Pearson's chi square test (goodness of fit)
- I'm thinking about buying a restaurant.
- So I go and ask the current owner, what is the
- distribution of the number of costumers you get each day.
- He said, oh I've already figured that out. And he gives me this distribution over here.
- It essentially says, %10 of his costumers come on Monday;
- %10 on Tuesday; %15 on Wednesday, so forth and so on.
- They're closed on Sundays.
- So this is 100% of their costumers for a week. If you add it all up you will get 100%.
- I obviously am a little suspicious. So I decide to see
- how good this distribution that he is describing actually fits observed data.
- So I actually observe the number of costumers when they come in during the week,
- and this is what I get for observed data.
- So to figure out whether I want accept or reject his hypothesis right here.
- I'm gonna do a little of a hypothesis test.
- So I'll make the null hypothesis that the owner's distribution is correct.
- And then the alternative hypothesis is going to be, that it's not correct.
- That it's not a correct distribution, that I should not feel reasonably okay relying on this.
- I should reject the owner's distribution.
- I want to do this with a significance level of 5%.
- Or another way of thinking about it, I'm going to calculate a statistic based on the data right here.
- It's going to be a chi-square statistic.
- Or the statistic I'm going to calculate has approximately a chi-square distribution.
- And given it has a chi-square distribution with a certain number of degrees of freedom--
- I'm gonna calculate that--
- what I want to see is the probability of getting a result like this or more extreme < 5%.
- If the probability of getting a result like this or something less likely than this
- is less than 5%, then I'm going to reject the null hypothesis,
- which is essentially rejecting the owner's distribution.
- If I don't get that, if I say, hey, the probability of getting a chi-square statistic
- then I'm not gonna reject it. I have no reason to really assume he's lying. Let's do that.
- So to calculate the chi-square statistic,
- here we're assuming the owner's distribution is correct.
- So assuming the owner's distribution was correct
- what would have been the expected observed?
- So we have the expected percentage here, but what would have been the expected observed?
- Let me write it here, expected.
- So we would have expected 10% of the total customers of that week to come on Monday;
- 10% of the total customers of that week to come on Tuesday;
- 15% to come on Wednesday... To figure
- out what that that actual number is, we need to figure out the total number of customers.
- So let's add these numbers right here.
- We have-- calculator out--
- so we have 30+14+34+45+57+20.
- So there's a total of 200 customers who came into the restaurant that week.
- So let me write this down.
- So this is equal to-- so I'll write the total over here. Total.
- Ignore this right here. I had 200 customers coming for the week.
- What is the expected number on Monday?
- Well, on Monday, we would've expected 10% of the 200 to come in.
- 20 customers, 10% times 200.
- On Tuesday, another 10%, so we would've expected 20 customers.
- Wednesday 15% of 200, that's 30 customers.
- On Thursday, 20% of 200 customers, so that would've been 40 customers.
- Then on Friday, 30%, that would've been 60 customers.
- And then on Saturday, 15% of 200, it would've been 30 customers.
- So if this distribution is correct, this is the actual number I would have expected.
- Now to calculate our chi-square statistic,
- let me just show it to you. And I'll write it instead of chi, I'm writing a capital X2.
- Sometimes someone will write the actual Greek letter chi here.
- But I'll write the X2 here to show-- let me write it this way,
- this is our chi-square statistic.
- But I'm going to write it with a X instead of a chi, because this is going to be
- approximately a chi-square distribution.
- I can't assume that it's exactly. So here we're dealing approximation right here.
- But it's fairly straight-forward to calculate.
- We take for each of the days, we take the difference between the observed and the expected.
- So it's going to be 30-20.
- I'll do the first one color coded.
- Divided by the expected.
- So we're essentially taking the square of
- almost kind of the error between what we observed and expected.
- Or the difference between what we observed and expected.
- We're kind of normalizing it by the expected right over here.
- We want to take the sum of all of these. I'll do all of those in yellow.
- So + (14-20)2/20 + (34-30)2/30 + (45-40)2/40 + (57-60)2/60 + (20-30)2/30.
- I just took the observed minus expected squared over the expected and took the sum of it.
- And this is what gives us chi-square statistic.
- Now let's just calculate what this number is going to be.
- So this is going to be equal to what?
- 30-20 is 10 squared which is 100 divided by 20, which is 5.
- I might not be able to do all of them in my head like this.
- Plus-- actually, let me I write it this way, so you see what I'm doing.
- This is going to be 100/20,
- + 14-20 is -6 squared is positive 36. So plus 36/20.
- + 34-30 is 4, squared is 16, so +16/30.
- + 45-40 is 5, squared is 25, so +25/40.
- + the different here is three squared is 9, so it's 9/60.
- + we have a difference of 10, squared is 100, over 30, +100/30.
- And this is equal to-- I'll get the calculator out for this. This is equal to--
- we have 100/20+36/20+16/30+25/40+9/60+100/30.
- It gives us 11.44.
- Let me write that down. This right here is going to be 11.44.
- This is my chi-square statistic, or you can call it X2. Sometimes
- you'll have it written as chi-squared, but this is approximately--
- this statistic is going to have approximately chi-square distribution
- Anyway with that said, let's figure out, if we assume that has a roughly chi-square distribution,
- what is the probability of getting a result at least this extreme?
- Or another way to say it,
- is this a more extreme result than the critical chi-square value
- that there's a 5% chance of getting result that extreme?
- So let's do it that way, let's figure our the critical chi-square value,
- and if this is more extreme than that, then we will reject our null hypothesis.
- So let's figure out our critical chi-square value.
- So we have an alpha, 5 percent.
- Actually, another thing to figure out is the degrees of freedom.
- The degrees of freedom here, we're taking 1, 2, 3, 4, 5, 6 sums.
- So you might be tempted to say that the degrees of freedom are six.
- But one thing to realize is that if you had all of this information over here,
- you could actually figure out this last piece of information.
- So actually have 5 degrees of freedom.
- When you have n data points like this, and you're measuring the observed vs expected,
- your degrees of freedom are going to be n-1,
- because you can figure out that nth data point,
- just based on everything else that you have, all of the other information.
- So our degrees of freedom here are going to be 5, n-1.
- Our significant level is 5% and our degrees of freedom is also going to be 5.
- So let's look at our chi-square distribution.
- We have a degree of freedom of five; we have a significance level of 5%.
- And so the critical chi-square value's 11.07. Let's go to this chart.
- We have a chi-square distribution with a degree of freedom of 5.
- So that's this distribution over here in magenta.
- And we care about a critical value of 11.07.
- So this is right here. We can't even see it on this.
- If I were to keep drawing this magenta thing, all the way over here,
- you'd have 8,
- over here, you'd have 10, over here, you'd have 12.
- 11.07 may be someplace right over there.
- So what it's saying is that the probability of getting a result at least as extreme as 11.07 is 5%.
- Our results, so our critical chi-square value is equal to-- we just saw-- 11.07.
- Let me look at the chart again. 11.07.
- The result we got for our statistic is even less likely than that.
- The probability is less than our significance level.
- So then we are going to reject.
- So the probability of getting-- 11.44 is more extreme than our critical chi-square level.
- So it is very unlikely that this distribution is true.
- So we will reject what he's telling us; we'll reject this distribution.
- It's not a good fit based on the significance level.
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