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So you would have noticed that there is one value of this outpoured that we are still not discussed.

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And that value is called F statistic.

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And in this video, we'll be discussing about F statistic.

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Now, if you see in the desert for each of the coefficient.

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There is a corresponding P-value, which is telling us whether each individual predictor is related

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to the response I'm talking about.

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These P values and probability values.

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These two columns.

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So since at least one of them has a small P value here, we have six of them, too.

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Since at least one of them has a small P value corresponding to their devalue.

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We may think that shortly at least one of the predictors are related to the response.

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Therefore, we will say that there is a relationship between this bond and predictor variables.

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That is not all of the coefficients of the variables that has been one.

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We do appear to be.

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Ardito.

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So all of them are not deedle.

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This argument may sound OK, but it is actually not, especially if we have a large number of variables.

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Let me put it mathematically for you first.

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What I'm saying is the null hypothesis is that all of them are Z2.

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I want to prove that this is not the case.

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I want to say that at least one of DBI does is definitely non-zero.

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That is the predictor that I have selected have some relationship with the response.

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Now, suppose if my model is saying that any one of the predictors say, hey, we got six.

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Suppose it was giving that only one of them to a room them is significantly littered with host praise.

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This is the P value and the rest are not.

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Now, can I say with confidence that since bit of room them is significantly lifted, I am confident

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that the predictors in this model are impacting the response variable.

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The answer is no.

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I cannot see that.

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Let me show you how.

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So suppose I toss a coin five times and I say that that I figured five heads in a row.

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I will thumb that going as a biased coin.

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Now, if this is my criteria of saying that the coin is biased or does the probability that I will classify

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a fair coin as a biased coin?

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As you can see, probably of head in one toss of a coin is half.

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So what is the probability that in all the five doses, I'll get all the heads?

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So half into half into half and half and half five times.

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There it is, harvestable fail become, which comes out to me.

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Point zero three one two, fail.

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This means that there is nearly three percent chance that I will wrongly call classify a fair coin as

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a biased coin.

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Now, if I was hundred feared grains five times.

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What is the probability of at least one of them getting all five head?

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So these are hundred fed grains.

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The probability of getting ahead is point five again.

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What is the probability that at least one of them is wrongly classified as a biased going?

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So this weekend, calculate using this formula that I've shown here, it is first finding out the probability

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of getting all heads in none of these.

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And we are finding one minus that probability.

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And that is coming out to be 95 percent.

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You do not need to know how this came out, but the result is important.

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The point that I'm trying to make is, although the probably of calling a fair coin as a bad coin was

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only three percent.

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But if you're doing this experiment a hundred times, it is almost certain that you will wrongly classify

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at least one of the coins.

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By almost Artane, I mean, there is more than 95 percent probability.

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You know, this is similar to the T statistic in the sense I said that we are 95 percent sure that we

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today is not zero.

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When I saw it, P-value and P-value.

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So we had nearly five percent unsure.

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So there is a five percent chance of a BITA, which is zero being classified as a nondurable bita.

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Not if we have a large number of variables.

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All having five percent chance of getting wrongly guessed as significant.

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It is almost certain that at least one of them is actually wrongly saying that of for that variable

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is non-zero.

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So if we depend on individual P values, there is a very high chance that we will incorrectly conclude

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that there is a relationship between predictive variables and they respond to real.

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The solution to this problem is to adjust for the number of predictors and then find the value.

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This new statistic, which I just for the number of predictors, is called F statistic.

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This includes the number of variables in the form of this be.

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So Alz of when we run a multiple regression model, give a F statistic value for the model and the corresponding

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P value.

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So this P-value must be checked against the threshold that whether there is a relationship between model

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predictors and the response.

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If there is, then we will look at the P-value and P-value of each individual variable to identify which

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of the variables is significant.

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Delegate.

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I hope you understand the concept.

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I'll briefly summarize it.

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The idea is, instead of looking at individual variables.

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And saying that the model predictors are significantly impacting the response variable.

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We will look at a different statistic which takes into account the number of variables that we have.

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This ensures that we do not make the mistake of saying that these models have a significant relationship

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with predicted, if we got that, we're not too deeply into variables versus individually impacting

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the response.

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By chance.

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So to avoid that jaunt, we are using this new statistic, which is called a statistic.

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We look at its value and we look at the corresponding P value.

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If this P value is lower than the threshold value, say, one percent or five percent, again, we say

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that the model predictors are significantly impacting the response.

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After that, we will look at the individual predictors and their estimates and P values, although not

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like I have told you a little.

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Also, even if you do not understand the whole concept behind this statistic, the concept that you

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should take away from this, we do listen is just that you have to check whether your P value of F statistic

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is lower than attracts your low five percent.

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You do not need to understand all of the concept behind me after Ristic.

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This will ensure that the model predictors that you used are having some significant impact on the response

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that you would.