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So we Dandi logistic model with one predictive variable.

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And we got this result.

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We have the B does it all and B, the one values here.

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B, does it with zero point six one.

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And B, the one is minus zero point zero three.

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Once we get the values or B, does it want to be done, we can calculate the value of probability of

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Y is equal to one using the formula.

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I showed you in the last lecture.

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Now, when we ran the model in the output, we also get these other values, which is standard error,

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the value and the probability value, which is also called p value.

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I will talk about this briefly here.

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All these three values are calculated to establish whether this particular variable, that is the price

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variable is actually impacting the very well or not.

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Take a moment here and think about it.

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This variable will impact the response variable.

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Only if the beta corresponding to it is non-zero.

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If beta one is zero, whatever change we may make to this variable.

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Nothing will happen to the response variable.

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So we want to establish that beta one is non-zero.

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Mathematically began guided in this hypothesis form.

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That null hypothesis is there is no relationship between price and sored variable.

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The ordinate is that there is some relationship.

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We want to negate that.

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We do.

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One is zero so that we can establish that price is impacting the response variable.

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You may be thinking that we already got a non-zero value for this be done.

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So why are we discussing this?

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The point is that although we got an expected value of beta using the maximum likelihood method, how

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confident are we with this media value?

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There is still a chance that it was actually zero, but when we calculated using the maximum likelihood

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method.

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There is some probability that we are getting this value instead of zero.

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So these three values is helping us get that confidence level with which we can say that we do.

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One is non-zero.

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So the first column is standard error.

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Standard error is like standard deviation and represents the expected deviation of the actual return

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from these predicted ones to the predicted values zero point zero three and its deviation from the true

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value is nearly zero point zero one, corresponding to the standard error because it does devalue.

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Zewail You can be noted simply by dividing this estimated beta value by the standard error.

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So zero point zero three zero zero zero point zero one is nearly this two point forward value corresponding

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to this devalue, which is also called a Zis statistic using a Z statistic graph corresponding to the

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Z value.

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We can get a P value.

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P value is telling us the probability of BITA actually being zero when we are getting the predicted

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beta value as minus zero point zero three.

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And when the standard edit is zero point zero one, you may be Orvil in these last two lines.

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Don't worry.

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Here's the key takeaway from these values.

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Whenever we get all these values, we just need to check this P-value.

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This P-value should be lower than a threshold value.

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You can select this threshold as body or convenience.

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That threshold will give you the confidence level with which you can say that bidets actually impacting

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the response.

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Woodinville or not.

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Usually we have a threshold value of five percent or one percent.

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So as you can see that here, the P value is zero point zero zero six.

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This basically means that there is only a zero point zero six percent chance that the actual beta was

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zero and we predicted it to be zero point zero three.

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Therefore, just look at the P value of all the variables, whichever variable has a P value lower than

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a threshold value.

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You can state that all those variables are impacting the response variable.

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But others, you may not be sure.