So we Dandi logistic model with one predictive variable.
And we got this result.
We have the B does it all and B, the one values here.
B, does it with zero point six one.
And B, the one is minus zero point zero three.
Once we get the values or B, does it want to be done, we can calculate the value of probability of
Y is equal to one using the formula.
I showed you in the last lecture.
Now, when we ran the model in the output, we also get these other values, which is standard error,
the value and the probability value, which is also called p value.
I will talk about this briefly here.
All these three values are calculated to establish whether this particular variable, that is the price
variable is actually impacting the very well or not.
Take a moment here and think about it.
This variable will impact the response variable.
Only if the beta corresponding to it is non-zero.
If beta one is zero, whatever change we may make to this variable.
Nothing will happen to the response variable.
So we want to establish that beta one is non-zero.
Mathematically began guided in this hypothesis form.
That null hypothesis is there is no relationship between price and sored variable.
The ordinate is that there is some relationship.
We want to negate that.
We do.
One is zero so that we can establish that price is impacting the response variable.
You may be thinking that we already got a non-zero value for this be done.
So why are we discussing this?
The point is that although we got an expected value of beta using the maximum likelihood method, how
confident are we with this media value?
There is still a chance that it was actually zero, but when we calculated using the maximum likelihood
method.
There is some probability that we are getting this value instead of zero.
So these three values is helping us get that confidence level with which we can say that we do.
One is non-zero.
So the first column is standard error.
Standard error is like standard deviation and represents the expected deviation of the actual return
from these predicted ones to the predicted values zero point zero three and its deviation from the true
value is nearly zero point zero one, corresponding to the standard error because it does devalue.
Zewail You can be noted simply by dividing this estimated beta value by the standard error.
So zero point zero three zero zero zero point zero one is nearly this two point forward value corresponding
to this devalue, which is also called a Zis statistic using a Z statistic graph corresponding to the
Z value.
We can get a P value.
P value is telling us the probability of BITA actually being zero when we are getting the predicted
beta value as minus zero point zero three.
And when the standard edit is zero point zero one, you may be Orvil in these last two lines.
Don't worry.
Here's the key takeaway from these values.
Whenever we get all these values, we just need to check this P-value.
This P-value should be lower than a threshold value.
You can select this threshold as body or convenience.
That threshold will give you the confidence level with which you can say that bidets actually impacting
the response.
Woodinville or not.
Usually we have a threshold value of five percent or one percent.
So as you can see that here, the P value is zero point zero zero six.
This basically means that there is only a zero point zero six percent chance that the actual beta was
zero and we predicted it to be zero point zero three.
Therefore, just look at the P value of all the variables, whichever variable has a P value lower than
a threshold value.
You can state that all those variables are impacting the response variable.
But others, you may not be sure.