Okay then for linear regression, so regression analyses is some form of predictive modeling techniques that identify the relationship between the dependent and independent variables. The tiny is used to find a causal effect relationship between two variables. So, for Python, we can do something that is, so y equals sepal and petal and then we can do something, SM da, constant. SM, da si, y si dollfie. So, we can see, one of them is our dependent variable and one of them is our independent variable. So, for this you can check on lifetime you'll see y is the variable that we want to predict and then the S is the variable that we used to predict.

Okay, so we can For this query in Python, so we can do something like this. So y is our sepal length okay our y is equal to our dx upper line okay then our x is equal to df factor land Okay, that is equal SM constant x k x equal s m.com constant x constant as more di equal SM da s ma de equals SM da well s ah should be y s and dot p y x xR dot fee then we came pre Armada so he not long we are using summary order summary I will have error in this code because si is no importer. So, SMEs I imposed this model as sn stepmothers dot API SSM as SM Okay, then you can run the code we don't have a concern. So, SM I see we can do something it SM da kV don't have a constant state models dot API key dot API and a constant da constant and we can run the call Okay, that'd be a badass summary parties are Ah, moda here Okay, interpreter resilence something like this.

See? Rita okay. So the output the piece that a linear equation is sepal length equal 0.4091 times the petal length grasshopper. 4.3 or Pisces. So how do we get a value 4.30 Pisces sister constant then the petal length Is 0.4 or nine 1.4191 p value is 00 tell us the significance of a linear model when P value is less than 0.05 the model is significant okay then we have our when we see a zero here is our now hypothesis so we can look into the null hypothesis it has a coefficient associated with a variable is equal to zero, then our auto hypothesis is that coefficient is not equal to zero that is our relationship. So in the intercept has a p value of zero which is smaller than 0.05 hands Day is a significant separator, then they will buy in a constant our constant has 95% confidence interval for the constant value to be four pi one by one in 4.461.

So what you Misha is that a constant value I is around 4.51 by two 4.461 we are based on the status here based on 95% confidence interval. So, what it means is that the constant value has around 95% correct data value is between 4.151 to 4.461. So, the values or what we call coefficient is 4.3 or five six okay then Pelin has a p value of zero which is less than 0.05 and then confidence interval for petal n is 0.37 to 0.4 policy. So what it means is that a title land has a value of 3.5 0.372. So my thought process that is around 96 95% confidence