Okay, so, linear regression. So, regression analysis is some form of predictive modeling techniques to identify the relationships between the dependent and independent variables that is used to find a causal effect relationship between variables. So to let's say use linear regression. So we will copy the data. So this is the data okay. So I will remove something Okay, I will remove it and then I'll do something like this.
Okay. Press Enter. Says See, Ryan this arrow here. So these error bars we refer to this error or some error in the disco. So okay here cause an error Okay, so I will remove this Okay, so nigh the data. So to let's say a builder create a linear model we can do something like this linear model, then data y to data x and then data equal to data.
So I can do something like this our model, two linear model, data, y two data, x their data to be equal to data. So let's chat a formula. Okay model to data y to data s data equal to the model y to s data to data model okay. So we have these intercept. So this is the y intercept and this is x Ah, so we linear regression we have y equals mx plus c. So, these are m should be minus 0.1049 then they see some 1.8993 So, let's see the result. Okay, so we can also call the summary summary of the model Okay, so, we can into the results.
So the APU the psi the linear equation is minus 0.104 is plus 1.993. So P value is 1.01 e to power minus 0.625 and 0.6246 tell us a significance of the linear model when the p value is less than 0.05. The model is significant. So, the r is zero based on now hypotheses, so the null hypothesis is our coefficient associated with a variable is equal to zero. The alternate hypothesis is the coefficient is not equal to zero the sample relationship. So, the intercept has a p value as one pi or one eight or power minus eight, which is smaller than 0.05 days SR Qurna Y variable.
The significant is indicator we've done done, Boss Star or asterisk the SS p value. So, policies to five is more than 0.0 perhaps there is no significant with the y variable. So, now have what they says is true to 95% confidence. So, the p value is 1.01 e to the power minus two okay. So, we go to some p value so, the intercept p value we about 1.01 e to the power minus eight okay. So, this is smaller than 0.05 hindsight is a significant with a Y variable okay.
So, these are intercept has a significance with a Y variable then for data so the p value is 0.625 The P value is 0.625 which is more than 0.0 alpha is no significant with a Y variable. So, there are now hypose diseases are true 95% confidence interval so called SR five which is more than 0.05 So, there is no significance between these are they over here okay this m and y is no significant relationship between these variables here and y data y is our response variable okay so, this is linear regression in our