00:01
Hello students, let's do this question.
00:03
Here, this question is solved by using the excel and in excel the data analysis tool pack is used to solve this question.
00:13
Here two variables are given and we have to build a regression model for these two variables and y is our dependent variable and x is our independent variable.
00:23
And the y variable is price, price is our y variable and the miles is.
00:32
Our x variable so here we have to predict the regression equation and we know that the regression equation y is equal to a plus b into x where a is the intercept and b is the slope so here for building the regression model we know the we have to find the values of a and b that is values of intercept and slope parameter.
01:01
So by using excel, first we go to data analysis toolpack, then choose regression, then choose independent variable, dependent variable, and press ok, then we get the result such as, so here we get this table after building the regression model, that is we get the multiple r, then r squared, adjusted r squared, standard error, observations, regression, residual anova table for regression model and intercept and slope parameter so our intercept parameter a becomes here 16 .4697 16 .4697 this is our intercept parameter and b is our slope parameter is nothing but minus 0 .0587 this is our slope parameter and the value of r square and r square is nothing but the coefficient of determination which equals to 0 .5386.
02:06
So if we conclude r square, then it means that 53 .86 % of the values of our model feeds the regression equation.
02:18
Therefore, we have to find also for a given miles, we have to find the predicted price.
02:28
So here given miles, that is the value of x is equals to 6 ,000 miles which equals to 60...