00:01
Uses one or more explanatory variables to predict one response variable.
00:06
The simple part is that we will be using only one explanatory variable.
00:10
If there are two or more explanatory variable, the multiple linear regression is necessary.
00:17
So here, the simple linear regression equation can be written as y is equal to a plus bx, where x is the explanatory variation.
00:30
Variable, y is the response variable, a is the intercept and b is the slope.
00:39
So this is explanatory, explanatory variable, x is the response variable, a is nothing but intercept and b is nothing but slope.
00:53
So the model of the multiple regression equation will be y is equal to or y cap a plus b1 x1 plus b2 x2 plus b2 x2 so here it will be x1 and x2 will be the explanatory x1 and x2 will be the explanatory y will be the response a will be the intercept and b1 and b2 b1 and b2 are the slopes so this will be be the variations between simple regression and multiple regression.
01:34
So the coefficient of determination in terms of r square is known as coefficient of determination.
01:41
A square it is considered to be determination, which indicates the proportion of total variations explained by the regression line.
01:50
So the formula for intercept is nothing but.
01:53
Intercept is nothing but a is equal to summation y divided by.
01:59
N and b is equal to or minus b summation x divided by n and the formula for slope is nothing but b is equal to n summation x y divided by n summation x squared by n summation x squared by n summation x square minus summation x the whole square.
02:33
Now, the coefficient of determination is obtained by the formula, r square is equal to ssr divided by sst, that is summation y cap minus y bar, the whole square divided by summation y minus y bar the whole square, y minus y bar the whole square so here s s r is nothing but the sum of the squares and s st is nothing but total sum of the squares so the formula for t will be in terms of t is equal to b minus e of b divided by s e of b so now let us consider x and y to be the variables or denote the amount of television advertising and the gross revenue weekly gross revenue now we are moving in terms of the excel data so excel using excel state we are going to find out the linear regression concept so for that you have to the step one will be click the data in the task bar then data analysis and regression then give okay in the step two you have to input the data regression data range input the data range and then you will be pressing okay so the linear regression output will be in terms of we will be obtaining it in terms of the excel sheet and the solution will be y cap is equal to minus 45 0 .4323 plus 40 .064 x multiplied by x 40 .064 x.
04:44
So here we have calculated it in terms of the exercise.
04:49
Now comparing it with the null and alternative hypothesis, our h0 will be null hypothesis, is beta 1 is equal to 0 and our alternative hypothesis h alpha is equal to beta 1 not equal to 0...