00:01
A, we should say what is the exploratory variable? so the exploratory variable usually is the one that we have in the x -axis or the one that we use to predict the other variable.
00:14
So basically, from the question, we have that it is written that we, in this case, they build a model that predicts like fat content using protein content.
00:30
So you, like i said, the explanatory variable is the one that is used to predict the other variable.
00:38
So the variable that is being used to predict the fat content is the protein content.
00:43
So the protein content is the explanatory variable.
00:48
So in this case, we already have that the variable that we are predicting, which is the response variable, in this case is the fat content.
00:57
So now in item c, we should say if the scatter plot that we have, it seems linear, and if you have outliers.
01:09
So basically what we need to check if it is, if you imagine a line, straight line in here, like we have the dots, a lot of dots, then we have like two dots here.
01:22
So if you can imagine that like there is like a linear relationship that could be like increasing, like, increasing.
01:27
This direction or decreasing in this direction.
01:31
So basically we want to check if you can see one of these two lines.
01:36
And from this kind of plot we can basically see like it appears that we have like something like this.
01:46
Of course there are some dots here that are like could be above this line, but like in general it seems linear.
01:54
So the answer for this question be yes, it seems linear.
01:57
In terms of of outliers we can see that these two dots they are like really not really far but they are like a little bit far from the others so like we can say that we have like two possible outliers i don't know if they are influential because as you can see they are still like in the they follow the same behavior like they are in the same direction as the straight line here so basically what i mean by that they could be outliers, but maybe not influential.
02:32
So now for item d, we should state the pearson correlation coefficient and also make interpretation.
02:39
So the coefficient here is the one that is given by r in the output, which is 0 .82 to 82 .7.
02:48
So what we should look here is that this value is positive and it is like quite high because this value could be like between minus 1 and 1.
03:00
If it is close to either minus 1 and 1, or 1 in this case, this means that the relationship, the linear relationship between the variables is strong.
03:12
So in our case, what we can say is that the relationship here between these two variables here is positive because like we have a positive coefficient.
03:23
And because it is close to one, we can say strong as well.
03:30
Now for item e, we should in this case write the equation.
03:36
So basically, the equation is given by the parameter estimates.
03:40
So we should plug here.
03:42
First, we can put that this is the fat content that we are like predicting.
03:47
And usually put hat to express that this is like a model.
03:51
It's not like a, it's something that we are.
03:53
Are estimating is not something that is totally true...