00:01
All right, so we're looking at a model that was made, and it was going to be the following.
00:07
Y hat, we've got the beta naught hat, plus beta 1 hat, x1, plus beta 2 hat, x2, plus beta 3 hat, x3, plus beta 4 hat, x4.
00:20
And what we're looking at is the price of lego sets y, and the explanatory variables are, we've got types and pieces.
00:32
So beta one or beta two through beta four, these are types of lego sets.
00:40
Beta one is pieces and then beta not, this is your two set term.
00:49
And there's quite a bit of data to look at.
00:53
There's a lot here.
00:54
So there's a summary, summary of fit.
01:00
There is the anova, also called the analysis of variance.
01:15
Then we have effect tests.
01:21
And we have indicator function parameterization.
01:38
Right, and given this information, there are two things we need to answer.
01:43
First one is a sentence, well, both of them are filled in blank, but the first one, it's a blank percent of the variability in the price of lego sets as explained by the model with explanatory variables, number of pieces and type of lego sets.
01:57
So for that, we're gonna look in this summary of fit, and this is it's called r squared and r squared and it's 0 .94 and so this whole one and this is what we're looking at this is the number but we interpret as a percent so ninety four point five if you're into the tenth place percent of the variability in y which is the price of lego sets is explained by this model where the expiring variables are pieces and types.
02:40
And so that's what it is, so it's r squared.
02:44
The second question is asking us about confidence interval.
02:53
Based on this example, we expect 95 % of the actual original prices of these lego sets of y to be within blank of the predicted actual original price.
03:10
So what's the basically your margin of error from the actual value and to do that we're gonna look also in the summary of fit it's called root mean square error root mean square error our root mean square error and the value is eight point six two five five, four, five, one.
03:43
And so this is like your standard deviation.
03:45
And so this isn't necessarily 95%...