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So that's our discussion on high leverage points and influential observations,
but I wanna think about whether if you go back to our data.
I think it's easier to discuss this, looking at our observations.
Here what we see on the top part,
there's a collection of five different observations.
So these are five different towns that have very high value
compared to what you see for all of the other towns.
So question is even though these points aren't high leverage points,
because they are in this typical x range, Are they influential observations?
Meaning if we remove these observations will the fit change very much.
So, not let's just see what happens in this data set.
Okay, so we're gonna remove these, what we're saying here we're gonna remove these
high value outlier neighborhoods and redo our analysis.
So what we're doing here is we're creating a data set,
which I'm gonna call sales underscore no high end for no high end towns.
Which takes our data set, still with center city removed, and
just filters out all the towns that have average values greater than $350,000.
Okay, so let's fit this new data set.
And again, let's compare coefficients.
So I'm gonna compare the coefficients to our fit with Center City removed
to the fit that further removes these high end houses,
or sorry, these high end towns.
And what you see is, yeah, there is some influence on The estimated coefficient.
But not nearly as significant as what we saw by simply removing center city.
So in this case, we've removed five observations
out of a total of 97 observations.
And we see that impact of crime rate on predicted decrease and
house value changes by a couple hundred dollars, but not by the amount that we saw
by just removing that one center city observation earlier on.
So this shows that high leverage points can be much more
likely to be influential observations for just small deviations from the data set.
Then outline observations that are within our x, our typical x range.
Okay, so the summary of all of this analysis and
discussion is the fact that when you have your data, and you're making some fit and
making predictions or interpreting the coefficients.
It's really, really important to do some data analysis to do
visualizations of your data or different checks for
whether you have these high leverage points or these outline observations and
checking whether they might potentially be these influential observations.
Because that can dramatically change how you're interpreting or
what you're predicting based on your estimated fit.
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