1. Descriptive Stat and Correlation
Homeworks #1 and #2
Descriptive Stat and Correlation

2. What Determines Housing Price?
Industrial: the mean is slightly higher than the median; suggesting that distribution is slightly skewed. Odd that there are so many values that are 18.1

3. Correlation Matrix
Chart from Prior Slide

4. Correlation and Outliers

5. Correlation and Outliers
Analysis with all data =
Outliers – rationale story?
Sometimes don’t know
How to remove outliers
Without outliers = ???

6. Normal Distribution & Outliers

7. Housing Price: Outliers
Tukey Outlier
Keep or
Remove?
hist(bh$hprice, col = "green")
plot(density(bh$hprice), ylim = c(0,0.07),col ="blue")
curve(dnorm(x, mean=mean(bh$hprice), sd=sd(bh$hprice)), col="red", add=T)
More:

8. Normal Distribution
4.13
13.33
22.53
31.73
40.93
50.13

9. Hard to know what trim value to use ahead of time
trim
the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint. Default 0.1
Hard to know what trim value to use ahead of time

10. More Descriptive Statistics

11. Mean / Standard Deviation Coefficient of Variation

12. Crime

13. Removing Outliers: Subset Data
bh.out <- subset(bh, bh$hprice < 37 & bh$crime <37)

14. Correlation after Removal of Outliers

15. Correlation and Outliers
Analysis with all data =
Outliers – rationale story?
Sometimes don’t know
How to remove outliers
Without outliers =

16. Equation for a line: Y = mx +b
m = slope = change Y, change X
b = y-intercept (when X=0) ∆Y
29.75 – 16.79
12.96 = = ∆X
0 - 20
- 20 Y b X