10/22/2020
Steven Sigler’s short treatise, The Seven Pillars of Statistical Wisdom, presents the seven major concepts driving most, if not all, of statistical theory and practice. Those pillars are
\(SE = \frac{\sigma}{\sqrt{n}}\)
Definition: a measure of the statistical accuracy of an estimate, equal to the standard deviation of the theoretical distribution of a large population of such estimates.
The Standard Error is calculated using (the standard deviation) and dividing it by the , where n represents the sample size.
This statistical idea incorporates both aggregation and information measurement. Aggregation is involved in obtaining the Standard Deviation( the numerator) in that we take a combination of observations to get the variation. Information Measurement is utilized by taking the square root of the number of observations (the denominator).
library(insuranceData)
data("AutoBi")
str(AutoBi)
## 'data.frame': 1340 obs. of 8 variables: ## $ CASENUM : int 5 13 66 71 96 97 120 136 152 155 ... ## $ ATTORNEY: int 1 2 2 1 2 1 1 1 2 2 ... ## $ CLMSEX : int 1 2 1 1 1 2 1 2 2 1 ... ## $ MARITAL : int NA 2 2 1 4 1 2 2 2 2 ... ## $ CLMINSUR: int 2 1 2 2 2 2 2 2 2 2 ... ## $ SEATBELT: int 1 1 1 2 1 1 1 1 1 1 ... ## $ CLMAGE : int 50 28 5 32 30 35 19 34 61 NA ... ## $ LOSS : num 34.94 10.892 0.33 11.037 0.138 ...
library(insuranceData)
data("AutoBi")
plot(AutoBi)
Using the Automobile Bodily Injury Claims data in R we analyze to see which variables are statistically significant. The finding of the analysis can be useful in building a model to charge appropriate premiums or plan for future losses.
Two pillars that are incorporated in running an analysis would be information measurement and regression. Information measurement is involved in that the amount of data an Insurance company has access to increases every day. Regression can be utilitzed to forecast.