Statistical Analysis-Examples

Bhawna G. Panwar

4 minute read

Statistical Analysis consists of code from the Chapter Lab Sections 4.6.1 and 4.6.2(Induction to Statitical book) library (ISLR)

Warning: package 'ISLR' was built under R version 3.3.3

names(Smarket )

[1] "Year" "Lag1" "Lag2" "Lag3" "Lag4" "Lag5" ## [7] "Volume" "Today" "Direction"

dim(Smarket )

[1] 1250 9

summary (Smarket )

Year Lag1 Lag2 ## Min. :2001 Min. :-4.922000 Min. :-4.922000 ## 1st Qu.:2002 1st Qu.:-0.639500 1st Qu.:-0.639500 ## Median :2003 Median : 0.

logistic Regression2

Bhawna G. Panwar

14 minute read

Logistic Regression was used to run the Credit dataset. credit <- read.csv("C:/Users/Bhawna/Documents/blog/data/credit.csv")

examine the launch data

str(credit)

'data.frame': 1000 obs. of 17 variables:

$ checking_balance : Factor w/ 4 levels "< 0 DM","> 200 DM",..: 1 3 4 1 1 4 4 3 4 3 …

$ months_loan_duration: int 6 48 12 42 24 36 24 36 12 30 …

$ credit_history : Factor w/ 5 levels "critical","good",.

Artificial Neural Networks

Bhawna G. Panwar

8 minute read

Artificial Neural Networks(ANN) Algorithm is used on Concrete dataset. Neural Networks are considered a black box process. ANNs are based on complex mathematical systems. But not a zero node NN is an alternative representation of the simple linear regression model. ANNs are versatile learners that can be applied to nearly any learning task: classification, numeric prediction, and even unsupervised pattern recognition. ANNs are best applied to problems where the input data and the output data are well-understood or at least fairly simple, yet the process that relates the input to the output is extremely complex.