# INFERENTIAL STATISTICS Flashcards

inferential stats?

reach conclusions that extend beyond immediate data sets

Bernoulli distribution?

important case of discrete variables–>Binary only 2 possible outcomes (0 or 1)

population parameter?

fixed feature of a particular population e.g. pop mean, pop variance

sample stats?

quantity that vary from one sample to another (obtain population parameter using random sampling as surveying entire population not practical)

Law of large numbers?

as sample size n increases, the sample mean gets closer to population mean

Central limit theorem?

when sample size large (n>=30),sampling distribution of x is approximately normal, regardless of distribution we started out with

hypothesis testing

tells us how extreme our sample outcome is. creates a rejection region, beyond which sample too extreme to maintain that null hypothesis is true

standardisation

Z=(x-mean)/SD Z~N(0,1)

test stat Z

(p observed-p)/sample variance (reject if >1.96)

reject Ho?

p-value<0.05

95% Confidence Interval

(pop mean-1.96*SD, pop mean+1.96*SD) reject if observed P not in range

import data from file?

Auto=read.csv(‘link’,header=TRUE,na.strings=’?’)

class of Auto?

‘data.frame’

structure of data?

str(Auto)

headers of data?

head(Auto)