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by: Lily Cogley

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# Lecture Notes Sections 6.2-6.3 STAT 250

Marketplace > George Mason University > Statistics > STAT 250 > Lecture Notes Sections 6 2 6 3
Lily Cogley
Mason
Introductory Statistics 1
Russell

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Class notes that go beyond the powerpoints. Good especially for people who missed class. Includes points emphasized in class and examples, along with the formulas used.
COURSE
Introductory Statistics 1
PROF.
Russell
TYPE
Class Notes
PAGES
2
WORDS
KARMA
25 ?

## 1

1 review
"These were really helpful...I'll be checking back regularly for these"
Antwan Mante

## Popular in Statistics

This 2 page Class Notes was uploaded by Lily Cogley on Tuesday October 6, 2015. The Class Notes belongs to STAT 250 at George Mason University taught by Russell in Summer 2015. Since its upload, it has received 13 views. For similar materials see Introductory Statistics 1 in Statistics at George Mason University.

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## Reviews for Lecture Notes Sections 6.2-6.3

These were really helpful...I'll be checking back regularly for these

-Antwan Mante

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Date Created: 10/06/15
Lecture Notes Ch 6263 10072015 62 The Normal Model Normal distributions are a family of symmetrical bell shaped density curves de ned by a mean m and a standard deviation 5 denoted Nm s The in ection points are m i s 1 1 2 f x e 2 quot x27r The normal distribution is continuous bellshaped and symmetrical Properties 0 As x get smaller it extends to negative in nity as x gets larger it extends to positive in nity 0 Total area under the curve is equal to 1 Normal Curve Mean0 Ellilllll Standard Devnation1 Examples Refer to table on blackboard under statistical tablesquot section Probability of 2 being greater than 1 Pzgt11P2lt1 11587 Probability of 2 being less that 125 P2lt12508944 Probability of 2 being greater than 209 Pzgt2091Pzlt209 1Pzlt209109817 00183 Standardize with zscore to get the standard deviation x 039 63 Binomial Distribution Properties 0 There are a xed number of trials n 0 Each observation fall into one of just two categories called success and failure 0 The probability of a success is the same for each trial and is labeled p o The n trials are all independent Z The random variable representing the count Xof successes in the binomial setting has the binomial distribution with parameters n and p Ntota number of observations Pprobability of success Xany whole number between 0 and n BnpX012n EX B505XO12345 2quot532 Factorial Notation n kkKn k Binomial Probability Function The function calculates the probabilities of exactly k successes in n trials PXk n k n k PX kmp 1 17 nnumber of trials knumber of successes pprobability of success

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