Sta 2321 Test 1

Front 1

Parameter

Back 1

A numerical value based on the population

Front 2

Statistic

Back 2

A numerical value based on the sample

Front 3

Outlier

Back 3

A data value that is an extreme value

Front 4

Resistance Measure

Back 4

a measure that is not affected by outliers

Front 5

The Median is a __________ measure

Back 5

Resistance

Front 6

Location formula for Percentiles

Back 6

The mth percentile, written Pm is loacted with the formula:

(m/100) * (n+1)

if not an integer continue with:

[location integer] + [decimal * distance between integer and next position]

Front 7

Outlier Detection Formula

Back 7

Q1 - 1.5 (IQR)
Q3 + 1.5 (IQR)

Front 8

Convexity Law

Back 8

The probability of any event must be between 0 and 1

0 < P(Ei )< 1

Front 9

The total sum of all probabilities for n mutually exclusive outcomes is __.

Back 9

1

P(E1) + P(E2) + . . . + P(En)

Front 10

For any two events A and B:

P(A or B)=

Back 10

P(A) + P(B) - P(A and B)

Front 11

The Multiplication Rule

Back 11

For any two events A and B:

P(A and B) = P(A|B) P(A) = P(A|B) P(B)

Front 12

The Multiplication for independent events

Back 12

P(A and B) = P(A) * P(B)

Front 13

3 Types of probability

Back 13

1) Marginal
2) Conditional
3) Joint

Front 14

Disease Diagnostics:

Back 14

1) Symptoms
2) Signs
3) Disgnostic Tests

Front 15

The accuracy of an examinatino is affected by two factors:

Back 15

1) Reproducability
2) Validity

Front 16

Experiment

Back 16

A process in which the outcome cannot be predicted ahead of time

Front 17

Sample Space

Back 17

S = A collection of all possible outcomes

Front 18

Event

Back 18

E = A subset of the sample space

Front 19

Equally Likely Events

Back 19

each outcome has the same chance of occuring

Front 20

Random Variable

Back 20

When the value of a variable is obtained as the result of a chance factor.

A variable is a random variable if the exact value of the variable can not be predicted in advance.

Front 21

Discrete Random Variable

Back 21

When the variable can only assume specific values

Front 22

Continuous Random Variable

Back 22

When the variable can theoretically assume any value on a given interval

Front 23

Probability Density Function
(pdf)

Back 23

Describes the distribution of a continuous random variable

Front 24

Probabiliy Mass Function (pmf)

Back 24

Describes the distribution of a discrete random variable

Front 25

Bernoulli Trial

Back 25

When a random process or experiment results in one of only two mutually exclusive outcomes

Front 26

Bernoulli Process

Back 26

a sequence of Bernoulli Trials such that:

1) Each trial has only two mutually exclusive outcomes, one a success, the other a failure.

2) The prob. of a success p, remains constant from trial to trial, the prob. of failure is denoted as q.

3) Trials are independent

Front 27

Binomial Distribution

Back 27

If the random variable x is defined to count the number of successes and our experiment is a Bernoulli Process, the resulting distribution is Binomial

Front 28

The Poisson Distribution

Back 28

Describes the number of events that will occur in a specified time, area or volume

Front 29

Characteristics of a Poisson Distribution

Back 29

1) Experiment consists of counting the number of times a certain event occurs in a specific time, area or volume

2) The prob. of any even tis the same for all units

3) The number of events that occur in one unit of time, area or volume is independent of the number of events that occur in other units

4) The mean of each unit is known and is denoted by Lambda.