Stats Final Review

Front 1

Discrete Random Variable Probability Distribution

Back 1

a table, formula, or graph that describes the values of a (discrete) random variable, which is one that can take on a countable # of values, and the probability associated with these values

Front 2

Calculate the complete probability distribution for a given situation

Back 2

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2.

Front 3

Expected Value (Population Mean)

Back 3

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Front 4

Population Variance

Back 4

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Front 5

Population Standard Deviation

Back 5

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Front 6

Laws of Expected Value

Back 6

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Front 7

Laws of Variance

Back 7

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3.

Front 8

Bivariate Distributions

Back 8

distribution that provides the probabilities of the combination of two variables

Front 9

Requirements for a Discrete Bivariate Distribution

Back 9

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2.

Front 10

Covariance

Back 10

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Front 11

Coefficient of Correlation

Back 11

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Front 12

Laws of Expected Value and Variance of the Sum of Two Variables

Back 12

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Front 13

Binomial Distribution

Back 13

the result of a binomial experiment, which has the following properties:
1. consists of a fixed # of trials (n)
2. Each trial has 2 possible outcomes (success/failure)
3. probability of success is p, failure 1-p
4. trials are independent of one another

Front 14

Binomial Probability Distribution

Back 14

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Front 15

Mean (Binomial Probability Distribution)
Variance (Binomial Probability Distribution)
Standard Deviation (Binomial Probability Distribution)

Back 15

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Front 16

Poisson Distribution

Back 16

characterized by the following properties:
1. # of successes in an interval is independent of the # of successes in any other interval
2. probability of success in an interval is the same for all equal-size intervals
3. probability of success in an interval is proportional to the size of the interval
4. probability of more than 1 success in an interval approaches 0 as the interval becomes smaller

Front 17

Poisson Probability Distribution

Back 17

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Front 18

Probability Density Function

Back 18

a function f(x) that approximates the curve of a histogram that would exist if the edges of the histogram's intervals were smooth

1.
2. the total area under the curve between a and b is 1.0

Front 19

Continuous Random Variable Probability Distribution

Back 19

a table, formula, or graph that describes the values of a (continuous) random variable, which is one whose values are uncountable

Front 20

Uniform Distribution

Back 20

function:

graph:

Front 21

Exponential Probability Density Function

Back 21

function:

graph:

Front 22

Normal Distribution

t Distribution

Chi-squared Distribution

F Distribution

Back 22

graph:

graph:

graph:

graph:

Front 23

Why do we have sampling distribution?

Back 23

Sampling distributions are important in statistics because they provide a major simplification on the route to statistical inference.

Front 24

Central Limit Theorem

Back 24

-states that the sampling distribution of the mean of a random sample is approximately normal for a sufficiently large sample size. The larger the sample size, the more closely the sampling distribution of x̅ (sample mean) will resemble a normal distribution; and vice versa
-this allows us to use the normal distribution as an approximation for the sampling distribution of x̅

Front 25

Finite Population Correction Factor

Back 25

standard error is:

finite population correction factor:

Front 26

Point Estimator

Back 26

uses a single value or point to draw inferences about a population in order to estimate the value of an unknown parameter of said population

Front 27

Interval Estimator

Back 27

uses an interval to draw inferences about a population in order to estimate the value of an unknown parameter of said population

Front 28

Hypothesis Testing

Back 28

1. there are 2 hypotheses (null H /alternative H )
2. begins with assumption that null hypothesis is true
3. goal is to determine if there is enough evidence to infer that the alternative hypothesis is true
4. there are only 2 decisions (conclude enough evidence to support null or alternative)
5. two possible errors:
Type I -reject true null or Type II - dont reject false null
--P(Type I error) = alpha P(Type II error) = beta

Front 29

Confidence Interval Estimator

Back 29

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