Section 2 - Univariate Distributions

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120 Terms

1

Discrete versus Continuous

Discret: A set of countable values

ex: {1,2,3,…}

Continuous: Values in intervals

ex: (0,100)

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2

Probability Function for a Continuous Random Variable and conditions

  • Probability Density Function (PDF) → not equal to Pr(X=x) since it can take on an infinite number of values

  • Denoted as f(x), fx(x)

<ul><li><p>Probability Density Function (PDF) → not equal to Pr(X=x) since it can take on an infinite number of values</p></li><li><p>Denoted as f(x), fx(x)</p></li></ul>
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3

What is a Discrete Uniform Distribution and its criteria?

1) Finite Number of possible values

2) Consecutively spaced by 1 (ex: 1,2,3,...,10 inclusive)

3) Equally likely to be observed

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Cumulative Distribution Function (CDF) for a Discrete Random Variable

  • Denoted as P(X ≤ x), F(x), Fx(x)

<ul><li><p>Denoted as P(X <span style="color: rgb(0, 29, 53)">≤ x), F(x), Fx(x)</span></p></li></ul>
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Cumulative Distribution Function (CDF) for a Continuous Random Variable

  • Denoted as P(X ≤ x), F(x), Fx(x)

<ul><li><p style="text-align: start">Denoted as P(X <span style="color: rgb(0, 29, 53)">≤ x), F(x), Fx(x)</span></p></li></ul>
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What are the two important formulas to know for a CDF for a discrete random variable?

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How do we calculate the CDF probabilities for a continuous random variable?

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What are the properties of a CDF for a Discrete Random Variable?

1) Non-decreasing

2) Fx(-∞) = 0, Fx(∞) = 1

<p>1) Non-decreasing</p><p>2) Fx(-<span style="color: rgb(77, 81, 86)">∞) = 0, </span>Fx(<span style="color: rgb(77, 81, 86)">∞) = 1</span></p>
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What are the properties of a CDF for a Continuous Random Variable?

1) Non-decreasing

2) Fx(-∞) = 0, Fx(∞) = 1

<p>1) Non-decreasing</p><p style="text-align: start">2) Fx(-<span style="color: rgb(77, 81, 86)">∞) = 0, </span>Fx(<span style="color: rgb(77, 81, 86)">∞) = 1</span></p>
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What is the survival function for a discrete random variable?

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What is the survival function for a continuous random variable?

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12

Hazard Rate Function

  • Only for continuous random variables

  • Likely not tested on the exam

<ul><li><p>Only for continuous random variables</p></li><li><p>Likely not tested on the exam</p></li></ul>
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13

What are mixed distributions?

1) Multiple distinct, discrete distributions

1) Multiple distinct, continuous distributions

1) A combination of discrete and continuous distributions

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What are the 2 ways to understand a mixed distribution?

1) Focus on the individual distribution separately

2) View the mixed distribution as a whole

<p>1) Focus on the individual distribution separately</p><p>2) View the mixed distribution as a whole</p>
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Definition: Expected Value

  • Also called the mean, expectation, or first moment

  • Denoted as E[x]

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What is the expected value for a discrete random variable?

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What is the expected value for a continuous random variable?

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What are the 3 key properties for an exoected value?

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What do we do if there is a mixed distribution?

  • Use intuitive and reasoning

  • If piecewise, split sum/integral wherever fx(x) for g(x) changes

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What is the conditional expectation for a discrete random variable?

  • Use the same general approach

  • Use conditional probabilities and conditional ranges

<ul><li><p>Use the same general approach</p></li><li><p>Use conditional probabilities and conditional ranges</p></li></ul>
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What is the conditional expectation for a continuous random variable?

  • Use the same general approach

  • Use conditional probabilities and conditional ranges

<ul><li><p>Use the same general approach</p></li><li><p>Use conditional probabilities and conditional ranges</p></li></ul>
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What is the conditional PDF?

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23

Survival Function method for Discrete Random Variables

  • Alternative to Probability Function Method

  • Can only use if the range is non-negative

<ul><li><p>Alternative to Probability Function Method</p></li><li><p>Can only use if the range is non-negative</p></li></ul>
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Survival Function method for Continuous Random Variables

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kth and 1st Raw Moment

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kth and 2nd central moment

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Definition: Variance

Average squared deviation from the mean

<p>Average squared deviation from the mean</p>
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Variance Equations for x and g(x)

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Properties for variance

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Coefficient of Variation

Measures variability of a random variable

<p>Measures variability of a random variable</p>
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Percentiles

  • πp = 100th Percentile

  • Pr(x πp) = p

  • πp​ is the percentile value such that the cumulative probability up to that value is p

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Percentile for Discrete Random Variables

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Percentile for Continuous Random Variables

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What are the common percentiles and IQR?

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What is the mode for discrete random variables?

  • Most likely values of a random variable

  • Value of x maximizes the PMF/PDF

  • The highest value for px(x) is the mode

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What is the mode for continuous random variables?

  • Critical Point with largest value of fx(x)

<ul><li><p>Critical Point with largest value of fx(x)</p></li></ul>
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Skewness: Equation

  • Measures a distributions symmetry

<ul><li><p>Measures a distributions symmetry</p></li></ul>
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What is zero skewness, positive skewness and negative skewness?

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Kurtosis: Equation

  • Measures the “peakedness” of a distribution

<ul><li><p>Measures the “peakedness” of a distribution</p></li></ul>
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What do the different levels of kurtosis mean?

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41

What is an example of a discrete uniform distribution?

1) Rolling a fair die

2) The number of customers that visit a store during a workday is uniform on {20,21,...,80}

<p>1) Rolling a fair die</p><p>2) The number of customers that visit a store during a workday is uniform on {20,21,...,80}</p>
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What are the parameters of a discrete uniform distribution?

x ~ Discrete Uniform(a,b)

a and b are the parameters

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What is the PMF of a discrete uniform distribution?

<p></p>
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What is the Expected Value (mean) of a discrete uniform distribution?

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What is the Variance of a discrete uniform distribution?

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46

What is a Bernoulli distribution and its criteria?

1) 2 possible outcomes, 0 and 1

2) 0 and 1 can be any binomial event

<p>1) 2 possible outcomes, 0 and 1</p><p style="text-align: start">2) 0 and 1 can be any binomial event</p>
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What is an example of a Bernoulli distribution?

A coin flip would be Bernoulli if we assign 1 to heads and 0 to tails and vice versa

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What if the PMF of a Bernoulli distribution?

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What if the mean of a Bernoulli distribution?

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What is the Variance of a bernoulli distribution?

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What is the Bernoulli Shortcut?

If we have an event with only 2 outcomes (a and b) then we can use this

<p>If we have an event with only 2 outcomes (a and b) then we can use this</p>
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What are the parameters of a Bernoulli Distribution?

x ~ Bernoulli (p)

Parameter is p

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53

What are the parameters of a Binomial Distribution?

x ~ binomial(n,p)

n and p are the parameters

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54

What is a Binomial Distribution and its criteria?

- The sum of n independent Bernoulli Trials with the probability of success p

- Counts the number of successes

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What is an example of a Binomial Distribution?

Tossing a coin 5 times and counting the number of heads in those 5 trials

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How can you identify a binomial distribution?

1) Fixed number of independent trials

2) Same 2 possible outcomes for each trial

3) Number of sccesses (varies) is the random variable

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How can you identify independent trials? (2 ways)

1) sampling with replacement

2) Sampling without replacement from a large population of unknown size (while population changes, the impact on each trial is small - treat the population as staying the same)

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What would dependent trials look like?

Sampling without replacement of a known population size

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59

What is the PMF for a binomial distribution?

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What is the expected value for a binomial distribution?

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What is the variance for a binomial distribution?

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62

What is the special property for the sum of a binomials?

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What is a Hypergeometric Distribution and its criteria?

1) Same situation as binomial but with DEPENDENT Bernoulli Trials

2) Sampling WITHOUT replacement from a KNOWN population size

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What are the parameters for a hypergeometric distribution and what do they mean?

x ~ Hypergeometric (N, m, n)

N is the population size

m is the successes in a population

n is the number of dependent trials

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What is the PMF of a hypergeometric distribution?

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What is the Expectation of a hypergeometric Distribution?

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What is the Variance of a hypergeometric Distribution?

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What other technique can we use to solve hypergeometric questions?

Counting techniques!

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What is a Geometric distribution and its criteria?

The number of independent bernoulli trials when we want to get the FIRST success

1) Fixed number of successes

2) Variable number of trials

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What is an example of a geometric distribution?

The number of coin tosses it takes to get the first “heads”

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How can we identify a geometric distribution?

1) Indepenedent bernoulli trials

2) Same two possible outcomes for each trials

3) Random variable is the number of trials/failues to get to ONE SUCCESS

2) Number of trials or failures? (“Number of trials before the first success” → number of failures)

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What is the parameter for a geometric distribution?

x ~ geometric(p)

p: probability of a success

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What is the trial version PMF of a geometric distribution?

X = Number of Trials to get one Success

<p>X = Number of Trials to get one Success</p>
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What is the trial version Expectation of a geometric distribution?

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What is the trial version variance of a geometric distribution?

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What is the Failure version PMF of a geometric distribution?

Y = Number of Failures before one success

<p>Y = Number of Failures before one success</p>
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What is the Failure version Expectation of a geometric distribution?

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What is the failure version Variance for a geometric distribution?

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What is the memoryless property for the trial version of a geometric distribution?

  • The outcomes of previous trials do NOT affect the probabilities of future trials

  • Consider every new trial as the first trial

<ul><li><p>The outcomes of previous trials do NOT affect the probabilities of future trials</p></li><li><p>Consider every new trial as the first trial</p></li></ul><p></p>
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What is the memoryless property for the failure version of a geometric distribution?

  • The outcomes of previous trials do NOT affect the probabilities of future trials

  • Consider every new trial as the first trial

<ul><li><p>The outcomes of previous trials do NOT affect the probabilities of future trials</p></li><li><p>Consider every new trial as the first trial</p></li></ul>
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81

What are the is the definition and parameters for a negative binomial distribution?

x ~ Negative Binomial(r,p)

The number of trials to get r successes, where each trial has a success probability of p

<p>x ~ Negative Binomial(r,p)</p><p>The number of trials to get r successes, where each trial has a success probability of p</p>
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What is the failure version expectation of a negative binomial distribution?

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What is an exmaple of a negative binomial distribution?

The number of coin tosses required to ger 5 “heads”

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What is the trial version PMF of a negative binomial distribution?

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What is the trial version expectation of a negative binomial distribution?

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What is the trial version variance of a negative binomial distribution?

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What is the special property for negative binomials?

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What is the failure version PMF of a negative binomial distribution?

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What is the failure version Variance of a negative binomial distribution?

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How can you identify a negative binomial distribution?

1) Independent bernoulli trials

2) Random variable is the number of trials/failures to get a certain number of successes

  • Fixed number of successes, r

  • Variable number of trials/variables

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What is a poisson distribution?

  • Number of events in a fixed interval

  • Occurences in disjoint intervals are independent

  • Interval is usually time but it can be something else

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What is an example of a poisson distribution?

  • The number of tornados in a field during a year

  • Count the number of flowers in a square foot of a field

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What is the parameter of a poisson distribution?

x ~ poisson(λ)

λ (lambda) is the parameter, it is the mean (event rate)

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What is the PMF of a poisson distribution?

  • Typically told to use poisson

  • Recognize from PMF

<ul><li><p>Typically told to use poisson</p></li><li><p>Recognize from PMF</p></li></ul>
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What is the expectation of a poisson distribution?

<p></p>
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What is the variance of a poisson distribution?

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What are the 2 properties of a poisson distribution?

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What is a continuous uniform distribution?

  • DO NOT CONFUSE WITH DISCRETE DISTRIBUTIONS

  • Equal-length intervals are equally likely

  • Infinite number of possible values (any value between a and b)

  • say the time to drive to work is between 8 and 12 minutes

  • Probability between 8 and 9 minutes, 9 and 10 minutes, 10 and 11 minutes, and 11 and 12 minutes are all the same

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What are the parameters of a and b?

x ~ continuous uniform(a,b)

a is the smallest value, b is the largest value

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What is the PDF of a continuous uniform distribution?

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