Probability Distributions

1 Binomial Distribution

1.1 Question

• What is a binomial distribution in Statistics?
• What is binomial distribution used for?
• Please argue 4 requirements needed to be a binomial distribution?
• Is a binomial distribution a normal distribution?
• Suppose there are twenty multiple choice questions in an Statistics class quiz. Each question has five possible answers, and only one of them is correct. Find the probability of having four or less correct answers if a student attempts to answer every question at random (Explain this exercise by using R).

1.2 Answer

• A sequence of identical bernoulli event, The binomial distribution is a probability distribution that summarizes the likelihood that a value will take one of two independent values under a given set of parameters or assumptions. binomial distribution only counts two states, typically represented as 1 (for a success) or 0 (for a failure) given a number of trials in the data.
• To find the probability of succes and failure from an event.
• (1)It has a μ and σ parameter whose location and distribution form can be self-defined, (2)The average is located in the middle of the distribution and the distribution is symmetrical around a straight upright line drawn through the average, (3)Total area below normal curve is 1 (this applies to the entire continuous probability distribution), (4)It can cut the axis horizontally and can extend both tails of the curve until there is no limit.
• Binomial distribution isn’t a normal distribution, because binomial distribution is discrete distribution which gives a probability of not being zero only for (some) integers, whereas the normal distribution is a continuous distribution where each normal density is zero for all real numbers.

prob <- 0.2
# 1/5 = 0,2
dbinom (0, size =20, prob) +
  dbinom (1, size =20, prob)+
  dbinom (2, size =20, prob)+
  dbinom (3, size =20, prob)+
  dbinom (4, size =20, prob)

## [1] 0.6296483

pbinom (4, size=20, prob )

## [1] 0.6296483

2 Poisson Distribution

2.1 Question

• What is a Poisson distribution in Statistics?
• When and Why is Poisson distribution used?
• What is the difference between Binomial distribution and Poisson distribution?
• If twenty cars are crossing a bridge per minute on average, visualize and find the probability of having thirteen or more cars crossing the bridge in a particular minute (Explain this exercise by using R).
• Suppose the probability that a drug produces a certain side effect is \(p = 0.1%\) and \(n = 1,000\) patients in a clinical trial receive the drug. What is the probability 0 people experience the side effect by using visualization techniques? (Explain this exercise by using R)

2.2 Answer

• Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant average rate and independently of the time since the last event.
• Distribution poisson is used when want to know probability the number of events at certain intervals such as distance, area and volume. Poisson distribution is used because it can be applied to systems with large numbers of events that may occur, which is quite rare in fact.
• Difference between Binomial distribution and Poisson distribution : (1)Binomial distribution is a distribution in which the probability of the number of repeated experiments is studied. The probability distribution that provides the number of independent events occurs randomly within a given period, called the probability distribution, (2)Binomial distribution is biparametric, displayed by two parameters n and p while poisson distribution is uniparametric, marked with a single parameter m, (3)The probability of success is constant in binomial distribution but in poisson distribution, there is a small number of chances of success.

1-ppois(13,20)

## [1] 0.9338724

ppois(13, lambda = 20, lower.tail=FALSE)

## [1] 0.9338724

1-ppois(0.001,1000)

## [1] 1

ppois(0.001, lambda = 1000, lower.tail=FALSE)

## [1] 1

3 Uniform Distribution (Continuous or Discrete)

3.1 Question

• How do you find the continuous uniform distribution?
• Is the uniform distribution discrete or continuous?
• What does it mean to have a uniform distribution?
• How do you solve uniform distribution problems using R?

3.2 Answer

• When random variable X has a value (continuous) with the same probability
• Discrete because Random variable X has a value of x1,x2,x3,… xk with equal probability,
• A uniform distribution is a type of probabilities where all outcomes are equally likely; each variable has the same probability to the outcome.
• Applying the runif function in R

4 Exponential Distribution

4.1 Question

• What is exponential distribution example?
• What is the exponential distribution used for?
• What is exponential distribution rate?
• Is exponential distribution discrete or continuous?
• Suppose the mean checkout time of a supermarket cashier is three minutes. Find the probability of a customer checkout being completed by the cashier in less than two minutes (Explain this exercise by using R).

4.2 Answer

• Exponential distribution is applied in the theory of reliability (hold time), wait time, queue problems and others.
• The exponential distribution for describes the arrival time of a randomly recurring independent event sequence. If Μ is the mean waiting time for the next event recurrence.
• Process in which events occur continuously and independently at a constant average rate.
• exponential distribution is continuous because value x is a hose whose value is greater than or equal to zero (X≥0).

pexp(2, rate=1/3)

## [1] 0.4865829

5 Normal Distribution

5.1 Question

• What is a normal distribution and Standard Normal Distrubution in Statistics?
• Why is it called normal distribution?
• How do you calculate normal distribution?
• What are the characteristics of a normal distribution?
• The annual salaries of employees in a large company are approximately normally distributed with a mean of $50,000 and a standard deviation of $20,000 (Explain this exercise by using R).
  • What percent of people earn less than $40,000?
  • What percent of people earn between $45,000 and $65,000?
  • What percent of people earn more than $70,000?

5.2 Answer

• The normal distribution is defined by the following probability density function, where μ is the population mean and σ^2 is the variance. Standar normal distribution is distribution with mean = 0 and standar deviation = 1
• Because has an x-axis range from minus to positive
• Find value z with value x minus mean Divided standar deviation.
• Characteristics of a normal distribution : (1)The average is located in the middle of the distribution and the distribution is symmetrical around a straight upright line drawn through the average, (2)The total area below the normal curve is 1, (3)Standard deviation σ which is the determinant of the width of the curve. The more pointed the curve will be when the smaller the σ, (4)The curve is shaped like a bell or a genta.

pnorm (40000, mean=50000, sd=20000)

## [1] 0.3085375

pnorm (65000, mean=50000, sd=20000) -
  pnorm (45000, mean=50000,sd=20000)

## [1] 0.372079

pnorm(70000, mean=50000,sd=20000)

## [1] 0.8413447

6 Chi-squared Distribution

6.1 Question

• What is the chi square distribution used for?
• What is the chi square distribution formula?
• What is the chi distribution?
• Is Chi square distribution continuous?
• 256 visual artists were surveyed to find out their zodiac sign. The results were: Aries (29), Taurus (24), Gemini (22), Cancer (19), Leo (21), Virgo (18), Libra (19), Scorpio (20), Sagittarius (23), Capricorn (18), Aquarius (20), Pisces (23). Test the hypothesis that zodiac signs are evenly distributed across visual artists (Explain this exercise by using R).

6.2 Answer

• chi square distribution used for goodness of fit an observation distribution with theoretical distribution, free data analysis classification criteria, and the presumption of trust hoses for normal distributed population deviations from sample standard deviations.
• V=X^2 1 + X^2 2 + ⋯ + X^2m∼χ2m∼
• The number of normal random mods that are mutually free.
• Chi square distribution continuous because variabel X continuous

7 Student \(t\) Distribution

7.1 Question

• What is the Student \(t\) distribution used for?
• Why is it called Student \(t\) distribution?
• How do you find the Student \(t\) distribution?
• What kind of distribution is the \(t\) distribution?
• Philips Corporation manufactures light bulbs. The CEO claims that an average Acme light bulb lasts 300 days. A researcher randomly selects 15 bulbs for testing. The sampled bulbs last an average of 290 days, with a standard deviation of 50 days. If the CEO’s claim were true, what is the probability that 15 randomly selected bulbs would have an average life of no more than 290 days? (Explain this exercise by using R)

Note: There are two ways to solve this problem, using the T Distribution Calculator. Both approaches are presented below. Solution A is the traditional approach. It requires you to compute the t statistic, based on data presented in the problem description. Then, you use the T Distribution Calculator to find the probability. Solution B is easier. You simply enter the problem data into the T Distribution Calculator. The calculator computes a t statistic “behind the scenes”, and displays the probability. Both approaches come up with exactly the same answer.

7.2 Answer

• t student distribution used to test the two-way hypothesis
• because the standard deviation of the population(s) is unknown so the value is replaced by the sample standard deviation ( S )
• Assume further that Z and V are independent, then the following quantity follows a Student t distribution with m degrees of freedom.
• Normal distribution and chi square distribution

8 F Distribution

8.1 Question

• What does F distribution mean?
• What does the F distribution tell you?
• What is an F distribution used for?
• How is the F distribution derived?
• Suppose you randomly select 7 women from a population of women, and 12 men from a population of men. The table below shows the standard deviation in each sample and in each population. (Compute the f statistic, Explain this exercise by using R)

Population	Population standard deviation	Sample standard deviation
Women	30	35
Men	50	45

8.2 Answer

• F distribution is two free khi-squared random variables, each divided by their degree of freedom.
• F distribution is continuous distribution
• F distribution is often used in statistical testing, including variance analysis and regression analysis.

#women's data
x1=30
x2=50
y1=35
y2=45
p =(y1^2/x1^2)
q = (y2^2/x2^2)
F1 = p/q
F1

## [1] 1.680384

#man's data 
F2 = q/p
F2

## [1] 0.595102