This is a Coursera Capston project.

This is my Coursera course report.

Done

--- title: "Coursera Reproducible Pitch Presentation" author: "" highlighter: highlight.js output: html_document knit: slidify::knit2slides mode: selfcontained hitheme: tomorrow subtitle: Md. Rajib Hossain framework: io2012 widgets: [] --- ## Introduction ### Probability Distribution In probability and statistics, a probability distribution assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey, or procedure of statistical inference. First we describe some common terms in probability ### Probability density function Probability density,Probability density function, p.d.f: most often reserved for continuous random variables. ### Cumulative distribution function In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found to have a value less than or equal to x. --- .class #id ## List Probability Density Functions In my shiny application I provide some common Density Functions which I describe ### Normal distribution In probability theory, the normal (or Gaussian) distribution is a very commonly occurring continuous probability distribution a function that tells the probability that any real observation will fall between any two real limits or real numbers, as the curve approaches zero on either side. ### Chi-squared distribution In probability theory and statistics, the chi-squared distribution with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables. --- .class #id ### Student's t-distribution In probability and statistics, Student's t-distribution (or simply the t-distribution) is a family of continuous probability distributions that arise when estimating the mean of a normally distributed population in situations where the sample size is small and population standard deviation is unknown. ### Poisson distribution In probability theory and statistics, the Poisson distribution (French pronunciation in English usually named after French mathematician Simeon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume. --- .class #id ### Binomial distribution In probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Therewith the probability of an event is defined by its binomial distribution. ### Hypergeometric distribution In probability theory and statistics, the Hypergeometric distribution is a discrete probability distribution that describes the probability of k successes in n draws without replacement from a finite population of size N containing exactly K successes. This is in contrast to the binomial distribution, which describes the probability of k successes in n draws with replacement. --- { } ## Thank You

Done

--- title: "Coursera Reproducible Pitch Presentation" author: "" highlighter: highlight.js output: html_document knit: slidify::knit2slides mode: selfcontained hitheme: tomorrow subtitle: Md. Rajib Hossain framework: io2012 widgets: [] --- ## Introduction ### Probability Distribution In probability and statistics, a probability distribution assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey, or procedure of statistical inference. First we describe some common terms in probability ### Probability density function Probability density,Probability density function, p.d.f: most often reserved for continuous random variables. ### Cumulative distribution function In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found to have a value less than or equal to x. --- .class #id ## List Probability Density Functions In my shiny application I provide some common Density Functions which I describe ### Normal distribution In probability theory, the normal (or Gaussian) distribution is a very commonly occurring continuous probability distribution a function that tells the probability that any real observation will fall between any two real limits or real numbers, as the curve approaches zero on either side. ### Chi-squared distribution In probability theory and statistics, the chi-squared distribution with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables. --- .class #id ### Student's t-distribution In probability and statistics, Student's t-distribution (or simply the t-distribution) is a family of continuous probability distributions that arise when estimating the mean of a normally distributed population in situations where the sample size is small and population standard deviation is unknown. ### Poisson distribution In probability theory and statistics, the Poisson distribution (French pronunciation in English usually named after French mathematician Simeon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume. --- .class #id ### Binomial distribution In probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Therewith the probability of an event is defined by its binomial distribution. ### Hypergeometric distribution In probability theory and statistics, the Hypergeometric distribution is a discrete probability distribution that describes the probability of k successes in n draws without replacement from a finite population of size N containing exactly K successes. This is in contrast to the binomial distribution, which describes the probability of k successes in n draws with replacement. --- { } ## Thank You

--- title : Coursera Reproducible Pitch Presentation subtitle : Md. Rajib Hossain author : framework : io2012 # {io2012, html5slides, shower, dzslides, ...} highlighter : highlight.js # {highlight.js, prettify, highlight} hitheme : tomorrow # widgets : [] # {mathjax, quiz, bootstrap} mode : selfcontained # {standalone, draft} knit : slidify::knit2slides --- ## Introduction ### Probability Distribution In probability and statistics, a probability distribution assigns a probability to each measurable subset of the possible outcomes of a random experiment, survey, or procedure of statistical inference. First we describe some common terms in probability ### Probability density function Probability density,Probability density function, p.d.f: most often reserved for continuous random variables. ### Cumulative distribution function In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a real-valued random variable X with a given probability distribution will be found to have a value less than or equal to x. --- .class #id ## List Probability Density Functions In my shiny application I provide some common Density Functions which I describe ### Normal distribution In probability theory, the normal (or Gaussian) distribution is a very commonly occurring continuous probability distribution a function that tells the probability that any real observation will fall between any two real limits or real numbers, as the curve approaches zero on either side. ### Chi-squared distribution In probability theory and statistics, the chi-squared distribution with k degrees of freedom is the distribution of a sum of the squares of k independent standard normal random variables. --- .class #id ### Student's t-distribution In probability and statistics, Student's t-distribution (or simply the t-distribution) is a family of continuous probability distributions that arise when estimating the mean of a normally distributed population in situations where the sample size is small and population standard deviation is unknown. ### Poisson distribution In probability theory and statistics, the Poisson distribution (French pronunciation in English usually named after French mathematician Simeon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume. --- .class #id ### Binomial distribution In probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Therewith the probability of an event is defined by its binomial distribution. ### Hypergeometric distribution In probability theory and statistics, the Hypergeometric distribution is a discrete probability distribution that describes the probability of k successes in n draws without replacement from a finite population of size N containing exactly K successes. This is in contrast to the binomial distribution, which describes the probability of k successes in n draws with replacement. --- { } ## Thank You

This is my coursera assigment