week 3 _ D2

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Woods Wrote:

How would you differentiate a discrete from a continuous random variable? 

· The statistical variable that assumes a finite set of data and a countable number of values, then it is called a discrete variable. ...

· For non-overlapping or otherwise known as mutually inclusive classification, wherein both the class limit are included, is applicable for the discrete variable. ...

· In a discrete variable, the range of specified numbers is complete, which is not in the case of a continuous variable.

· Discrete variables are the variables, wherein the values can be obtained by counting. On the other hand, Continuous variables are the random variables that measure something.

· Discrete variable assumes independent values whereas continuous variable assumes any value in a given range or continuum.

· A discrete variable can be graphically represented by isolated points. Unlike, a continuous variable which can be indicated on the graph with the help of connected points.

Reference:

keydifferences.com/difference-between-discrete-and-continuous-variable.html

Leroy Wrote:

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What Is a Random Variable?

A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment's outcomes. Random variables are often designated by letters and can be classified as discrete, which are variables that have specific values, or continuous, which are variables that can have any values within a continuous range.

Discrete Random Variable

When the random variable can assume only a countable, sometimes infinite, number of values.

Continuous Random Variable

When the random variable can assume an uncountable number of values in a line interval.

There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.

There are only two possible outcomes, called success and failure, for each trial. The outcome that we are measuring is defined as a success, while the other outcome is defined as a failure. The letter p denotes the probability of a success on one trial, and q denotes the probability of a failure on one trial. p + q = 1.

The n trials are independent and are repeated using identical conditions. Because the n trials are independent, the outcome of one trial does not help in predicting the outcome of another trial. Another way of saying this is that for each individual trial, the probability, p, of a success and probability, q, of a failure remain the same. Let us look at several examples of a binomial experiment.

Reference:

https://www.investopedia.com/terms/r/random-variable.asp

https://online.stat.psu.edu/stat500/lesson/3/3.1

https://www.texasgateway.org/resource/43-binomial-distribution-optional

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Edited by Leroy Kelly on Jun 7, 2021, 9:32:30 PM