Probability calculator

Calculate probabilities or values based on the *Binomial*, *Chi-squared*, *Discrete*, *F*, *Exponential*, *Normal*, *Poisson*, *t*, or *Uniform* distribution.

Suppose Consumer Reports (CR) wants to test manufacturer claims about battery life. The manufacturer claims that more than 90% of their batteries will power a laptop for at least 12 hours of continuos use. CR sets up 20 identical laptops with the manufacturer’s batteries. If the manufacturer’s claims are accurate, what is the probability that 15 or more laptops are still running after 12 hours?

The description of the problem suggests we should select `Binomial`

from the `Distribution`

dropdown. To find the probability, select `Values`

as the `Input type`

and enter `15`

as the `Upper bound`

. In the output below we can see that the probability is 0.989. The probability that exactly 15 laptops are still running after 12 hours is 0.032.

A manufacturer wants to determine the appropriate inventory level for headphones required to achieve a 95% service level. Demand for the headphones obeys a normal distribution with a mean of 3000 and a standard deviation of 800.

To find the required number of headphones to hold in inventory choose `Normal`

from the `Distribution`

dropdown and then select `Probability`

as the `Input type`

. Enter `.95`

as the `Upper bound`

. In the output below we see the number of units to stock is 4316.

A **discrete** random variable can take on a limited (finite) number of possible values. The **probability distribution** of a discrete random variable lists these values and their probabilities. For example, probability distribution of the number of cups of ice cream a customer buys could be described as follows:

- 40% of customers buy 1 cup;
- 30% of customers buy 2 cups;
- 20% of customers buy 3 cups;
- 10% of customers buy 4 cups.

We can use the probability distribution of a random variable to calculate its **mean** (or **expected value**) as follows;

\[ E(C) = \mu_C = 1 \times 0.40 + 2 \times 0.30 + 3 \times 0.20 + 4 \times 0.10 = 2\,, \]

where \(\mu_C\) is the mean number of cups purchased. We can *expect* a randomly selected customer to buy 2 cups. The variance is calculated as follow:

\[ Var(C) = (1 - 2)^2 \times 0.4 + (2 - 2)^2 \times 0.3 + (3 - 2)^2 \times 0.2 + (4 - 2)^2 \times 0.1 = 1\,. \]

To get the mean and standard deviation of the discrete probability distribution above, as well as the probability a customer will buy 2 or more cups (0.6), specify the following in the probability calculator.

You can also use the probability calculator to determine a `p.value`

or a `critical value`

for a statistical test. See the helpfiles for `Single mean`

, `Single proportion`

, `Compare means`

, `Compare proportions`

, `Cross-tabs`

in the *Basics* menu and `Linear regression (OLS)`

in the *Model* menu for details.

Add code to *Report > Rmd* to (re)create the analysis by clicking the icon on the bottom left of your screen or by pressing `ALT-enter`

on your keyboard.

If a plot was created it can be customized using `ggplot2`

commands (e.g., `plot(result) + labs(title = "Normal distribution")`

). See *Data > Visualize* for details.

For an overview of related R-functions used by Radiant for probability calculations see *Basics > Probability*