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**SAS Functions [4-14]**

**RAND Function**

You can use the

**RAND**function to create random values based on a pre-specified distribution.

In this session, the examples are based on a

**TEMPLATE**data set:

[Note: Copy and run the code in the yellow box below before going through the examples.]

__Example__

**1. Uniform Distribution**

Data Rand1;

Set Template;

Randno = RAND('uniform');

Run;

Set Template;

Randno = RAND('uniform');

Run;

The

**RAND**function above creates a list of random values based on the uniform distribution.

The values from a uniform distribution will be between 0 to 1.

(Try it! You might get some totally different values but it is totally normal.)

You can also use other distributions such as Bernoulli and Binomial distributions to generate the random values as well.

**2. Bernoulli Distribution**

__Example__

Data Rand2;

Set Template;

Randno = RAND('bernoulli', 0.7);

Run;

*The second parameter is

**p**(probability of success).

**3. Binomial Distribution**

__Example__

Data Rand3;

Set Template;

Randno = RAND('binomial', 0.7, 10);

Run;

*The second parameter is

**p**( probability of success). The third parameter is

**n**(number of independent Bernoulli trials).

**4. Exponential Distribution**

__Example__

Data Rand4;

Set Template;

Randno = RAND('exponential');

Run;

**5. Normal Distribution**

__Example__

Data Rand5;

Set Template;

Randno = RAND('Normal', 5, 1.5);

Run;

*The second and third parameters are

**mean**and

**standard deviation**, respectively.

**6. Poisson Distribution**

__Example__

Data Rand6;

Set Template;

Randno = RAND('poisson', 2);

Run;

*The second parameter is

**m**(numeric mean parameter).

**Exercise**

__Background (optional)__

You have played fantasy basketball for a number of years.

You are getting sick of the 'Experts' giving you inaccurate predictions years after years.

You are going to do your own prediction this year using SAS.

__Task__

Copy and run the

**STAT**data set from the yellow box below.

**STAT**contains 3 variables:

**PLAYER:**The player being analyzed**STAT:**The statistics (points, rebounds, assists, blocks and field goals percentage)**CURRENT:**The current statistics from said player

Now, you are going to predict the player's statistics for next season based on a random factor.

The random factor should be a value between 0.9 to 1.1.

You can generate the random factor based on a UNIFORM distribution.

The prediction statistics will be calculated as:

**Pred = Curr x Random Factor**

Write a SAS program to calculate the stat prediction for said player. Create any data set or variables if necessary.

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