If you missed the simulating introduction to Continuous Variables and Normal Distributions it might be worth taking a few minutes to read.

#### Z Value

The functional form for a normal distribution is a bit complicated. It can also be difficult to compare two variables if their mean and or standard deviations are different, for example heights in centimeters and weights in kilograms, even if both variables can be described by a normal distribution. To get around both of these conflicts we can define a new variable:

(1)This variable gives a measure of how far the variable is from the mean $(x-\mu)$ then "normalizes" it by dividing by the standard deviation $(\sigma)$. This new variable gives us a way of **comparing different variables**. The *z-value* tells us how many standard deviations or "how many sigmas" the variable is from its respective mean.

When the distribution function is expressed in terms of the z-value it is sometimes called the "standard normal distribution." You've got to love the creativity!

(2)#### Areas Under the Curve

To calculate the probability that a variable is within a range we have to find the area under the curve… Hooray, calculus! It turns out that there is no indefinite integral of the function! However, smart folks have figured out how to do definite integrals, but they are a bit complex so the folks who have to work with normal distributions rely on tables, which you have in your formula booklet, or calculators.

An example of the table is shown in the collapsible box below. It's as close as I could find to the one the IB gives you…

These tables can be a bit scary, but you simply need to know how to read them.

- The left most column tells you how many sigmas above the the mean to 1 decimal place.
- The top row gives the second decimal place.
- The intersection of a row and column gives the probability.

For example, if we want to know the probability that a variable is no more than 0.51 sigmas above the mean we find select the 6th row down (corresponding to 0.5) and the 2nd column (corresponding to 0.01). The intersection of the 6th row and 2nd column is 0.6950. Which tells us that there is a 69.50% percent chance that a variable is less than 0.51 sigmas above the mean…

Notice that for 0.00 sigmas the probability is 0.5000. Thus showing that there is equal probability of being above or below the mean! So nice when stuff makes sense.

### Using Ti-84 to Find Areas Under the Curve

A 6 minute video showing how to get area under the normal distribution given a range of z-values. It also covers the inverse, that is going from area to z-values. **This is something you need to know how to do!**

### "Simple" Examples

##### Example 1

Find $P(Z \leq 1.5)$

##### Example 2

Find $P(Z \geq 1.17)$

##### Example 3

Find $P( -1.16 \leq Z \leq 1.32)$

Many errors in this article. Example 2 for the simple questions show P(Z>1.5) when the worked example is 1.17. Also, example 3 shows P(−1.16≥Z≤1.32) but then it changes correctly at the end to P(−1.16≤Z≤1.32).

Nonetheless, the article still helped me. Although, it'd be nice to have the worked examples for the harder questions since now I'd like help with those.

Thanks.

ReplyOptionsThanks for the extra set of eyes. I fixed the two errors you mentioned.

I'll see if I can get an few more solutions up on the page.

-HS

ReplyOptionsI got solutions to the last 3 examples up. As always extra eyes are appreciated to find typos and errors.

-HS

ReplyOptionsFor example 5

b) why did you use 153 for the mean.. I think it's supposed to be 155.7…

Thanks!

ReplyOptionsQuestion 6 - Part b. 72 is one standard deviation away, but 90 is not. 88 would be 1 standard deviation away.

ReplyOptionsExample 5:

Your answer for the mean is wrong in part a. I'm pretty sure you don't need to make it negative. I think the mean should be 150.3

ReplyOptionsAgree with Chelsea on Example 5:

The question states that the 0.705 is the probability for a boy being shorter than 153, or P(X<153)=0.705. You solved (and indeed wrote) the opposite: P(X>153)=0.705.

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