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Algebra 2 Statistics and probability

Normal distribution and fitting data with mean & SD

20 practice questions 0 video lessons Theory + worked examples

The Normal Distribution

Common Core Algebra 2 • Standard S-ID.4 • Statistics & Probability

The Normal Distribution is a topic in Statistics & Probability in the Common Core State Standards. It is aligned to Standard S-ID.4, which requires students to use the mean and standard deviation of a data set to fit it to a normal distribution and estimate population percentages.

The normal distribution is a symmetric bell curve; the 68-95-99.7 rule gives those percents within 1, 2, and 3 standard deviations of the mean.

Common Core Algebra 2 › Statistics & Probability › The Normal Distribution  —  Standard S-ID.4

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Theory

The normal distribution is a symmetric bell curve described by its mean \(\mu\) and standard deviation \(\sigma\). The 68-95-99.7 rule gives:

  • \(68\%\) of data within \(1\sigma\) of the mean,
  • \(95\%\) within \(2\sigma\),
  • \(99.7\%\) within \(3\sigma\).
A \(z\)-score \(z=\dfrac{x-\mu}{\sigma}\) counts standard deviations from the mean.
The normal distribution The normal curve is symmetric, with about 68, 95, and 99.7 percent of data within one, two, and three standard deviations. -3σ -2σ -1σ μ 68% 95%
The bell curve and the 68-95-99.7 rule.
68-95-99.7 rule 68-95-99.7 rule 68-95-99.7 rule 68% within 1σ of the mean 95% within 2σ 99.7% within 3σ z = (x - μ) / σ
The empirical rule and z-scores.

The z-score:

\[z=\dfrac{x-\mu}{\sigma}\]
the z-score is the value minus the mean over the standard deviation
The curve is symmetric, so each tail beyond a \(z\) is half the outside.

How to use the normal model

  1. Identify the mean and standard deviation.
  2. Count standard deviations from the mean.
  3. Apply the 68-95-99.7 percentages.
  4. Use \(z\)-scores for other values.
Example 1 — One standard deviation
Test scores are normal with mean \(100\), SD \(15\). Between what values are \(68\%\)?
Solution

Within \(1\sigma\) of the mean.

\(100\pm15\)\(=\)\(85\ \text{to}\ 115\)
68 percent are between 85 and 115
Example 2 — Two standard deviations
For the same scores, between what values are \(95\%\)?
Solution

Within \(2\sigma\).

\(100\pm30\)\(=\)\(70\ \text{to}\ 130\)
95 percent are between 70 and 130
Example 3 — Tail percentage
What percent of scores are above \(115\)?
Solution

\(115\) is \(1\sigma\) above; \(68\%\) are within, leaving \(32\%\) split.

\(\dfrac{100-68}{2}\)\(=\)\(16\%\)
16 percent are above 115
Example 4 — z-score
Find the \(z\)-score of \(130\) for mean \(100\), SD \(15\).
Solution

Use \(z=\dfrac{x-\mu}{\sigma}\).

\(z\)\(=\)\(\dfrac{130-100}{15}=2\)
the z-score is 2

Common pitfalls

The percentages are within \(\pm\) the SD, split evenly on each side.
A tail beyond \(1\sigma\) is \(16\%\), not \(32\%\).
The \(z\)-score subtracts the mean before dividing.

Frequently asked questions

What is the 68-95-99.7 rule?

Those percents of data lie within 1, 2, and 3 SDs of the mean.

What is a z-score?

The number of standard deviations a value is from the mean.

What percent is above one standard deviation?

About \(16\%\).

Is the normal curve symmetric?

Yes — it is symmetric about the mean.