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The 68-95-99.7% Rule and z-scoresPowerPoint Presentation

The 68-95-99.7% Rule and z-scores

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Presentation Transcript

Normal distribution

- The shape of histograms sometimes approximate what is known as a “bell-shape.”

Normal distribution

- By joining the columns of the histogram together, a bell-shaped curve is formed.
- When we get data that forms a bell-shape, we call it a normal distribution.
- Two important things to remember about normal distributions:
- The mean of the data, , is at the centre of the bell-curve.
- Most of the data is gathered close to the mean. The further we go from the mean the less data there is.

The 68-95-99.7% rule

- Recall that standard deviation, s, is a measure of the spread of the data.
- In a normal distribution,
68% of data have values within 1 standard deviation of the mean.

The 68-95-99.7% rule

95% of the data have values within 2 standard deviations of the mean.

The 68-95-99.7% rule

99.7% of the data have values within 3 standard deviations of the mean.

The 68-95-99.7% rule

- Using the 68-95-99.7% rule, we can work out the percentage of data in each section of the bell curve.

Questions

- Examples on board 1I, Q. 2, 3, 4, 10, 13
- Questions: 5, 7, 9, 11, 14

z-scores

- The z-score indicates the position of a certain score in a set of data relative to the mean.
- It enables us to compare scores in different distributions.
- A z-score of zero means that the score is equal to the mean. A negative z-score means that the score is below the mean, and a positive z-score is a score above the mean.

z-scores

- Calculate z-scores using the formula
where x is the score, is the mean, and s is the standard deviation.

- A z-score of -1 would mean that the score is one standard deviation below the mean. A score that is one standard deviation above the mean would have a z-score of +1.

Questions

- Examples: 15, 20, 23
- Questions: 16, 19, 22, 25

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