Compare a Mean with Individual Values
Add the values and divide by the number of supplied values.
About 18 minutes, plus any extended practice
Supplied materials
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Learn the idea
A mean is the total divided by the number of values. It summarizes a dataset without preserving every observation. In a small dataset an unusually large value can pull the mean upward. Use the original list as well as the summary before making a statement about typical observations.
Add the values and divide by the number of supplied values. The mean need not equal a value that any item actually has.
Use this supplied case: Paper Python 3 expression: sum([2,4,6])/len([2,4,6]). What value results? The task is to connect the given information to the computing idea. Write a short evidence trail so another learner can reproduce your choice. The simplified case describes the stated model, not every real system.
- Represent the supplied worked case with a labeled drawing, table or short trace.
- Add the values and divide by the number of supplied values.
- Explain why the worked answer fits and why one alternative does not.
- Try the new check and practice cases; revisit the stated rule if your evidence does not match.
See the method in action
Paper Python 3 expression: sum([2, 4, 6])/len([2, 4, 6]). What value results? The supported answer is 4.0. Tracing the expression under Python 3 rules gives 4.0. Add the values and divide by the number of supplied values. The mean need not equal a value that any item actually has. A mean is the total divided by the number of values.
Check your reasoning
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Helpful tips
- Add the values and divide by the number of supplied values.
- The mean need not equal a value that any item actually has.
See the answer and explanation
5.0
The numerator evaluates to 15 and the denominator to 3. Divide 15 by 3 to obtain 5.0; keep every supplied observation in the denominator. Add the values and divide by the number of supplied values.
Take it further
New list[2, 3, 10]has mean 15/3=5 and median 3. Explain how the high value affects the mean; the mean is not every observation.
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More practice: try new cases
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