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1.1 Outliers Outlier: is an extreme value that is much less than or much greater than the other data values. Have a strong effect on the mean and standard.

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Presentation on theme: "1.1 Outliers Outlier: is an extreme value that is much less than or much greater than the other data values. Have a strong effect on the mean and standard."— Presentation transcript:

1 1.1 Outliers Outlier: is an extreme value that is much less than or much greater than the other data values. Have a strong effect on the mean and standard deviation. There are different ways to find, one way is to look for data values that are more than 3 standard deviations from the mean.

2 1.1 Outliers Example 1: The number of electoral votes in 2004 for 11 western states are shown. Find the mean and standard deviation of the data using your calculator. Identify any outliers and describe how they affect the mean and standard deviation. 11, 3, 7, 4, 3, 55, 4, 5, 9, 10, 5

3 Outliers Example 2: In the 2003 and 2004 American League Championship Series the New York Yankees scored the following numbers of run against the Boston Red Socks: 2, 6, 4, 2, 4, 6, 6, 10, 3, 19, 4, 4, 2, 3. Identify the outliers, and describe how it affects the mean and standard deviation.

4 Definitions Population: the entire group of people or objects that you want information about ***Parameter is used to describe a population Sample: part of the population ***Statistic is used to describe a sample Random sample (probability sample): when every member of a population has an equal chance of being selected for a sample (less likely to be biased)

5 Definitions Cont. Biased sample: a sample that may not be representative of a population (the population can be underrepresented or overrepresented.) Underrepresented: one or more of the parts of a population are left out when choosing the sample Overrepresented: a greater emphasis is placed on one or more of the parts of a population when choosing the sample

6 Example 1 A car factory just manufactured a load of 6,000 cars. The quality control team randomly chooses 60 cars and tests the air conditioners. They discover that 2 of the air conditioners do not work. Identify the population and the sample.

7 Example 2 Decide whether each sampling method could result in a biased sample. Explain your reasoning. A. A survey of a city’s residents is conducted by asking 20 randomly selected people at a grocery store whether the city should impose a beverage tax. B. A survey of students at a school is conducted by asking 30 randomly selected students in an all-school assembly whether they walk, drive, or take the bus to school. C. An online news site asks readers to take a brief survey about whether they subscribe to a daily newspaper.

8 Example 3 A car dealer wants to know what percentage of the population in the area is planning to buy a car in the next year. The dealer surveys the next 15 people who come to the car lot. Are the results of the survey likely to be representative of the population? B. A restaurant owner wants to know how often families in his area go out for dinner. He surveys 25 families who eat at his restaurant on Tuesday night. Are his results likely to be representative of the population? Explain.

9 Example 4-Making Predictions
In a survey of 40 employees at a company, 18 said they were unhappy with their pay. The company has 180 employees. Predict the number of employees who are unhappy with their pay. unhappy employees in sample employees in sample unhappy employees in company employees in company = 18 40 x 180 = = 40x 81 = x You can predict that about 81 employees are unhappy with their pay.

10 Example 5 In a random sample of phone calls to a police station, 11 of the 25 calls were for emergencies. Suppose the police station receives 175 calls in one day. Predict the number of calls that will be for emergencies.

11 Homework Pg (3-31 odd)


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