[size=150]The person whose reaction time is being tested will hold their thumb and forefinger open on either side of the flat side of the ruler at the 0 inch mark on the other side of the ruler. The person running the test will drop the ruler and the other person should close their fingers as soon as they notice the ruler moving to catch it. The distance that the ruler fell should be used as the data for this experiment.[/size][br][br][i]With your partner[/i], write a statistical question that can be answered by comparing data from two different conditions for the test.

[size=150][i]With your partner,[/i] run the experiment to collect at least 20 results under each condition.[/size][br][br][size=150]Analyze your 2 data sets to compare the statistical question. Next, create a visual display that includes:[/size][br][list][*]your statistical question[/*][*]the data you collected[/*][*]a data display[/*][*]the measure of center and variability you found that are appropriate for the data[/*][*]an answer to the statistical question with any supporting mathematical work[/*][/list]

Use the table of information to compare the size of students with different dominant hands.

[list][*][size=150]mean of 65.3 degrees Fahrenheit[/size][/*][*][size=150]median of 63.5 degrees Fahrenheit[/size][/*][*][size=150]standard deviation of 9.3 degrees Fahrenheit[/size][/*][*][size=150]IQR of 7.1 degrees Fahrenheit[/size][/*][/list][br]Recall that the temperature [math]C[/math], measured in degrees Celsius, is related to the temperature [math]F[/math] measured in degrees Fahrenheit, by [math]C=\frac{5}{9}(F-32)[/math][br]Describe how the value of each statistic changes when 32 is subtracted from the temperature in degrees Fahrenheit.[br]

Describe how the value of each statistic further changes when the new values are multiplied by [math]\frac{5}{9}[/math].

Describe how to find the value of each statistic when the temperature is measured in degrees Celsius.[br]

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W/IIg68LrvpGEtYD+JgPFLnVAzSD1nMl7hAB9d+XZCIrR22XHv7UrpK6molRpsIC70ETlWDgZiaPHWAQr+HV1Wu9UMOOlKH10tQkLA46t6R4YXdHOhNFeZEO3P0dqT65tQhK8XLWNb1JfJNFucUK/rAsa7vbN7Iw+5BvsEGdA1qNzlVzuga5DMbT7v5zupKzzY35A3yjfGezK7JbDBOyEvFCOO971K+nUyHyRgk32zAV+iKS1vrwuupnRi9MF2LgAiIwDgQUGIcBy9qDiIgAo0SUGJsFKeEiYAIjAMBJcZx8KLmIAIi0CgBJcZGcUqYCIjAOBBQYhwHL2oOIiACjRJQYmwUp4SJgAiMAwElxnHwouYgAiLQKAElxkZxSpgIiMA4EFBiHAcvag4iIAKNEvgXNfUXwp4XaGQAAAAASUVORK5CYII=[/img][br]Give an example of a box plot that has a greater median and a greater measure of variability, but the same minimum and maximum values.

[size=150]One dog’s vitamin C level was not in the normal range. It was 0.9 milligrams per liter, which is a very low level of vitamin C.[/size][br][br]If the value 0.9 is eliminated from the data set, does the mean increase or decrease?[br]

If the value 0.9 is eliminated from the data set, does the standard deviation increase or decrease?[br]

[size=150]6 6 7 8 8 8 9 10 10 12 13 14 15 16 30[/size] [br][br]The median is 10 hours, Q1 is 8, Q3 is 14, and the IQR is 6 hours. Are there any outliers in the data? or show your reasoning.

[list][size=150][*]mean: 15,976 followers[/*][*]median: 16,432 followers[/*][*]standard deviation: 3,279 followers[/*][*]Q1: 13,796[/*][*]Q3: 19,070[/*][*]IQR: 5,274 followers[/*][/size][/list]Give an example of a number of followers that a very popular band might have that would be considered an outlier for this data.

Give an example of a number of followers that a relatively unknown band might have that would be considered an outlier for this data. [br]

[size=150]The weights of one population of mountain gorillas have a mean of 203 pounds and standard deviation of 18 pounds. The weights of another population of mountain gorillas have a mean of 180 pounds and standard deviation of 25 pounds. Andre says the two populations are similar. [br][/size][br]Do you agree? Explain your reasoning.

[size=150]The box plot represents the distribution of the amount of change, in cents, that 50 people were carrying when surveyed.[br][img]data:image/png;base64,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[/img][br]The box plot represents the distribution of the same data set, but with the maximum, 203, removed.[br][img]data:image/png;base64,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[/img][br]The median is 25 cents for both plots. After examining the data, the value 203 is removed since it was an error in recording.[/size][br][br]Explain why the median remains the same when 203 cents was removed from the data set.[br]

When 203 cents is removed from the data set, does the mean remain the same? Explain your reasoning.[br]