Understanding Percentiles in Uniform Distribution

Understanding Percentiles in Uniform Distribution

Assessment

Interactive Video

Mathematics, Science

9th - 12th Grade

Hard

Created by

Jackson Turner

FREE Resource

This video tutorial explains how to determine percentiles in a uniform distribution using the example of an 8-week-old baby's smile times. It covers the calculation of the 90th and 75th percentiles, illustrating the process with graphs and probability density functions. The tutorial emphasizes understanding the relationship between percentiles and areas under the curve in a uniform distribution.

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the main focus of the video tutorial?

Learning about normal distribution

Calculating the mean of a dataset

Understanding percentiles in a uniform distribution

Exploring the concept of standard deviation

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is assumed about the smiling times of the baby?

They are always increasing

They follow a uniform distribution

They follow a normal distribution

They are random and unpredictable

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the probability density function for the given uniform distribution?

f(x) = x/23

f(x) = 23/x

f(x) = x^2/23

f(x) = 1/23

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the 90th percentile represent in this context?

The maximum smiling time

The average smiling time

The smiling time above which 90% of the data falls

The smiling time below which 90% of the data falls

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How is the 90th percentile calculated?

By calculating the median

By solving the equation k * (1/23) = 0.9

By finding the mean of the data

By using the standard deviation

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the value of the 90th percentile for the smiling times?

23 seconds

20.7 seconds

17.25 seconds

0 seconds

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the 75th percentile indicate in this scenario?

The shortest smiling time

The longest smiling time

The smiling time below which 75% of the data falls

The smiling time above which 25% of the data falls

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