Below, the 31-year maximum significant wave heights are classified. The arithmetic mean of the 31-year maximum significant wave heights is 0.88 m and its standard deviation is 0.06 m. Check (test) whether this classified data set data is suitable for normal distribution or not at the 5% significance level. Class :0,75 – 0,80 0,80 – 0,85 0,85 – 0,90 0,90 – 0,95 0,95 – 1,00 1,00 – 1,05 Number of data respectively: 2 - 7 - 1 - 2 - 4 - 5 - 1

Below, the 31-year maximum significant wave heights are classified. The arithmetic mean of the 31-year maximum significant wave heights is 0.88 m and its standard deviation is 0.06 m. Check (test) whether this classified data set data is suitable for normal distribution or not at the 5% significance level. Class :0,75 – 0,80 0,80 – 0,85 0,85 – 0,90 0,90 – 0,95 0,95 – 1,00 1,00 – 1,05 Number of data respectively: 2 - 7 - 1 - 2 - 4 - 5 - 1

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Description: Below, the 31-year maximum significant wave heights are classified. The arithmetic mean of the 31-year maximum significant wave heights is 0.88 m and its standard deviation is 0.06 m. Check (test) whether this classified data set data is suitable for

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.4: Distributions Of Data

Problem 19PFA

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Topic Video

Question

Class :0,75 – 0,80 0,80 – 0,85 0,85 – 0,90 0,90 – 0,95 0,95 – 1,00 1,00 – 1,05

Number of data respectively: 2 - 7 - 1 - 2 - 4 - 5 - 1

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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