For the Central Limit Theorem, the idea is that the more values within your data set, the more the distribution approaches a normally distributed situation. Do you think that happened here? Why or why not?I am just having a hard time what happened in this Theorem. Can you able to help and explain please. Thank you Data and other behind the scenes calculations0.5-Leukemias Cancer for 2017Tis set of 20 means calculated from finding samples ofsize 20 from the larger set of data-0:4-S is set of 60 means calculated from finding samples ofsize 60 from the larger set of dataClick the circle next to the histogram to turn it on or off.-0.3histogram ( T,w)Data Set, Bin WidthBAR HEIGHTS OBIN ALIGNMENTCountRelative DensityCenterLeft-0.2histogram ( S,w)Data Set, Bin WidthBAR HEIGHTS OBIN ALIGNMENT-0.1-CountRelative DensityCenterLeftOpen this folder only if doing a classmate response onthe graph.500100015002000250030003500

Answered: For the Central Limit Theorem, the idea…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

For the Central Limit Theorem, the idea is that the more values within your data set, the more the distribution approaches a normally distributed situation.

Do you think that happened here? Why or why not?

I am just having a hard time what happened in this Theorem. Can you able to help and explain please. Thank you

Data and other behind the scenes calculations
0.5-
Leukemias Cancer for 2017
Tis set of 20 means calculated from finding samples of
size 20 from the larger set of data
-0:4-
S is set of 60 means calculated from finding samples of
size 60 from the larger set of data
Click the circle next to the histogram to turn it on or off.
-0.3
histogram ( T,w)
Data Set, Bin Width
BAR HEIGHTS O
BIN ALIGNMENT
Count
Relative Density
Center
Left
-0.2
histogram ( S,w)
Data Set, Bin Width
BAR HEIGHTS O
BIN ALIGNMENT
-0.1-
Count
Relative Density
Center
Left
Open this folder only if doing a classmate response on
the graph.
500
1000
1500
2000
2500
3000
3500

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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