Given the following information:My confidence intervals: 90% confidence interval = (2.33, 2.56), and 99% confidence interval = (2.26, 2.63)My peer's confidence intervals: 90% confidence interval at ( 2.42 , 2.66 ) and 99% confidence interval at ( 2.36 , 2.72).How do their confidence intervals compare with yours?If the population standard deviation is unknown and the sample size is not sufficiently large, would you still use the Normal distribution to calculate these confidence intervals, or would you choose another distribution? If the latter, which distribution would you choose?

1. The obtained confidence intervals are: 90% confidence interval = (2.33, 2.56), and 99% confidence…

Answered: Given the following information: My…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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Given the following information:

My confidence intervals: 90% confidence interval = (2.33, 2.56), and 99% confidence interval = (2.26, 2.63)

My peer's confidence intervals: 90% confidence interval at ( 2.42 , 2.66 ) and 99% confidence interval at ( 2.36 , 2.72).

How do their confidence intervals compare with yours?
If the population standard deviation is unknown and the sample size is not sufficiently large, would you still use the Normal distribution to calculate these confidence intervals, or would you choose another distribution? If the latter, which distribution would you choose?

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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