How do you prove that the normally distributed assumption has been met for a Two Independent Samples t Test?Residual plots have no patternsModified boxplots each have less than two outliersScatterplots are linearNormal Sampling Distribution: np ≥ 5 & n(1-p) ≥ 5Histograms are skewedNormal Sampling Distribution: n's ≥ 30 or Populations are normally distributedNormal quantile plots are linearModified boxplots have no outliersHistograms are symmetricNormal quantile plots have no patternsNormal Sampling Distribution: n₁p₁ ≥ 5, n₁(1-p₁) ≥ 5 n₂p₂ ≥ 5 & n₂(1-p₂) ≥ 5Scatterplots have no patternsResidual plots are linear

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Answered: How do you prove that the normally…

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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How do you prove that the normally distributed assumption has been met for a Two Independent Samples t Test?

Residual plots have no patterns
Modified boxplots each have less than two outliers
Scatterplots are linear
Normal Sampling Distribution: np ≥ 5 & n(1-p) ≥ 5
Histograms are skewed
Normal Sampling Distribution: n's ≥ 30 or Populations are normally distributed
Normal quantile plots are linear
Modified boxplots have no outliers
Histograms are symmetric
Normal quantile plots have no patterns
Normal Sampling Distribution: n₁p₁ ≥ 5, n₁(1-p₁) ≥ 5 n₂p₂ ≥ 5 & n₂(1-p₂) ≥ 5
Scatterplots have no patterns
Residual plots are linear

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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