How do you prove that the normally distributed assumption has been met for a Two Proportion z Test? Choose all assumptions which are appropriate.Normal Sampling Distribution: all n's ≥ 30 or Populations are normally distributedScatterplots are linearModified boxplots have less than two outliersResidual plots are linearModified boxplots have no outliersHistograms are bell shapedNormal quantile plots have no patternNormal Sampling Distribution: n₁p₁ ≥ 5, n₁(1-p₁) ≥ 5, n₂p₂ ≥ 5 & n₂(1-p₂) ≥ 5Histograms are uniformNormal Sampling Distribution: np ≥ 5 & n(1-p) ≥ 5Residual plots have no patternNormal quantile plots are linearScatterplots have no pattern

Name: How do you prove that the normally distributed assumption has been met
Uploaded: 2024-03-20T07:07:12.000Z
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Description: How do you prove that the normally distributed assumption has been met for a Two Proportion z Test? Choose all assumptions which are appropriate.Normal Sampling Distribution: all n's ≥ 30 or Populations are normally distributedScatterplots are linear

The Two Proportion z test can be used to test the population proportion are equal or not.

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

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How do you prove that the normally distributed assumption has been met for a Two Proportion z Test? Choose all assumptions which are appropriate.

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

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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