How do you prove that the normal sampling distribution assumption has been met for a One Sample z Test or a One Sample t Test? (Choose all answers which are correct.)Normal quantile plot has no patternModified boxplot has at least two outliersScatterplot is linearScatterplot has no patternHistogram is symmetricModified boxplot has less than two outliersNormal Sampling Distribution: n₁p₁ ≥ 5, n₁(1 - p₁) ≥ 5 n₂p₂ ≥ 5 & n₂(1 - p₂) ≥ 5Normal Sampling Distribution: n ≥ 30 or Population is normally distributedResiduals plot has no patternNormal quantile plot is linearNormal Sampling Distribution: np ≥ 5 & n(1 - p) ≥ 5Residuals plot is linearHistogram is skewed

Name: How do you prove that the normal sampling distribution assumption has
Uploaded: 2024-03-20T07:07:12.000Z
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Description: How do you prove that the normal sampling distribution assumption has been met for a One Sample z Test or a One Sample t Test? (Choose all answers which are correct.)Normal quantile plot has no patternModified boxplot has at least two outliersScatter

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Holt Mcdougal Larson Pre-algebra: Student Edition 2012

1st Edition

ISBN:9780547587776

Author:HOLT MCDOUGAL

Publisher:HOLT MCDOUGAL

Chapter11: Data Analysis And Probability

Section11.4: Collecting Data

Problem 2E

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How do you prove that the normal sampling distribution assumption has been met for a One Sample z Test or a One Sample t Test? (Choose all answers which are correct.)

Normal quantile plot has no pattern
Modified boxplot has at least two outliers
Scatterplot is linear
Scatterplot has no pattern
Histogram is symmetric
Modified boxplot has less than two outliers
Normal Sampling Distribution: n₁p₁ ≥ 5, n₁(1 - p₁) ≥ 5 n₂p₂ ≥ 5 & n₂(1 - p₂) ≥ 5
Normal Sampling Distribution: n ≥ 30 or Population is normally distributed
Residuals plot has no pattern
Normal quantile plot is linear
Normal Sampling Distribution: np ≥ 5 & n(1 - p) ≥ 5
Residuals plot is linear
Histogram is skewed

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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