An experiment was conducted to determine whether giving candy to dining parties resulted in greater tips. The mean tip percentages and standard deviations are given in the accompanying table along with the sample sizes. Assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. Complete parts (a). μ n ˜x s No candy μ1 38 19.02 1.38 Two candies μ2 38 21.56 2.37 Construct the confidence interval suitable for testing the claim in part a ?< μ1 - μ2

An experiment was conducted to determine whether giving candy to dining parties resulted in greater tips. The mean tip percentages and standard deviations are given in the accompanying table along with the sample sizes. Assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. Complete parts (a). μ n ˜x s No candy μ1 38 19.02 1.38 Two candies μ2 38 21.56 2.37 Construct the confidence interval suitable for testing the claim in part a ?< μ1 - μ2

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.4: Distributions Of Data

Problem 19PFA

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Question

μ n ˜x s
No candy μ1 38 19.02 1.38
Two candies μ2 38 21.56 2.37

Construct the confidence interval suitable for testing the claim in part a

?< μ1 - μ2<?

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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