the correct answer. Answer it all. 7. ______________ states that the samples will be normally distributed and the sample mean will be approximately equal to the population mean as the sample size gets bigger. a. Hypothesis b. Central Limit Theorem c. T-test d. Parameter 8. Observation that shows no significant difference between the two parameters. A. Alternative Hypothesis B. Type I Error c. Null Hypothesis d. Type II Error

the correct answer. Answer it all. 7. ______________ states that the samples will be normally distributed and the sample mean will be approximately equal to the population mean as the sample size gets bigger. a. Hypothesis b. Central Limit Theorem c. T-test d. Parameter 8. Observation that shows no significant difference between the two parameters. A. Alternative Hypothesis B. Type I Error c. Null Hypothesis d. Type II Error

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.3: Measures Of Spread

Problem 16HP

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Question

Choose the correct answer. Answer it all.

7. ______________ states that the samples will be normally distributed and the sample mean will be approximately equal to the population mean as the sample size gets bigger.

a. Hypothesis

b. Central Limit Theorem

c. T-test

d. Parameter

8. Observation that shows no significant difference between the two parameters.

A. Alternative Hypothesis

B. Type I Error

c. Null Hypothesis

d. Type II Error

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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