Solutions for Chapter 7-1: Confidence Intervals for the Mean When s Is Known

Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition | ISBN: 9780073386102 | Authors: Allan G Bluman Professor Emeritus

Full solutions for Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition

ISBN: 9780073386102

Elementary Statistics: A Step by Step Approach 8th ed. | 8th Edition | ISBN: 9780073386102 | Authors: Allan G Bluman Professor Emeritus

Solutions for Chapter 7-1: Confidence Intervals for the Mean When s Is Known

Solutions for Chapter 7-1
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Textbook: Elementary Statistics: A Step by Step Approach 8th ed.
Edition: 8
Author: Allan G Bluman Professor Emeritus
ISBN: 9780073386102

This textbook survival guide was created for the textbook: Elementary Statistics: A Step by Step Approach 8th ed., edition: 8. Since 26 problems in chapter 7-1: Confidence Intervals for the Mean When s Is Known have been answered, more than 10289 students have viewed full step-by-step solutions from this chapter. Chapter 7-1: Confidence Intervals for the Mean When s Is Known includes 26 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. Elementary Statistics: A Step by Step Approach 8th ed. was written by Patricia and is associated to the ISBN: 9780073386102.

Key Statistics Terms and definitions covered in this textbook
  • All possible (subsets) regressions

    A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

  • Arithmetic mean

    The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

  • Assignable cause

    The portion of the variability in a set of observations that can be traced to speciic causes, such as operators, materials, or equipment. Also called a special cause.

  • Attribute

    A qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.

  • Backward elimination

    A method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain

  • Chi-square (or chi-squared) random variable

    A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

  • Chi-square test

    Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

  • Comparative experiment

    An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

  • Covariance

    A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .

  • Decision interval

    A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.

  • Defect

    Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.

  • Defect concentration diagram

    A quality tool that graphically shows the location of defects on a part or in a process.

  • Discrete uniform random variable

    A discrete random variable with a inite range and constant probability mass function.

  • Distribution function

    Another name for a cumulative distribution function.

  • Eficiency

    A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.

  • First-order model

    A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model

  • Forward selection

    A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.

  • Gamma function

    A function used in the probability density function of a gamma random variable that can be considered to extend factorials

  • Gaussian distribution

    Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

  • Harmonic mean

    The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .

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