- 4.1.1E: What is a probability experiment?
- 4.1.2E: Define sample space.
- 4.1.3E: What is the difference between an outcome and an event?
- 4.1.4E: What are equally likely events?
- 4.1.5E: What is the range of the values of the probability of an event?
- 4.1.6E: When an event is certain to occur, what is its probability?
- 4.1.7E: If an event cannot happen, what value is assigned to its probability?
- 4.1.8E: What is the sum of the probabilities of all the outcomes in a sampl...
- 4.1.9E: If the probability that it will rain tomorrow is 0.20, what is the ...
- 4.1.10E: ?A probability experiment is conducted. Which of these cannot be co...
- 4.1.19E: ?Shopping Mall Promotion A shopping mall has set up a promotion as ...
- 4.1.20E: Selecting a State Choose one of the 50 states at random.a. What is ...
- 4.1.21E: Human Blood Types Human blood is grouped into four types. The perce...
- 4.1.23E: Prime Numbers A prime number is a number that is evenly divisible o...
- 4.1.24E: Rural Speed Limits Rural speed limits for all 50 states are indicat...
- 4.1.25E: Gender of Children A couple has three children. Find each probabili...
- 4.1.27E: Craps Game In a game of craps, a player loses on the roll if a 2, 3...
- 4.1.28E: ?Computers in Elementary Schools Elementary and secondary schools w...
- 4.1.29E: College Debt The following information shows the amount of debt stu...
- 4.1.34E: Federal Government Revenue The source of federal government revenue...
- 4.1.35E: Selecting a Bill A box contains a $1 bill, a $5 bill, a $10 bill, a...
- 4.1.36E: Tossing Coins Draw a tree diagram and determine the sample space fo...
- 4.1.37E: Selecting Numbered Balls Four balls numbered 1 through 4 are placed...
- 4.1.38E: Family Dinner Combinations A family special at a neighborhood resta...
- 4.1.39E: Required First-Year College Courses First-year students at a partic...
- 4.1.40E: Tossing a Coin and Rolling a Die A coin is tossed; if it falls head...
- 4.1.41EC: Distribution of CEO Ages The distribution of ages of CEOs is as fol...
- 4.1.42EC: Tossing a Coin A person flipped a coin 100 times and obtained 73 he...
- 4.1.43EC: Medical Treatment A medical doctor stated that with a certain treat...
- 4.1.44EC: ?Wheel Spinner The wheel spinner shown here is spun twice. Find the...
- 4.1.45EC: Tossing Coins Toss three coins 128 times and record the number of h...
- 4.1.46EC: Tossing Coins Toss two coins 100 times and record the number of hea...
- 4.1.47EC: ?Odds Odds are used in gambling games to make them fair. For exampl...
Solutions for Chapter 4.1: Sample Spaces and Probability
Full solutions for Elementary Statistics: A Step By Step Approach | 9th Edition
ISBN: 9780073534985
Summary of Chapter 4.1: Sample Spaces and Probability
A sample space is the set of all possible outcomes of a probability experiment.
This textbook survival guide was created for the textbook: Elementary Statistics: A Step By Step Approach , edition: 9. Elementary Statistics: A Step By Step Approach was written by and is associated to the ISBN: 9780073534985. Chapter 4.1: Sample Spaces and Probability includes 33 full step-by-step solutions. This expansive textbook survival guide covers the following chapters and their solutions. Since 33 problems in chapter 4.1: Sample Spaces and Probability have been answered, more than 666780 students have viewed full step-by-step solutions from this chapter.
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`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).
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Analytic study
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study
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Average
See Arithmetic mean.
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Bernoulli trials
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
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Bivariate distribution
The joint probability distribution of two random variables.
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Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
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Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.
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Chance cause
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.
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Conditional mean
The mean of the conditional probability distribution of a random variable.
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Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
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Conidence level
Another term for the conidence coeficient.
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Counting techniques
Formulas used to determine the number of elements in sample spaces and events.
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Critical region
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.
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Defects-per-unit control chart
See U chart
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Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).
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False alarm
A signal from a control chart when no assignable causes are present
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Fraction defective
In statistical quality control, that portion of a number of units or the output of a process that is defective.
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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
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Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .
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Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.