Exam 8: Continuous Probability Distributions
Exam 1: What Is Statistics14 Questions
Exam 2: Types of Data, Data Collection and Sampling16 Questions
Exam 3: Graphical Descriptive Methods Nominal Data19 Questions
Exam 4: Graphical Descriptive Techniques Numerical Data64 Questions
Exam 5: Numerical Descriptive Measures147 Questions
Exam 6: Probability106 Questions
Exam 7: Random Variables and Discrete Probability Distributions55 Questions
Exam 8: Continuous Probability Distributions117 Questions
Exam 9: Statistical Inference: Introduction8 Questions
Exam 10: Sampling Distributions65 Questions
Exam 11: Estimation: Describing a Single Population127 Questions
Exam 12: Estimation: Comparing Two Populations22 Questions
Exam 13: Hypothesis Testing: Describing a Single Population129 Questions
Exam 14: Hypothesis Testing: Comparing Two Populations78 Questions
Exam 15: Inference About Population Variances49 Questions
Exam 16: Analysis of Variance115 Questions
Exam 17: Additional Tests for Nominal Data: Chi-Squared Tests110 Questions
Exam 18: Simple Linear Regression and Correlation213 Questions
Exam 19: Multiple Regression121 Questions
Exam 20: Model Building92 Questions
Exam 21: Nonparametric Techniques126 Questions
Exam 22: Statistical Inference: Conclusion103 Questions
Exam 23: Time-Series Analysis and Forecasting145 Questions
Exam 24: Index Numbers25 Questions
Exam 25: Decision Analysis51 Questions
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If the random variable X is uniformly distributed over the interval 10 x 50, find the following probabilities.
a. P(X 30).
b. P(X 25).
c. P(18 X 35).
d. P(X = 40).
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Given that X is a binomial random variable, the binomial probability P(X x) is approximated by the area under a normal curve to the right of:
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The values of zA are the 100(1 - A)th percentiles of a standard normal random variable.
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Given that Z is a standard normal random variable, the area to the left of a value z is expressed as:
(Multiple Choice)
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Consider a binomial random variable X with n = 300 and p = 0.02. Approximate the values of the following probabilities.
a. P(X = 4).
b. P(X 5).
c. P(X 8).
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If the random variable X is exponentially distributed with = 2 parameter, then the variance of the distribution is 0.5.
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A random variable X is normally distributed with a mean of 250 and a standard deviation of 50. Given that X = 175, its corresponding z-score is -1.50.
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Using the standard normal curve, the z-score representing the 90th percentile is 1.28.
(True/False)
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If the z-value for a given value x of the random variable X is z = 2.326, and the distribution of X is normal with a mean of 50 and a standard deviation of 5, to what x-value does this z-value correspond?
(Multiple Choice)
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Using the standard normal curve, the area between z = 0 and z = 3.50 is about 0.50.
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Continuous probability distributions describe probabilities associated with random variables that are able to assume any of an infinite number of values.
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A continuous random variable X has the probability density function f(x) = 2 , x 0.
a. Find the mean and standard deviation of X.
b. What is the probability that X is between 1 and 3?
c. What is the probability that X is at most 2?
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Which of the following is a characteristic of a normal distribution?
(Multiple Choice)
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The probability density function, f(x), for any continuous random variable X, represents:
(Multiple Choice)
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Let z1 be a z-score that is unknown but identifiable by position and area. If the symmetrical area between -z1 and + z1 is 0.9544, the value of z1 is 2.0.
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A continuous probability distribution represents a random variable having an infinite number of outcomes that may assume any number of values within an interval.
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The mean and standard deviation of an exponential random variable cannot equal to each other.
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Given that X is a binomial random variable, the binomial probability P(X x) is approximated by the area under a normal curve to the left of:
(Multiple Choice)
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A standard normal distribution is a normal distribution with:
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Which of the following is always true for all probability density functions of continuous random variables?
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