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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Subjects
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clep
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math
Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
study here
.
Match each statement with the correct term.
Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.
This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. Is that part of a population which is actually observed.
Conditional distribution
Residuals
A sample
Statistical dispersion
2. Have no meaningful rank order among values.
Residuals
A Statistical parameter
observational study
Nominal measurements
3. Failing to reject a false null hypothesis.
Variability
Type 2 Error
Dependent Selection
Joint probability
4. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
Bias
Sampling
s-algebras
5. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Type II errors
A statistic
The variance of a random variable
Statistical adjustment
6. Is denoted by - pronounced 'x bar'.
inferential statistics
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Credence
Random variables
7. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
Skewness
Atomic event
Divide the sum by the number of values.
A data point
8. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
the population cumulants
Confounded variables
Type 2 Error
Step 2 of a statistical experiment
9. Of a group of numbers is the center point of all those number values.
Parameter
The average - or arithmetic mean
Estimator
hypotheses
10. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
Sampling frame
Bias
variance of X
Skewness
11. A subjective estimate of probability.
Credence
A Distribution function
Independence or Statistical independence
That is the median value
12. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o
Qualitative variable
the population variance
covariance of X and Y
Observational study
13. ?r
The median value
the population cumulants
Seasonal effect
covariance of X and Y
14. Samples are drawn from two different populations such that there is a matching of the first sample data drawn and a corresponding data value in the second sample data.
That value is the median value
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Dependent Selection
Lurking variable
15. A measurement such that the random error is small
Divide the sum by the number of values.
Average and arithmetic mean
Residuals
Reliable measure
16. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Law of Parsimony
Particular realizations of a random variable
Correlation
17. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
That is the median value
A statistic
Joint distribution
Step 2 of a statistical experiment
18. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
Estimator
the sample or population mean
A probability space
Variable
19. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then
A Probability measure
Sampling Distribution
Independence or Statistical independence
A likelihood function
20. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
An Elementary event
f(z) - and its cdf by F(z).
the population correlation
Standard error
21. Many statistical methods seek to minimize the mean-squared error - and these are called
Qualitative variable
methods of least squares
Average and arithmetic mean
Variability
22. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Interval measurements
An event
A Random vector
A Distribution function
23. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl
Mutual independence
Outlier
categorical variables
Type 1 Error
24. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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25. A measure that is relevant or appropriate as a representation of that property.
Binomial experiment
Lurking variable
Valid measure
Type I errors
26. The standard deviation of a sampling distribution.
Treatment
Sampling Distribution
Parameter - or 'statistical parameter'
Standard error
27. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
That value is the median value
Correlation
Statistical dispersion
Marginal probability
28. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Seasonal effect
inferential statistics
nominal - ordinal - interval - and ratio
Experimental and observational studies
29. ?
the population correlation
Sample space
Quantitative variable
applied statistics
30. Is data arising from counting that can take only non-negative integer values.
Simulation
Count data
P-value
An experimental study
31. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.
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32. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
Outlier
observational study
Lurking variable
33. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Joint distribution
Statistical adjustment
Type 1 Error
The median value
34. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that
hypothesis
Inferential statistics
A likelihood function
A probability density function
35. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Variable
Binary data
the population mean
Particular realizations of a random variable
36. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re
The Expected value
The Mean of a random variable
the population cumulants
the population correlation
37. Rejecting a true null hypothesis.
Greek letters
A sample
Step 3 of a statistical experiment
Type 1 Error
38. The probability of correctly detecting a false null hypothesis.
Power of a test
A Statistical parameter
applied statistics
Joint distribution
39. Some commonly used symbols for sample statistics
Credence
Coefficient of determination
hypothesis
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
40. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.
Kurtosis
Joint distribution
P-value
A statistic
41. Describes the spread in the values of the sample statistic when many samples are taken.
Probability density functions
Sampling
Variability
Descriptive
42. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.
Conditional probability
s-algebras
The average - or arithmetic mean
Independent Selection
43. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
Sampling
The average - or arithmetic mean
A statistic
44. Is the probability distribution - under repeated sampling of the population - of a given statistic.
hypothesis
observational study
A sampling distribution
Kurtosis
45. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Descriptive statistics
Law of Large Numbers
Ratio measurements
The Mean of a random variable
46. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Bias
A Probability measure
Type I errors & Type II errors
Interval measurements
47. Two events are independent if the outcome of one does not affect that of the other (for example - getting a 1 on one die roll does not affect the probability of getting a 1 on a second roll). Similarly - when we assert that two random variables are i
Bias
Independence or Statistical independence
inferential statistics
Prior probability
48. Describes a characteristic of an individual to be measured or observed.
the population correlation
Inferential statistics
Joint distribution
Variable
49. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
A population or statistical population
Type 1 Error
An experimental study
50. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Block
Descriptive
A likelihood function
applied statistics