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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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clep
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math
Instructions:
Answer 50 questions in 15 minutes.
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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 data arising from counting that can take only non-negative integer values.
That value is the median value
Statistical dispersion
Marginal probability
Count data
2. To find the median value of a set of numbers: Arrange the numbers in numerical order. Locate the two middle numbers in the list. Find the average of those two middle values.
Statistical adjustment
That value is the median value
Credence
Independent Selection
3. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Probability density
Experimental and observational studies
Step 1 of a statistical experiment
Greek letters
4. Of a group of numbers is the center point of all those number values.
experimental studies and observational studies.
The average - or arithmetic mean
Sampling Distribution
Binary data
5. A subjective estimate of probability.
A data point
Block
Conditional probability
Credence
6. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Type 2 Error
Seasonal effect
applied statistics
expected value of X
7. A numerical measure that assesses the strength of a linear relationship between two variables.
Observational study
hypotheses
s-algebras
Correlation coefficient
8. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Ratio measurements
Statistical adjustment
Alpha value (Level of Significance)
Seasonal effect
9. Gives the probability of events in a probability space.
A Probability measure
f(z) - and its cdf by F(z).
quantitative variables
An estimate of a parameter
10. Describes the spread in the values of the sample statistic when many samples are taken.
Law of Large Numbers
Bias
The Range
Variability
11. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
The Covariance between two random variables X and Y - with expected values E(X) =
Statistics
the population correlation
s-algebras
12. The collection of all possible outcomes in an experiment.
Sample space
Divide the sum by the number of values.
The variance of a random variable
s-algebras
13. A data value that falls outside the overall pattern of the graph.
the population cumulants
Independence or Statistical independence
Outlier
Type I errors & Type II errors
14. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
The Covariance between two random variables X and Y - with expected values E(X) =
descriptive statistics
Joint distribution
15. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
Step 1 of a statistical experiment
The sample space
the population variance
hypothesis
16. Probability of accepting a false null hypothesis.
Statistic
An Elementary event
Beta value
The Mean of a random variable
17. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).
Valid measure
Joint probability
An event
Step 1 of a statistical experiment
18. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'
Credence
Conditional probability
Trend
Sample space
19. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Parameter - or 'statistical parameter'
Sampling Distribution
Step 3 of a statistical experiment
20. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Lurking variable
Residuals
Law of Parsimony
A statistic
21. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
Bias
The variance of a random variable
Sampling
the population correlation
22. A measurement such that the random error is small
methods of least squares
Step 1 of a statistical experiment
The variance of a random variable
Reliable measure
23. 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.
Joint distribution
Kurtosis
Power of a test
Variability
24. Describes a characteristic of an individual to be measured or observed.
Valid measure
Variability
Variable
Descriptive statistics
25. Long-term upward or downward movement over time.
Trend
A data point
Alpha value (Level of Significance)
Law of Parsimony
26. Gives the probability distribution for a continuous random variable.
A probability density function
A probability distribution
Sampling
Coefficient of determination
27. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies
Bias
That value is the median value
experimental studies and observational studies.
Joint probability
28. ?
Parameter - or 'statistical parameter'
Atomic event
the population correlation
Type 1 Error
29. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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30. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.
Statistical inference
Treatment
Reliable measure
Descriptive
31. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
Simpson's Paradox
The sample space
f(z) - and its cdf by F(z).
Simple random sample
32. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit
The Covariance between two random variables X and Y - with expected values E(X) =
Credence
Probability density
Joint probability
33. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
the population variance
Correlation coefficient
Marginal probability
Binomial experiment
34. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Posterior probability
Individual
observational study
categorical variables
35. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Atomic event
The variance of a random variable
Joint probability
36. 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)
Interval measurements
Parameter
Marginal distribution
Seasonal effect
37. Have no meaningful rank order among values.
Nominal measurements
quantitative variables
the population variance
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
38. Is the length of the smallest interval which contains all the data.
Probability density functions
Sampling frame
Simulation
The Range
39. Is a sample space over which a probability measure has been defined.
Statistical inference
Individual
A probability space
categorical variables
40. A variable describes an individual by placing the individual into a category or a group.
the population cumulants
Step 3 of a statistical experiment
Power of a test
Qualitative variable
41. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Conditional distribution
A likelihood function
A Random vector
Beta value
42. The standard deviation of a sampling distribution.
methods of least squares
Standard error
That is the median value
Statistics
43. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Parameter
the population variance
Independence or Statistical independence
44. Is a parameter that indexes a family of probability distributions.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
observational study
A Statistical parameter
A data set
45. Many statistical methods seek to minimize the mean-squared error - and these are called
A likelihood function
Correlation coefficient
methods of least squares
Type I errors
46. Is the probability distribution - under repeated sampling of the population - of a given statistic.
hypotheses
Individual
A sampling distribution
Quantitative variable
47. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
Random variables
Estimator
Ratio measurements
P-value
48. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
An Elementary event
Parameter
Likert scale
Binary data
49. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a
Sampling frame
Step 2 of a statistical experiment
the population variance
Null hypothesis
50. 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.
The Range
A likelihood function
An estimate of a parameter
Estimator
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