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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. Describes a characteristic of an individual to be measured or observed.
Variable
f(z) - and its cdf by F(z).
The average - or arithmetic mean
A population or statistical population
2. Describes the spread in the values of the sample statistic when many samples are taken.
experimental studies and observational studies.
Variability
covariance of X and Y
the population variance
3. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Ordinal measurements
Likert scale
the population mean
Dependent Selection
4. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A population or statistical population
Simple random sample
Descriptive
5. Another name for elementary event.
Conditional probability
A Probability measure
Independent Selection
Atomic event
6. 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 population cumulants
Step 1 of a statistical experiment
That value is the median value
The variance of a random variable
7. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.
P-value
Divide the sum by the number of values.
Correlation
Statistics
8. 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
A Distribution function
Probability density
Average and arithmetic mean
Marginal probability
9. A numerical facsimilie or representation of a real-world phenomenon.
methods of least squares
Posterior probability
variance of X
Simulation
10. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
A Probability measure
Simulation
the population variance
11. 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
Lurking variable
Sampling frame
Simple random sample
Skewness
12. A list of individuals from which the sample is actually selected.
hypotheses
Sampling frame
Correlation
Inferential statistics
13. 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
That value is the median value
A likelihood function
A probability density function
14. The standard deviation of a sampling distribution.
Statistical inference
Standard error
Step 2 of a statistical experiment
Probability and statistics
15. (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.
Variability
A sampling distribution
An Elementary event
Type I errors
16. Cov[X - Y] :
Statistical dispersion
Conditional distribution
covariance of X and Y
Bias
17. Many statistical methods seek to minimize the mean-squared error - and these are called
the population mean
Step 1 of a statistical experiment
Posterior probability
methods of least squares
18. Have imprecise differences between consecutive values - but have a meaningful order to those values
experimental studies and observational studies.
Ratio measurements
Bias
Ordinal measurements
19. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
Dependent Selection
Lurking variable
the sample or population mean
The sample space
20. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Confounded variables
Mutual independence
Simpson's Paradox
Type II errors
21. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Kurtosis
Alpha value (Level of Significance)
Nominal measurements
Individual
22. ?
the population correlation
Outlier
Divide the sum by the number of values.
Credence
23. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Outlier
Step 1 of a statistical experiment
Probability
A sampling distribution
24. Patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations - and are then used for drawing inferences about the process or population being studied; this is called
An estimate of a parameter
inferential statistics
A probability density function
categorical variables
25. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Atomic event
Placebo effect
An estimate of a parameter
Quantitative variable
26. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are
the population correlation
The Mean of a random variable
Descriptive statistics
Step 1 of a statistical experiment
27. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Greek letters
experimental studies and observational studies.
Marginal distribution
Particular realizations of a random variable
28. Have no meaningful rank order among values.
Correlation coefficient
Kurtosis
An estimate of a parameter
Nominal measurements
29. When you have two or more competing models - choose the simpler of the two models.
Inferential statistics
Law of Parsimony
Simulation
That is the median value
30. Is a sample and the associated data points.
Law of Parsimony
Alpha value (Level of Significance)
A data set
Statistics
31. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Divide the sum by the number of values.
A sampling distribution
Mutual independence
Binomial experiment
32. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
Alpha value (Level of Significance)
Conditional probability
Skewness
33. A subjective estimate of probability.
The variance of a random variable
Credence
Step 3 of a statistical experiment
The standard deviation
34. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Ordinal measurements
The Mean of a random variable
Valid measure
Posterior probability
35. A numerical measure that describes an aspect of a population.
Placebo effect
Sampling
Law of Large Numbers
Parameter
36. To find the average - or arithmetic mean - of a set of numbers:
Alpha value (Level of Significance)
f(z) - and its cdf by F(z).
Probability and statistics
Divide the sum by the number of values.
37. Is a sample space over which a probability measure has been defined.
A sample
A probability space
Coefficient of determination
A probability density function
38. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.
Dependent Selection
Estimator
Parameter - or 'statistical parameter'
A Distribution function
39. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Skewness
Posterior probability
Conditional distribution
Correlation
40. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no
Type I errors
the population correlation
Probability and statistics
Ordinal measurements
41. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
methods of least squares
P-value
The variance of a random variable
Nominal measurements
42. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
the population variance
A statistic
Variability
Power of a test
43. A numerical measure that describes an aspect of a sample.
Likert scale
Pairwise independence
Statistic
Nominal measurements
44. 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.
Particular realizations of a random variable
Conditional probability
A population or statistical population
That value is the median value
45. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.
the population mean
The sample space
The median value
Experimental and observational studies
46. 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
Outlier
Marginal distribution
Statistical inference
Step 2 of a statistical experiment
47. The collection of all possible outcomes in an experiment.
Valid measure
The Expected value
Sample space
The average - or arithmetic mean
48. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Type 1 Error
Statistical adjustment
Step 1 of a statistical experiment
49. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Inferential statistics
inferential statistics
Valid measure
Particular realizations of a random variable
50. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Inferential statistics
Trend
Descriptive
Ordinal measurements
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