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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
Start Test
Study First
Subjects
:
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
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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. Have no meaningful rank order among values.
quantitative variables
Nominal measurements
Null hypothesis
Estimator
2. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
Standard error
Coefficient of determination
Individual
3. Is its expected value. The mean (or sample mean of a data set is just the average value.
Joint probability
The Mean of a random variable
Confounded variables
Step 1 of a statistical experiment
4. Is the length of the smallest interval which contains all the data.
Placebo effect
The Range
Block
A probability distribution
5. Is data that can take only two values - usually represented by 0 and 1.
Interval measurements
Credence
Mutual independence
Binary data
6. A group of individuals sharing some common features that might affect the treatment.
Probability
Mutual independence
Block
A Probability measure
7. A subjective estimate of probability.
Credence
descriptive statistics
f(z) - and its cdf by F(z).
Placebo effect
8. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Statistical dispersion
categorical variables
Atomic event
Credence
9. Describes the spread in the values of the sample statistic when many samples are taken.
Dependent Selection
Variability
Likert scale
Sampling frame
10. Any specific experimental condition applied to the subjects
experimental studies and observational studies.
Treatment
Qualitative variable
Power of a test
11. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
variance of X
A data set
quantitative variables
12. Some commonly used symbols for sample statistics
Type 2 Error
Bias
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Treatment
13. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
applied statistics
An estimate of a parameter
quantitative variables
Outlier
14. Failing to reject a false null hypothesis.
Seasonal effect
Law of Parsimony
Type 2 Error
Particular realizations of a random variable
15. When there is an even number of values...
That is the median value
Standard error
Type I errors & Type II errors
Random variables
16. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Simpson's Paradox
descriptive statistics
the sample or population mean
Correlation
17. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Simpson's Paradox
Probability density functions
methods of least squares
Statistical inference
18. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Parameter - or 'statistical parameter'
Sampling
Standard error
Statistical dispersion
19. 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.
The Range
Simulation
the population cumulants
Statistical inference
20. 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
Null hypothesis
Ordinal measurements
Kurtosis
21. ?r
the population cumulants
Type I errors & Type II errors
Individual
Posterior probability
22. Some commonly used symbols for population parameters
Law of Parsimony
Qualitative variable
the population mean
Outlier
23. A measure that is relevant or appropriate as a representation of that property.
Sample space
the population cumulants
Valid measure
variance of X
24. Long-term upward or downward movement over time.
Trend
The Expected value
Parameter
Marginal distribution
25. Statistical methods can be used for summarizing or describing a collection of data; this is called
Experimental and observational studies
applied statistics
descriptive statistics
the population correlation
26. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
descriptive statistics
Ratio measurements
Reliable measure
27. 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.
Parameter - or 'statistical parameter'
The variance of a random variable
Estimator
The median value
28. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A Random vector
A probability density function
Reliable measure
A statistic
29. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
applied statistics
Null hypothesis
Probability density functions
Conditional distribution
30. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Joint probability
observational study
An estimate of a parameter
Particular realizations of a random variable
31. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
Conditional distribution
Average and arithmetic mean
Type 2 Error
Alpha value (Level of Significance)
32. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.
Dependent Selection
Sampling
A probability space
hypothesis
33. 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
Type 2 Error
Estimator
Mutual independence
A Distribution function
34. Of a group of numbers is the center point of all those number values.
nominal - ordinal - interval - and ratio
The average - or arithmetic mean
Variability
Power of a test
35. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
the sample or population mean
Reliable measure
variance of X
quantitative variables
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
Conditional probability
The Expected value
Probability density functions
Dependent Selection
37. 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.
38. Is a parameter that indexes a family of probability distributions.
Observational study
A Probability measure
Count data
A Statistical parameter
39. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
applied statistics
Posterior probability
methods of least squares
the population correlation
40. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Sampling
A Random vector
Count data
Trend
41. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Sampling Distribution
experimental studies and observational studies.
Observational study
Prior probability
42. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.
Credence
Simple random sample
Independent Selection
Marginal probability
43. E[X] :
A likelihood function
Estimator
nominal - ordinal - interval - and ratio
expected value of X
44. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Valid measure
A sampling distribution
the sample or population mean
Interval measurements
45. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
Sample space
Independent Selection
Coefficient of determination
P-value
46. A numerical measure that assesses the strength of a linear relationship between two variables.
Marginal distribution
Lurking variable
hypotheses
Correlation coefficient
47. Are usually written in upper case roman letters: X - Y - etc.
A likelihood function
expected value of X
Random variables
A Random vector
48. Have imprecise differences between consecutive values - but have a meaningful order to those values
the sample or population mean
The average - or arithmetic mean
Ordinal measurements
Particular realizations of a random 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
Variable
Qualitative variable
50. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Independence or Statistical independence
Residuals
Sampling
Simulation