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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Study First
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
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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. 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
experimental studies and observational studies.
Conditional distribution
Skewness
Bias
2. Gives the probability of events in a probability space.
A Probability measure
Bias
Correlation
The Range
3. A group of individuals sharing some common features that might affect the treatment.
Independence or Statistical independence
Block
the population variance
Parameter
4. A numerical facsimilie or representation of a real-world phenomenon.
A Distribution function
inferential statistics
A probability space
Simulation
5. 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
Sampling frame
Correlation
Type 2 Error
An experimental study
6. Describes the spread in the values of the sample statistic when many samples are taken.
Prior probability
Variability
Binomial experiment
A statistic
7. 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
Confounded variables
Outlier
hypothesis
inferential statistics
8. Is defined as the expected value of random variable (X -
Observational study
Binomial experiment
categorical variables
The Covariance between two random variables X and Y - with expected values E(X) =
9. Have imprecise differences between consecutive values - but have a meaningful order to those values
the population cumulants
Power of a test
Ordinal measurements
Placebo effect
10. 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
Parameter - or 'statistical parameter'
Mutual independence
Step 2 of a statistical experiment
Statistic
11. (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
Kurtosis
The Expected value
the population variance
Correlation coefficient
12. Are simply two different terms for the same thing. Add the given values
An event
Variability
Average and arithmetic mean
Particular realizations of a random variable
13. 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.
Sampling frame
Kurtosis
Confounded variables
The average - or arithmetic mean
14. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
Nominal measurements
Observational study
the sample or population mean
15. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Nominal measurements
experimental studies and observational studies.
hypotheses
applied statistics
16. A list of individuals from which the sample is actually selected.
Sampling frame
Reliable measure
Alpha value (Level of Significance)
Step 3 of a statistical experiment
17. 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}.
The sample space
Random variables
A statistic
Correlation coefficient
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'
Conditional probability
Alpha value (Level of Significance)
Sample space
Likert scale
19. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.
A probability density function
Probability and statistics
An experimental study
Type 2 Error
20. 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
observational study
Probability density
Probability
Inferential statistics
21. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.
Marginal probability
Kurtosis
Binary data
Probability and statistics
22. Many statistical methods seek to minimize the mean-squared error - and these are called
Interval measurements
methods of least squares
hypotheses
Beta value
23. Is the length of the smallest interval which contains all the data.
The Range
Residuals
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
inferential statistics
24. E[X] :
Binary data
f(z) - and its cdf by F(z).
A random variable
expected value of X
25. Probability of accepting a false null hypothesis.
Null hypothesis
That value is the median value
Beta value
A data point
26. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
A likelihood function
Probability density functions
Atomic event
Trend
27. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Marginal distribution
Inferential
Independent Selection
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
28. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
A sampling distribution
The median value
The Covariance between two random variables X and Y - with expected values E(X) =
Bias
29. Statistical methods can be used for summarizing or describing a collection of data; this is called
Skewness
descriptive statistics
f(z) - and its cdf by F(z).
Placebo effect
30. 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.
descriptive statistics
Statistic
Skewness
Dependent Selection
31. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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32. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
Type I errors & Type II errors
the population correlation
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
33. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Type 1 Error
Law of Large Numbers
Prior probability
A Random vector
34. 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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35. Another name for elementary event.
Marginal probability
Prior probability
Block
Atomic event
36. Gives the probability distribution for a continuous random variable.
Sampling frame
Binomial experiment
Likert scale
A probability density function
37. Probability of rejecting a true null hypothesis.
Probability density functions
Alpha value (Level of Significance)
An Elementary event
The Mean of a random variable
38. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Statistics
Binomial experiment
Greek letters
That is the median value
39. Rejecting a true null hypothesis.
observational study
Descriptive statistics
Random variables
Type 1 Error
40. A data value that falls outside the overall pattern of the graph.
Reliable measure
covariance of X and Y
the population mean
Outlier
41. A numerical measure that describes an aspect of a population.
Alpha value (Level of Significance)
Treatment
expected value of X
Parameter
42. 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
Observational study
The Range
Simpson's Paradox
A likelihood function
43. Long-term upward or downward movement over time.
A sample
Nominal measurements
Trend
The Expected value
44.
Sampling frame
the population mean
Kurtosis
P-value
45. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Bias
Pairwise independence
An experimental study
Dependent Selection
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
the population mean
Statistical adjustment
Type II errors
Step 2 of a statistical experiment
47. 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.
Count data
The Range
A sampling distribution
A data point
48. The probability of correctly detecting a false null hypothesis.
Power of a test
Greek letters
Count data
Probability density
49. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
A likelihood function
P-value
Placebo effect
A data set
50. Is that part of a population which is actually observed.
Inferential
A sample
Law of Parsimony
Average and arithmetic mean