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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
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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. Are simply two different terms for the same thing. Add the given values
observational study
Law of Parsimony
P-value
Average and arithmetic mean
2. A data value that falls outside the overall pattern of the graph.
The standard deviation
Outlier
Placebo effect
Independence or Statistical independence
3. 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.
Marginal probability
quantitative variables
Nominal measurements
A Distribution function
4. 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.
Parameter
An event
Statistical inference
s-algebras
5. A numerical measure that assesses the strength of a linear relationship between two variables.
Standard error
Correlation coefficient
the population mean
A probability distribution
6. 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
Quantitative variable
Step 2 of a statistical experiment
An event
7. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Marginal probability
A probability space
P-value
Sampling Distribution
8. Is data arising from counting that can take only non-negative integer values.
That value is the median value
Count data
Skewness
That is the median value
9. E[X] :
Step 1 of a statistical experiment
Bias
expected value of X
Atomic event
10. 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
Step 2 of a statistical experiment
observational study
the population mean
Law of Parsimony
11. Is a subset of the sample space - to which a probability can be assigned. For example - on rolling a die - 'getting a five or a six' is an event (with a probability of one third if the die is fair).
the sample or population mean
An event
A sample
variance of X
12. Some commonly used symbols for sample statistics
The standard deviation
Statistical inference
A Statistical parameter
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
13. 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.
Independence or Statistical independence
Joint distribution
That value is the median value
Random variables
14. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Null hypothesis
Statistical dispersion
inferential statistics
15. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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16. Is that part of a population which is actually observed.
Parameter - or 'statistical parameter'
Experimental and observational studies
A sample
categorical variables
17. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present
Step 3 of a statistical experiment
Step 1 of a statistical experiment
Simpson's Paradox
Independence or Statistical independence
18.
the population mean
Statistical inference
The Mean of a random variable
Type II errors
19. 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.
Ordinal measurements
A data point
Descriptive
Step 1 of a statistical experiment
20. 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.
hypotheses
Independence or Statistical independence
Conditional distribution
A Statistical parameter
21. Cov[X - Y] :
covariance of X and Y
Treatment
A data point
Bias
22. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
Descriptive statistics
Observational study
Step 3 of a statistical experiment
23. A variable describes an individual by placing the individual into a category or a group.
Estimator
Qualitative variable
P-value
Experimental and observational studies
24. 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
Simple random sample
Particular realizations of a random variable
Independence or Statistical independence
Standard error
25. A list of individuals from which the sample is actually selected.
An estimate of a parameter
the population correlation
f(z) - and its cdf by F(z).
Sampling frame
26. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
the population variance
Pairwise independence
Prior probability
Type II errors
27. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
Quantitative variable
Law of Large Numbers
Parameter - or 'statistical parameter'
28. To find the average - or arithmetic mean - of a set of numbers:
Marginal probability
The variance of a random variable
Divide the sum by the number of values.
Placebo effect
29. Data are gathered and correlations between predictors and response are investigated.
Marginal probability
Conditional probability
observational study
Standard error
30. 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
A data point
Prior probability
Trend
inferential statistics
31. Describes a characteristic of an individual to be measured or observed.
Bias
Variable
A likelihood function
Marginal distribution
32. Is its expected value. The mean (or sample mean of a data set is just the average value.
Probability density
The Mean of a random variable
Probability density functions
Power of a test
33. 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.
Individual
f(z) - and its cdf by F(z).
A population or statistical population
Reliable measure
34. Is defined as the expected value of random variable (X -
Power of a test
The Covariance between two random variables X and Y - with expected values E(X) =
Inferential
the population mean
35. S^2
Parameter
the population variance
Variability
Independence or Statistical independence
36. When there is an even number of values...
Simpson's Paradox
Sampling Distribution
That is the median value
Parameter
37. 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
Lurking variable
experimental studies and observational studies.
the population variance
variance of X
38. Any specific experimental condition applied to the subjects
the population correlation
Treatment
Greek letters
A Distribution function
39. Rejecting a true null hypothesis.
Posterior probability
Variability
Bias
Type 1 Error
40. 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.
Experimental and observational studies
Estimator
f(z) - and its cdf by F(z).
An event
41. 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 1 Error
Mutual independence
A sample
variance of X
42. Probability of accepting a false null hypothesis.
the population variance
Reliable measure
Parameter
Beta value
43. The proportion of the explained variation by a linear regression model in the total variation.
Coefficient of determination
Skewness
quantitative variables
A data set
44. Var[X] :
The median value
the population mean
variance of X
Step 3 of a statistical experiment
45. 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).
Trend
Joint probability
Greek letters
Estimator
46. Have imprecise differences between consecutive values - but have a meaningful order to those values
Independent Selection
Statistical inference
Ordinal measurements
Greek letters
47. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
Parameter - or 'statistical parameter'
The Range
Ratio measurements
48. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Sampling Distribution
Descriptive
Alpha value (Level of Significance)
Parameter - or 'statistical parameter'
49. Are usually written in upper case roman letters: X - Y - etc.
Count data
Valid measure
Random variables
Prior probability
50. 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
A Probability measure
A likelihood function
Seasonal effect
Descriptive statistics