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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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clep
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Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
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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 the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.
Residuals
Type 1 Error
Marginal probability
An Elementary event
2. 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
covariance of X and Y
Binary data
A Probability measure
inferential statistics
3. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
nominal - ordinal - interval - and ratio
Prior probability
Correlation coefficient
4. Gives the probability of events in a probability space.
A Probability measure
The standard deviation
the population mean
Step 2 of a statistical experiment
5. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Distribution function
A Random vector
observational study
Skewness
6. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to
hypotheses
Seasonal effect
Statistics
Bias
7. Is that part of a population which is actually observed.
A sample
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
An Elementary event
Power of a test
8. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Lurking variable
Quantitative variable
Residuals
Independence or Statistical independence
9. 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.
A sampling distribution
Simple random sample
Prior probability
Descriptive
10. Is data arising from counting that can take only non-negative integer values.
Treatment
Count data
A Probability measure
Type 1 Error
11. Probability of rejecting a true null hypothesis.
The standard deviation
Alpha value (Level of Significance)
Reliable measure
Dependent Selection
12. Failing to reject a false null hypothesis.
Type 2 Error
Correlation
Variable
An estimate of a parameter
13. Is a sample space over which a probability measure has been defined.
Joint probability
Block
Likert scale
A probability space
14. 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
Reliable measure
Independence or Statistical independence
An event
Nominal measurements
15. Two variables such that their effects on the response variable cannot be distinguished from each other.
Statistic
Simpson's Paradox
A probability distribution
Confounded variables
16. A subjective estimate of probability.
Residuals
Credence
Type I errors
A likelihood function
17. 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
Probability and statistics
A Probability measure
Statistic
Probability
18. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Marginal distribution
Binomial experiment
s-algebras
Nominal measurements
19. The collection of all possible outcomes in an experiment.
Block
descriptive statistics
Ratio measurements
Sample space
20. A numerical measure that describes an aspect of a population.
Parameter
A sampling distribution
Statistic
Conditional distribution
21. 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.
Variable
Joint distribution
Kurtosis
Observational study
22. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Sampling frame
Prior probability
Greek letters
Qualitative variable
23. A data value that falls outside the overall pattern of the graph.
Probability
Qualitative variable
A probability density function
Outlier
24. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
variance of X
expected value of X
An experimental study
25. (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.
Seasonal effect
Standard error
An Elementary event
Bias
26. Gives the probability distribution for a continuous random variable.
experimental studies and observational studies.
Qualitative variable
A probability density function
A Probability measure
27. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
A Statistical parameter
A data point
The median value
28. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
An event
Statistical inference
Sampling frame
Particular realizations of a random variable
29. 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
Descriptive statistics
A Random vector
Confounded variables
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
30. In particular - the pdf of the standard normal distribution is denoted by
Block
f(z) - and its cdf by F(z).
The sample space
Type I errors
31. 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.
Sampling
Statistics
Treatment
nominal - ordinal - interval - and ratio
32. To find the average - or arithmetic mean - of a set of numbers:
Descriptive statistics
the population mean
The median value
Divide the sum by the number of values.
33. A group of individuals sharing some common features that might affect the treatment.
The standard deviation
Block
Pairwise independence
Type 2 Error
34. Probability of accepting a false null hypothesis.
Bias
Beta value
The variance of a random variable
Sample space
35. Var[X] :
A data set
variance of X
Binary data
Experimental and observational studies
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
nominal - ordinal - interval - and ratio
variance of X
Prior probability
37. 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.
the population mean
descriptive statistics
Probability and statistics
Dependent Selection
38. 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).
Probability density functions
the population mean
Power of a test
Joint probability
39. Another name for elementary event.
observational study
An estimate of a parameter
Sampling frame
Atomic event
40. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.
Standard error
The average - or arithmetic mean
The Covariance between two random variables X and Y - with expected values E(X) =
Independent Selection
41. A list of individuals from which the sample is actually selected.
Independence or Statistical independence
That value is the median value
Sampling frame
Dependent Selection
42. (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
The Expected value
Skewness
Particular realizations of a random variable
A likelihood function
43. Some commonly used symbols for population parameters
Block
the population mean
Beta value
Simpson's Paradox
44. A numerical measure that describes an aspect of a sample.
Coefficient of determination
Statistic
nominal - ordinal - interval - and ratio
The sample space
45. 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 Expected value
Correlation
Joint probability
The variance of a random variable
46. Are simply two different terms for the same thing. Add the given values
Inferential
Particular realizations of a random variable
Average and arithmetic mean
covariance of X and Y
47. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.
Simulation
Particular realizations of a random variable
hypotheses
A random variable
48. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.
Seasonal effect
Simulation
Type II errors
A data set
49. Is defined as the expected value of random variable (X -
The Covariance between two random variables X and Y - with expected values E(X) =
Law of Parsimony
The Expected value
Simple random sample
50. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
categorical variables
applied statistics
A probability density function
Individual