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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. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
The Covariance between two random variables X and Y - with expected values E(X) =
Reliable measure
Placebo effect
Mutual independence
2. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A statistic
Sampling
Divide the sum by the number of values.
covariance of X and Y
3. Probability of rejecting a true null hypothesis.
Sampling
Alpha value (Level of Significance)
Quantitative variable
A population or statistical population
4. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Kurtosis
Binary data
Treatment
5. 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).
An event
An Elementary event
An experimental study
the population cumulants
6. 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}.
Qualitative variable
The sample space
experimental studies and observational studies.
Correlation
7. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
A Probability measure
Law of Large Numbers
Inferential
Estimator
8. 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
Simple random sample
A Distribution function
Conditional probability
experimental studies and observational studies.
9. The collection of all possible outcomes in an experiment.
The variance of a random variable
Count data
Sample space
Law of Parsimony
10. Is a sample and the associated data points.
Inferential
The variance of a random variable
Correlation coefficient
A data set
11. (cdfs) are denoted by upper case letters - e.g. F(x).
Independence or Statistical independence
That is the median value
applied statistics
Cumulative distribution functions
12. A data value that falls outside the overall pattern of the graph.
Outlier
Statistical adjustment
Probability
Statistical inference
13. A variable describes an individual by placing the individual into a category or a group.
Particular realizations of a random variable
Statistical dispersion
A probability distribution
Qualitative variable
14. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
Type II errors
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Statistics
15. E[X] :
expected value of X
Sampling
Nominal measurements
Interval measurements
16. 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'
Valid measure
Average and arithmetic mean
Simpson's Paradox
Conditional probability
17. 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.
the population mean
Inferential statistics
Descriptive statistics
Marginal probability
18. A list of individuals from which the sample is actually selected.
Confounded variables
Estimator
Type 1 Error
Sampling frame
19. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
A probability density function
the population variance
Greek letters
20. Have no meaningful rank order among values.
Marginal distribution
Statistical dispersion
Nominal measurements
Ordinal measurements
21. 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 2 Error
Probability and statistics
applied statistics
Joint probability
22. Describes a characteristic of an individual to be measured or observed.
inferential statistics
The variance of a random variable
Ratio measurements
Variable
23. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
Simple random sample
A sampling distribution
A population or statistical population
24. A subjective estimate of probability.
the population correlation
Bias
Credence
Correlation coefficient
25. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
Inferential statistics
An Elementary event
A Probability measure
26. Gives the probability distribution for a continuous random variable.
An estimate of a parameter
A probability density function
Pairwise independence
Observational study
27. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Correlation
nominal - ordinal - interval - and ratio
Standard error
descriptive statistics
28. 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.
Simulation
Simpson's Paradox
A population or statistical population
A data set
29. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Individual
The Expected value
Quantitative variable
Interval measurements
30. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
The standard deviation
Null hypothesis
A Random vector
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
31. Of a group of numbers is the center point of all those number values.
Probability density functions
The average - or arithmetic mean
Descriptive
Trend
32. 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.
Correlation
Descriptive statistics
observational study
Seasonal effect
33. 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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34.
Sampling frame
the population mean
Estimator
Dependent Selection
35. 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)
Residuals
An Elementary event
Interval measurements
The Covariance between two random variables X and Y - with expected values E(X) =
36. (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.
Law of Parsimony
f(z) - and its cdf by F(z).
An Elementary event
The sample space
37. A measurement such that the random error is small
Marginal probability
A sampling distribution
Reliable measure
expected value of X
38. Is defined as the expected value of random variable (X -
Residuals
Estimator
Joint distribution
The Covariance between two random variables X and Y - with expected values E(X) =
39. In particular - the pdf of the standard normal distribution is denoted by
the sample or population mean
Simple random sample
A Statistical parameter
f(z) - and its cdf by F(z).
40. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
covariance of X and Y
Pairwise independence
experimental studies and observational studies.
Alpha value (Level of Significance)
41. A group of individuals sharing some common features that might affect the treatment.
Ordinal measurements
the sample or population mean
Likert scale
Block
42. Is the exact middle value of a set of numbers Arrange the numbers in numerical order. Find the value in the middle of the list.
A Statistical parameter
The Mean of a random variable
quantitative variables
The median value
43. 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
That is the median value
inferential statistics
The standard deviation
Step 3 of a statistical experiment
44. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
descriptive statistics
Ordinal measurements
Particular realizations of a random variable
Greek letters
45. 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.
Kurtosis
Divide the sum by the number of values.
the population correlation
Type 1 Error
46. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
The Range
An estimate of a parameter
Divide the sum by the number of values.
A probability density function
47. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
A statistic
Quantitative variable
Joint probability
P-value
48. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Count data
A Distribution function
Binomial experiment
Individual
49. 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 population correlation
Count data
the population variance
Statistical inference
50. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
Independence or Statistical independence
Atomic event
The variance of a random variable