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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.
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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. Gives the probability of events in a probability space.
The Mean of a random variable
The standard deviation
Likert scale
A Probability measure
2. 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.
applied statistics
Parameter - or 'statistical parameter'
Observational study
Conditional distribution
3. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then
quantitative variables
Law of Large Numbers
Quantitative variable
A likelihood function
4. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
An event
Variable
The variance of a random variable
s-algebras
5. E[X] :
expected value of X
Kurtosis
Nominal measurements
A data point
6. Failing to reject a false null hypothesis.
Type 2 Error
the population correlation
Descriptive statistics
Simpson's Paradox
7. A subjective estimate of probability.
Dependent Selection
Cumulative distribution functions
Credence
A Random vector
8. 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
That value is the median value
Probability and statistics
Statistical dispersion
Sample space
9. Is the length of the smallest interval which contains all the data.
inferential statistics
observational study
Probability density
The Range
10. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
Step 3 of a statistical experiment
Step 1 of a statistical experiment
Block
A Probability measure
11. In particular - the pdf of the standard normal distribution is denoted by
Bias
Particular realizations of a random variable
f(z) - and its cdf by F(z).
The standard deviation
12. Is denoted by - pronounced 'x bar'.
Qualitative variable
Probability density
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Bias
13. 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.
hypotheses
Posterior probability
A data point
Valid measure
14. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Dependent Selection
Independence or Statistical independence
Type I errors
A probability distribution
15. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
The average - or arithmetic mean
An experimental study
A statistic
16. Long-term upward or downward movement over time.
Binomial experiment
A probability distribution
Trend
Standard error
17. The collection of all possible outcomes in an experiment.
Parameter - or 'statistical parameter'
Conditional probability
Sample space
variance of X
18. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
A Statistical parameter
Step 3 of a statistical experiment
Prior probability
Coefficient of determination
19. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
Dependent Selection
Quantitative variable
Sample space
20. A numerical measure that assesses the strength of a linear relationship between two variables.
Experimental and observational studies
The standard deviation
Correlation coefficient
An Elementary event
21. Gives the probability distribution for a continuous random variable.
inferential statistics
Valid measure
categorical variables
A probability density function
22. Is defined as the expected value of random variable (X -
Mutual independence
applied statistics
The Covariance between two random variables X and Y - with expected values E(X) =
categorical variables
23. The proportion of the explained variation by a linear regression model in the total variation.
Simple random sample
Coefficient of determination
the population correlation
Interval measurements
24. A numerical measure that describes an aspect of a population.
The median value
The Mean of a random variable
Parameter
A Random vector
25. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Individual
Placebo effect
Law of Large Numbers
hypothesis
26. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Descriptive statistics
Conditional probability
Quantitative variable
Reliable measure
27. 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
Independence or Statistical independence
Correlation
Conditional probability
Step 3 of a statistical experiment
28. A list of individuals from which the sample is actually selected.
Valid measure
Sampling frame
A Probability measure
The Covariance between two random variables X and Y - with expected values E(X) =
29. 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
Type 2 Error
Type 1 Error
Law of Parsimony
30. 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.
Statistical dispersion
An experimental study
Ratio measurements
Simple random sample
31. When there is an even number of values...
Skewness
That is the median value
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Ordinal measurements
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.
Step 2 of a statistical experiment
Quantitative variable
Sampling
Coefficient of determination
33. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Type 2 Error
Bias
Seasonal effect
34. 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
Probability
An experimental study
s-algebras
35. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Credence
The standard deviation
inferential statistics
Treatment
36. Any specific experimental condition applied to the subjects
Descriptive
A probability density function
Treatment
Simple random sample
37. Two variables such that their effects on the response variable cannot be distinguished from each other.
the sample or population mean
Confounded variables
Independent Selection
Sample space
38. Is that part of a population which is actually observed.
f(z) - and its cdf by F(z).
Sampling
Type II errors
A sample
39. Interpretation of statistical information in that the assumption is that whatever is proposed as a cause has no effect on the variable being measured can often involve the development of a
Marginal probability
Null hypothesis
Step 1 of a statistical experiment
Kurtosis
40. Rejecting a true null hypothesis.
Statistic
Type 1 Error
A likelihood function
Binary data
41. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
The median value
Posterior probability
Step 1 of a statistical experiment
42. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
A sample
Trend
nominal - ordinal - interval - and ratio
Sampling
43. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Divide the sum by the number of values.
Inferential statistics
A statistic
Sample space
44. Are simply two different terms for the same thing. Add the given values
Average and arithmetic mean
Statistical adjustment
The sample space
A Statistical parameter
45. 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.
Independent Selection
Parameter - or 'statistical parameter'
The standard deviation
The Range
46. Probability of rejecting a true null hypothesis.
Statistic
Alpha value (Level of Significance)
Binomial experiment
Particular realizations of a random variable
47. Are usually written in upper case roman letters: X - Y - etc.
Nominal measurements
That is the median value
Dependent Selection
Random variables
48. Have imprecise differences between consecutive values - but have a meaningful order to those values
Lurking variable
Ordinal measurements
A Probability measure
Block
49. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Estimator
Greek letters
Ratio measurements
50. To find the average - or arithmetic mean - of a set of numbers:
Type 2 Error
Divide the sum by the number of values.
Ordinal measurements
Marginal probability