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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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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. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
the sample or population mean
Joint distribution
A sampling distribution
2. A measure that is relevant or appropriate as a representation of that property.
Valid measure
Marginal distribution
Variability
Statistical adjustment
3. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
covariance of X and Y
s-algebras
hypotheses
Seasonal effect
4. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
A Probability measure
the sample or population mean
Estimator
Conditional distribution
5. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Sampling Distribution
quantitative variables
A data point
Bias
6. 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
Step 1 of a statistical experiment
Probability and statistics
Probability density
quantitative variables
7. 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
An experimental study
Variability
Inferential statistics
8. Is a sample and the associated data points.
variance of X
Probability and statistics
A data set
Descriptive statistics
9. 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 likelihood function
The Expected value
The Range
The median value
10. The proportion of the explained variation by a linear regression model in the total variation.
Coefficient of determination
Lurking variable
inferential statistics
A population or statistical population
11. 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
the population mean
Residuals
Step 1 of a statistical experiment
Kurtosis
12. Failing to reject a false null hypothesis.
Valid measure
An estimate of a parameter
Type 2 Error
quantitative variables
13. 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.
Simple random sample
A population or statistical population
Bias
f(z) - and its cdf by F(z).
14. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Independence or Statistical independence
categorical variables
Experimental and observational studies
Variable
15. Is used in 'mathematical statistics' (alternatively - 'statistical theory') to study the sampling distributions of sample statistics and - more generally - the properties of statistical procedures. The use of any statistical method is valid when the
Probability
Greek letters
A Random vector
That is the median value
16. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
observational study
Parameter
s-algebras
17. 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
Bias
Step 1 of a statistical experiment
Interval measurements
18. Long-term upward or downward movement over time.
Lurking variable
Joint distribution
Trend
Valid measure
19. Gives the probability of events in a probability space.
A Probability measure
Parameter
A Distribution function
Kurtosis
20. Have no meaningful rank order among values.
the population cumulants
Descriptive statistics
the sample or population mean
Nominal measurements
21. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
Type II errors
Prior probability
An estimate of a parameter
Skewness
22. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Joint distribution
Probability density
Law of Parsimony
Simpson's Paradox
23. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris
Marginal probability
Inferential statistics
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
P-value
24. 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.
That value is the median value
Statistical inference
The Range
Coefficient of determination
25. 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.
Statistical inference
An Elementary event
Conditional probability
Placebo effect
26. A numerical facsimilie or representation of a real-world phenomenon.
Marginal distribution
Outlier
Variable
Simulation
27. 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.
Probability density functions
Variable
Seasonal effect
Sampling frame
28.
Estimator
Correlation coefficient
the population mean
Nominal measurements
29. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
That is the median value
Descriptive
Descriptive statistics
Probability density
30. (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.
An Elementary event
Bias
A Distribution function
observational study
31. 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).
Joint probability
A probability distribution
Lurking variable
the population correlation
32. 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
Independence or Statistical independence
A Distribution function
Residuals
quantitative variables
33. Two variables such that their effects on the response variable cannot be distinguished from each other.
Ratio measurements
Estimator
Type 2 Error
Confounded variables
34. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
The standard deviation
Bias
Step 3 of a statistical experiment
35. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
Binomial experiment
Independence or Statistical independence
Sampling frame
36. Cov[X - Y] :
Prior probability
A likelihood function
Statistic
covariance of X and Y
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.
Parameter
A sampling distribution
Dependent Selection
Type 1 Error
38. A data value that falls outside the overall pattern of the graph.
The Range
Simpson's Paradox
Outlier
Block
39. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
A Statistical parameter
The Covariance between two random variables X and Y - with expected values E(X) =
the sample or population mean
40. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Ratio measurements
Correlation coefficient
descriptive statistics
Statistical dispersion
41. 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).
Type 2 Error
A probability space
Probability density functions
An event
42. The standard deviation of a sampling distribution.
Standard error
Block
Variability
Posterior probability
43. Any specific experimental condition applied to the subjects
Treatment
The Range
Average and arithmetic mean
The average - or arithmetic mean
44. ?
The Range
Marginal distribution
Random variables
the population correlation
45. A list of individuals from which the sample is actually selected.
Sampling frame
Count data
the population correlation
Joint distribution
46. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Statistical adjustment
A Statistical parameter
Bias
Marginal distribution
47. The probability of correctly detecting a false null hypothesis.
Simpson's Paradox
Beta value
Null hypothesis
Power of a test
48. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
Bias
A probability density function
applied statistics
Lurking variable
49. 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
the population mean
An experimental study
Kurtosis
50. A measurement such that the random error is small
Probability density functions
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Reliable measure
The Mean of a random variable