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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. When there is an even number of values...
Experimental and observational studies
Descriptive statistics
That is the median value
The Mean of a random variable
2. 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.
The Expected value
quantitative variables
s-algebras
A random variable
3. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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4. 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.
Type 2 Error
Probability density
the sample or population mean
Conditional distribution
5. Failing to reject a false null hypothesis.
Type 2 Error
That is the median value
Kurtosis
expected value of X
6. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Particular realizations of a random variable
Treatment
methods of least squares
Bias
7. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Simulation
categorical variables
An experimental study
Null hypothesis
8. 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.
Cumulative distribution functions
Residuals
Kurtosis
Nominal measurements
9. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Conditional probability
Probability density functions
Skewness
Type I errors
10. 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.
Sampling Distribution
Seasonal effect
Block
Type 1 Error
11. A numerical measure that describes an aspect of a population.
variance of X
A Distribution function
Count data
Parameter
12. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
methods of least squares
Null hypothesis
A likelihood function
Inferential
13. 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
The sample space
Ratio measurements
Type I errors
Coefficient of determination
14. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Likert scale
Confounded variables
Simulation
15. 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 statistic
Sample space
The Covariance between two random variables X and Y - with expected values E(X) =
16. A numerical measure that describes an aspect of a sample.
A data point
Statistic
A random variable
Likert scale
17. Is the probability distribution - under repeated sampling of the population - of a given statistic.
An experimental study
A sampling distribution
the population cumulants
Ratio measurements
18. 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
methods of least squares
Step 2 of a statistical experiment
Correlation coefficient
hypotheses
19. 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
Outlier
A Probability measure
Descriptive statistics
Interval measurements
20. ?
the population correlation
Bias
Credence
Count data
21. 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.
Parameter - or 'statistical parameter'
Step 1 of a statistical experiment
Descriptive
A population or statistical population
22. 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
Descriptive
observational study
Skewness
23. 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
Seasonal effect
That is the median value
The Covariance between two random variables X and Y - with expected values E(X) =
24. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Standard error
That is the median value
Descriptive
Inferential statistics
25. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
Ratio measurements
Sampling Distribution
f(z) - and its cdf by F(z).
26. 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
Binary data
The median value
applied statistics
27. A list of individuals from which the sample is actually selected.
An experimental study
Probability and statistics
Sampling frame
hypotheses
28. Is a sample space over which a probability measure has been defined.
the population variance
A probability space
A data set
Individual
29. 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
Descriptive statistics
nominal - ordinal - interval - and ratio
Null hypothesis
30. 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
Probability density
Probability and statistics
Inferential statistics
A data point
31. A measure that is relevant or appropriate as a representation of that property.
Descriptive
Type I errors & Type II errors
Probability
Valid measure
32. A subjective estimate of probability.
Simpson's Paradox
Credence
Dependent Selection
Skewness
33. 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
Likert scale
Alpha value (Level of Significance)
Simulation
34. Of a group of numbers is the center point of all those number values.
Inferential
Residuals
The average - or arithmetic mean
Step 2 of a statistical experiment
35. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Lurking variable
An event
Ratio measurements
Statistical dispersion
36. Is data that can take only two values - usually represented by 0 and 1.
Binary data
expected value of X
Probability and statistics
Nominal measurements
37. 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'
Marginal distribution
Interval measurements
methods of least squares
Conditional probability
38. 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.
Credence
Atomic event
Estimator
Prior probability
39. The probability of correctly detecting a false null hypothesis.
Treatment
Inferential statistics
Power of a test
Correlation
40. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
A Distribution function
Parameter
Average and arithmetic mean
41. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Random variables
Type II errors
Correlation
inferential statistics
42. Var[X] :
Bias
Nominal measurements
variance of X
expected value of X
43. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Reliable measure
Nominal measurements
experimental studies and observational studies.
The standard deviation
44. Gives the probability of events in a probability space.
A Probability measure
Beta value
That is the median value
Count data
45. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Placebo effect
Law of Large Numbers
categorical variables
Parameter
46.
the population mean
Type 2 Error
Binomial experiment
Sampling Distribution
47. Statistical methods can be used for summarizing or describing a collection of data; this is called
Sampling Distribution
Count data
descriptive statistics
A sample
48. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Bias
Prior probability
Marginal distribution
Outlier
49. 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
hypotheses
the population mean
hypothesis
50. A group of individuals sharing some common features that might affect the treatment.
Placebo effect
Block
Conditional probability
A statistic