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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. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
A sample
Individual
Residuals
A data set
2. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Descriptive statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A statistic
Type 1 Error
3. 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
Valid measure
Step 1 of a statistical experiment
Residuals
f(z) - and its cdf by F(z).
4. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
Ordinal measurements
Lurking variable
Null hypothesis
Bias
5. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
Lurking variable
The Expected value
The Covariance between two random variables X and Y - with expected values E(X) =
6. 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.
Count data
Joint probability
Coefficient of determination
That value is the median value
7. (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
An estimate of a parameter
Simulation
The Expected value
expected value of X
8. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
Standard error
Interval measurements
A sampling distribution
9. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A random variable
A Random vector
Sampling frame
Dependent Selection
10. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o
methods of least squares
An estimate of a parameter
quantitative variables
Observational study
11. 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.
A population or statistical population
Pairwise independence
Marginal distribution
A likelihood function
12. Is data that can take only two values - usually represented by 0 and 1.
A data set
Likert scale
hypothesis
Binary data
13. A variable describes an individual by placing the individual into a category or a group.
Qualitative variable
Correlation coefficient
Marginal probability
descriptive statistics
14. Another name for elementary event.
Atomic event
Greek letters
the population cumulants
hypotheses
15. Have no meaningful rank order among values.
Nominal measurements
Statistical inference
covariance of X and Y
Binary data
16. 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
A sampling distribution
the population mean
observational study
17. Any specific experimental condition applied to the subjects
the population variance
Marginal probability
Variability
Treatment
18. 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).
A random variable
Cumulative distribution functions
Coefficient of determination
Joint probability
19. 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)
Probability
Simulation
variance of X
Interval measurements
20. 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.
s-algebras
A data point
Independence or Statistical independence
Binomial experiment
21. 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.
Individual
Inferential statistics
A Random vector
The variance of a random variable
22. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
A data set
quantitative variables
Type 2 Error
23. A numerical measure that assesses the strength of a linear relationship between two variables.
variance of X
Independent Selection
Correlation coefficient
Lurking variable
24. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that
Alpha value (Level of Significance)
hypothesis
the population variance
The average - or arithmetic mean
25. Statistical methods can be used for summarizing or describing a collection of data; this is called
Statistic
A Random vector
descriptive statistics
Standard error
26. A data value that falls outside the overall pattern of the graph.
Outlier
Beta value
Descriptive statistics
hypotheses
27. Is defined as the expected value of random variable (X -
Particular realizations of a random variable
The Covariance between two random variables X and Y - with expected values E(X) =
Statistical adjustment
Step 3 of a statistical experiment
28. Of a group of numbers is the center point of all those number values.
Seasonal effect
Kurtosis
The Expected value
The average - or arithmetic mean
29. The standard deviation of a sampling distribution.
covariance of X and Y
Law of Large Numbers
Standard error
A Random vector
30. Is data arising from counting that can take only non-negative integer values.
Statistical adjustment
Count data
Probability
Outlier
31. 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.
Reliable measure
Quantitative variable
Simple random sample
the population cumulants
32. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
Quantitative variable
Power of a test
Probability density functions
33. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.
Coefficient of determination
hypotheses
Particular realizations of a random variable
A Distribution function
34. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
Individual
Step 3 of a statistical experiment
Statistical dispersion
35. 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.
The Range
Sampling
Bias
Kurtosis
36. Is the probability distribution - under repeated sampling of the population - of a given statistic.
A sampling distribution
Beta value
A probability density function
Cumulative distribution functions
37. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
38. A subjective estimate of probability.
Null hypothesis
Joint probability
An experimental study
Credence
39. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Greek letters
A probability distribution
Statistics
Beta value
40. 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.
The sample space
Marginal distribution
Bias
Correlation
41. 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.
Posterior probability
Statistical inference
Prior probability
Statistic
42. Is a sample space over which a probability measure has been defined.
Bias
The Expected value
Beta value
A probability space
43. 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
categorical variables
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Type II errors
44. Have imprecise differences between consecutive values - but have a meaningful order to those values
Simpson's Paradox
Seasonal effect
Ordinal measurements
Marginal distribution
45. 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
Joint distribution
Treatment
Independence or Statistical independence
the sample or population mean
46. Where the null hypothesis is falsely rejected giving a 'false positive'.
The standard deviation
Block
Type I errors
Correlation
47. A list of individuals from which the sample is actually selected.
Type I errors
A data point
Sampling frame
Interval measurements
48. 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.
49. Is the length of the smallest interval which contains all the data.
Type II errors
Power of a test
Average and arithmetic mean
The Range
50. 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 Range
The sample space
An estimate of a parameter
Correlation coefficient