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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. To find the average - or arithmetic mean - of a set of numbers:
Conditional distribution
Divide the sum by the number of values.
Cumulative distribution functions
Sampling Distribution
2. 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}.
Posterior probability
Conditional distribution
A probability density function
The sample space
3. 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.
A Distribution function
Interval measurements
applied statistics
Independent Selection
4. 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
Type 2 Error
Type II errors
Correlation
hypothesis
5. 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
methods of least squares
Ratio measurements
A Statistical parameter
Lurking variable
6. 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
Mutual independence
Marginal distribution
Statistics
7. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Observational study
Individual
Placebo effect
Type I errors & Type II errors
8. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Individual
Binomial experiment
The standard deviation
Mutual independence
9. Some commonly used symbols for population parameters
the population mean
An Elementary event
Observational study
Particular realizations of a random variable
10. Any specific experimental condition applied to the subjects
Treatment
That is the median value
The standard deviation
Conditional distribution
11. 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.
Joint probability
A random variable
Nominal measurements
An experimental study
12. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
Parameter - or 'statistical parameter'
Kurtosis
quantitative variables
13. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Sampling frame
Statistical adjustment
Type I errors
Probability
14. The proportion of the explained variation by a linear regression model in the total variation.
Type II errors
Coefficient of determination
A data point
Pairwise independence
15. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Block
Sampling Distribution
Count data
Inferential
16. Are simply two different terms for the same thing. Add the given values
A Distribution function
Inferential statistics
Statistic
Average and arithmetic mean
17. Some commonly used symbols for sample statistics
Likert scale
the population variance
Marginal distribution
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
18. 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
A data point
Skewness
P-value
Mutual independence
19. 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).
Average and arithmetic mean
Independence or Statistical independence
Type I errors & Type II errors
Joint probability
20. 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
Binomial experiment
Beta value
Probability
Valid measure
21. 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
An experimental study
A Statistical parameter
The variance of a random variable
inferential statistics
22. 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.
observational study
Interval measurements
Seasonal effect
Treatment
23. (cdfs) are denoted by upper case letters - e.g. F(x).
Variability
Prior probability
Cumulative distribution functions
Type 2 Error
24. A numerical measure that assesses the strength of a linear relationship between two variables.
Ratio measurements
Correlation coefficient
Prior probability
Joint distribution
25. Gives the probability of events in a probability space.
A sampling distribution
Average and arithmetic mean
A Probability measure
Standard error
26. In particular - the pdf of the standard normal distribution is denoted by
Joint probability
Bias
Standard error
f(z) - and its cdf by F(z).
27. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Average and arithmetic mean
Conditional probability
Probability density functions
Treatment
28. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Conditional probability
An estimate of a parameter
inferential statistics
A sampling distribution
29. Is a sample space over which a probability measure has been defined.
A probability space
A probability density function
Probability density
Type 2 Error
30. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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31. Is a parameter that indexes a family of probability distributions.
the sample or population mean
A population or statistical population
Credence
A Statistical parameter
32. S^2
Quantitative variable
hypothesis
the population variance
A probability density function
33. A subjective estimate of probability.
Credence
the population mean
Ratio measurements
the population variance
34. 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.
Estimator
The variance of a random variable
An Elementary event
Trend
35. A variable describes an individual by placing the individual into a category or a group.
Posterior probability
Quantitative variable
Qualitative variable
A likelihood function
36. Describes a characteristic of an individual to be measured or observed.
f(z) - and its cdf by F(z).
Simple random sample
inferential statistics
Variable
37. 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.
Marginal probability
Simpson's Paradox
hypothesis
categorical variables
38. 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.
Type 1 Error
A probability space
A data set
A population or statistical population
39. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Interval measurements
Placebo effect
Law of Large Numbers
Beta value
40. 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
That is the median value
Cumulative distribution functions
Sampling frame
41. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A data set
A statistic
Inferential statistics
Kurtosis
42. A group of individuals sharing some common features that might affect the treatment.
Inferential
Block
Joint distribution
Quantitative variable
43. 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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44. 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
A population or statistical population
Mutual independence
Probability and statistics
45. Is a sample and the associated data points.
A data point
Random variables
Nominal measurements
A data set
46. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
A sampling distribution
The Mean of a random variable
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
47. 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.
Inferential
Conditional distribution
The standard deviation
A random variable
48. Where the null hypothesis is falsely rejected giving a 'false positive'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Type I errors
An event
That value is the median value
49. 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.
A probability space
Treatment
Observational study
That value is the median value
50. Rejecting a true null hypothesis.
Lurking variable
Statistical dispersion
A likelihood function
Type 1 Error