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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
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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. 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.
quantitative variables
Correlation
Kurtosis
Statistical inference
2. (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
Sample space
The Expected value
Step 3 of a statistical experiment
descriptive statistics
3. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Probability
Likert scale
Statistic
Sampling
4. When you have two or more competing models - choose the simpler of the two models.
Lurking variable
categorical variables
Law of Parsimony
Sampling
5. Data are gathered and correlations between predictors and response are investigated.
Divide the sum by the number of values.
Block
Null hypothesis
observational study
6. The collection of all possible outcomes in an experiment.
Probability and statistics
Joint probability
Skewness
Sample space
7.
That value is the median value
hypotheses
Statistical dispersion
the population mean
8. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Mutual independence
categorical variables
Law of Parsimony
applied statistics
9. E[X] :
Independence or Statistical independence
expected value of X
Credence
A Random vector
10. 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
Estimator
Null hypothesis
Conditional distribution
Reliable measure
11. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Inferential
Bias
Treatment
A probability distribution
12. 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}.
observational study
Placebo effect
The sample space
Greek letters
13. Have imprecise differences between consecutive values - but have a meaningful order to those values
Skewness
A likelihood function
Sample space
Ordinal measurements
14. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Average and arithmetic mean
nominal - ordinal - interval - and ratio
Skewness
Marginal distribution
15. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Type 1 Error
Pairwise independence
covariance of X and Y
Credence
16. The standard deviation of a sampling distribution.
A likelihood function
Standard error
Independent Selection
Sample space
17. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl
A Probability measure
Standard error
Mutual independence
Particular realizations of a random variable
18. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
An event
An Elementary event
the sample or population mean
19. A numerical measure that describes an aspect of a sample.
Statistic
Beta value
Probability density functions
Inferential statistics
20. 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.
Simple random sample
Qualitative variable
Descriptive
That is the median value
21. A subjective estimate of probability.
Ratio measurements
Observational study
Bias
Credence
22. 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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23. 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
Quantitative variable
Simpson's Paradox
Joint probability
24. Another name for elementary event.
Probability and statistics
Credence
Random variables
Atomic event
25. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Step 2 of a statistical experiment
Individual
f(z) - and its cdf by F(z).
observational study
26. Statistical methods can be used for summarizing or describing a collection of data; this is called
the population variance
Conditional probability
descriptive statistics
An estimate of a parameter
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.
A statistic
Reliable measure
Seasonal effect
The median value
28. 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 cumulants
Marginal distribution
Step 1 of a statistical experiment
A population or statistical population
29. Are usually written in upper case roman letters: X - Y - etc.
Conditional probability
Random variables
A sampling distribution
Descriptive statistics
30. 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.
Coefficient of determination
Probability and statistics
Bias
Dependent Selection
31. 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
Joint probability
Probability and statistics
Residuals
Binary data
32. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Marginal probability
Treatment
33. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Statistical adjustment
Alpha value (Level of Significance)
Interval measurements
Atomic event
34. Probability of rejecting a true null hypothesis.
Credence
The Range
Coefficient of determination
Alpha value (Level of Significance)
35. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A statistic
The Range
Sampling Distribution
Null hypothesis
36. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Simple random sample
Sampling Distribution
Joint distribution
Greek letters
37. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
A probability space
Count data
Inferential
38. 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.
A data point
Statistical inference
A Statistical parameter
Placebo effect
39. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Experimental and observational studies
Joint distribution
Independent Selection
Law of Large Numbers
40. Probability of accepting a false null hypothesis.
Beta value
Probability density
experimental studies and observational studies.
Kurtosis
41. ?r
Estimator
Sampling
Power of a test
the population cumulants
42. 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
A data set
Trend
Variability
Probability density
43. Is data arising from counting that can take only non-negative integer values.
Count data
Marginal probability
That is the median value
A random variable
44. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Random variables
Descriptive
Likert scale
Sampling Distribution
45. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Greek letters
The variance of a random variable
Bias
Standard error
46. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Skewness
A sampling distribution
Ordinal measurements
Independence or Statistical independence
47. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
Simpson's Paradox
Skewness
hypothesis
48. A variable describes an individual by placing the individual into a category or a group.
Simpson's Paradox
Qualitative variable
Sampling Distribution
Probability
49. 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.
the population correlation
Observational study
the population mean
Conditional distribution
50. 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.
A random variable
Simpson's Paradox
Observational study
the population variance