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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. A subjective estimate of probability.
Confounded variables
A Random vector
Credence
Parameter
2. 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
A random variable
The median value
Qualitative variable
3. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Greek letters
Simple random sample
A random variable
4. A measure that is relevant or appropriate as a representation of that property.
Valid measure
The Range
Statistics
Correlation
5. 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
Descriptive statistics
Treatment
A Random vector
Statistical adjustment
6. The collection of all possible outcomes in an experiment.
Likert scale
Count data
Sample space
A Statistical parameter
7. S^2
Statistical inference
Type II errors
the population variance
categorical variables
8. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies
Parameter - or 'statistical parameter'
Marginal probability
experimental studies and observational studies.
observational study
9. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Marginal probability
Particular realizations of a random variable
Statistical dispersion
Bias
10. Gives the probability of events in a probability space.
A random variable
A Probability measure
P-value
The standard deviation
11. Statistical methods can be used for summarizing or describing a collection of data; this is called
Conditional probability
descriptive statistics
Law of Parsimony
The median value
12. 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
Credence
Independence or Statistical independence
Nominal measurements
13. A data value that falls outside the overall pattern of the graph.
Binary data
Bias
Skewness
Outlier
14. Another name for elementary event.
Atomic event
Likert scale
Power of a test
Beta value
15. 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.
A Probability measure
Conditional distribution
the population mean
Mutual independence
16. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Conditional distribution
Null hypothesis
Statistical adjustment
observational study
17. Any specific experimental condition applied to the subjects
Joint distribution
Treatment
Bias
Parameter
18. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Beta value
Treatment
Particular realizations of a random variable
Law of Large Numbers
19. 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.
Confounded variables
That value is the median value
Type I errors & Type II errors
A random variable
20. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
Marginal distribution
Independent Selection
A sampling distribution
21. 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.
Variable
Sampling
P-value
An estimate of a parameter
22. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
An event
Nominal measurements
applied statistics
Particular realizations of a random variable
23. Have no meaningful rank order among values.
Sample space
Nominal measurements
Observational study
Null hypothesis
24. Data are gathered and correlations between predictors and response are investigated.
Simulation
observational study
The Range
P-value
25. Are simply two different terms for the same thing. Add the given values
Estimator
Variability
Null hypothesis
Average and arithmetic mean
26. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
expected value of X
A sample
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
27. 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
Probability and statistics
the population mean
Null hypothesis
Nominal measurements
28. 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
A sample
Independence or Statistical independence
A population or statistical population
f(z) - and its cdf by F(z).
29. Rejecting a true null hypothesis.
Sample space
Independent Selection
Prior probability
Type 1 Error
30. 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 distribution
Greek letters
Placebo effect
Marginal probability
31. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Ratio measurements
Pairwise independence
That value is the median value
Statistical inference
32. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Dependent Selection
Greek letters
A sample
Treatment
33. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Probability density functions
Residuals
Simpson's Paradox
Experimental and observational studies
34. Probability of rejecting a true null hypothesis.
Qualitative variable
Alpha value (Level of Significance)
The Range
A population or statistical population
35. 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).
Descriptive
Law of Large Numbers
the population correlation
An event
36. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Bias
Treatment
The median value
Placebo effect
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.
Seasonal effect
Correlation coefficient
Dependent Selection
Correlation
38. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
categorical variables
f(z) - and its cdf by F(z).
the population cumulants
Variability
39. Gives the probability distribution for a continuous random variable.
Conditional distribution
A probability density function
A data set
P-value
40. 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
Outlier
expected value of X
Descriptive
41. (cdfs) are denoted by upper case letters - e.g. F(x).
A data point
Cumulative distribution functions
Count data
Block
42. 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.
An experimental study
Type I errors & Type II errors
A probability space
Parameter
43. A numerical measure that assesses the strength of a linear relationship between two variables.
Parameter
Correlation coefficient
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Block
44. Is a sample space over which a probability measure has been defined.
A probability space
Block
Residuals
Step 3 of a statistical experiment
45. 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
Step 2 of a statistical experiment
Lurking variable
Qualitative variable
Sampling Distribution
46. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Ordinal measurements
Lurking variable
Quantitative variable
Atomic event
47. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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48. 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.
Dependent Selection
Parameter - or 'statistical parameter'
Estimator
Greek letters
49. 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.
Statistical dispersion
The variance of a random variable
Statistical adjustment
experimental studies and observational studies.
50. Is denoted by - pronounced 'x bar'.
Mutual independence
Sample space
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Simple random sample