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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
Start Test
Study First
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
.
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. ?r
the population cumulants
the population correlation
Skewness
Reliable measure
2. 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
Parameter
Observational study
A random variable
3. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
The Expected value
Binomial experiment
A Random vector
4. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Type 1 Error
The standard deviation
the population cumulants
5. Describes the spread in the values of the sample statistic when many samples are taken.
Valid measure
Simulation
Variability
Law of Parsimony
6. 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).
Law of Large Numbers
An event
Credence
Probability density
7. 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
Statistic
A probability space
inferential statistics
f(z) - and its cdf by F(z).
8. A measurement such that the random error is small
Skewness
Joint distribution
A population or statistical population
Reliable measure
9. 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
Observational study
Confounded variables
Variable
Ratio measurements
10. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Estimator
That is the median value
An estimate of a parameter
Placebo effect
11. A numerical measure that assesses the strength of a linear relationship between two variables.
Type I errors & Type II errors
Sampling
Correlation coefficient
Mutual independence
12. Another name for elementary event.
Atomic event
Ratio measurements
observational study
Statistical adjustment
13. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Statistical inference
Estimator
variance of X
14. 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'
Conditional probability
A population or statistical population
Inferential statistics
Individual
15. 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.
Dependent Selection
An event
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A population or statistical population
16. 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.
Pairwise independence
s-algebras
Dependent Selection
Nominal measurements
17. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris
Alpha value (Level of Significance)
Sampling Distribution
Inferential statistics
observational study
18. Some commonly used symbols for sample statistics
Law of Large Numbers
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A Statistical parameter
A Random vector
19. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Random variables
Bias
Dependent Selection
An experimental study
20. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Confounded variables
Bias
Coefficient of determination
Prior probability
21. 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.
A Probability measure
Independent Selection
Quantitative variable
Law of Large Numbers
22. 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
Descriptive statistics
A probability distribution
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
23. 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.
Statistics
Inferential
Reliable measure
An experimental study
24. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
inferential statistics
An experimental study
the sample or population mean
Outlier
25. A numerical measure that describes an aspect of a population.
Descriptive
s-algebras
Parameter
Random variables
26. 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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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
A probability space
observational study
Null hypothesis
Estimator
28. 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.
That value is the median value
Block
f(z) - and its cdf by F(z).
quantitative variables
29. 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.
Nominal measurements
Particular realizations of a random variable
Null hypothesis
Lurking variable
30. 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 sample space
Joint distribution
A probability density function
Probability
31. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
The Range
Type II errors
Greek letters
Likert scale
32. When there is an even number of values...
A statistic
Type II errors
That is the median value
Type 1 Error
33. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Posterior probability
Probability
A Statistical parameter
Quantitative variable
34. Where the null hypothesis is falsely rejected giving a 'false positive'.
Descriptive
Type II errors
Binary data
Type I errors
35. 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.
Confounded variables
Atomic event
Seasonal effect
Simpson's Paradox
36. Rejecting a true null hypothesis.
Alpha value (Level of Significance)
Type 1 Error
The Mean of a random variable
An event
37. (cdfs) are denoted by upper case letters - e.g. F(x).
Placebo effect
Statistical dispersion
The Expected value
Cumulative distribution functions
38. 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.
Joint distribution
Random variables
Sampling
Statistical dispersion
39. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Correlation
Kurtosis
observational study
Residuals
40. 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
experimental studies and observational studies.
Correlation coefficient
Simple random sample
41. A numerical measure that describes an aspect of a sample.
s-algebras
Type II errors
the population cumulants
Statistic
42. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Descriptive statistics
Pairwise independence
The standard deviation
Credence
43. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
Probability density
Statistic
Particular realizations of a random variable
44. A numerical facsimilie or representation of a real-world phenomenon.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Correlation coefficient
Sample space
Simulation
45. The standard deviation of a sampling distribution.
methods of least squares
Descriptive statistics
the population variance
Standard error
46. 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
Simpson's Paradox
Descriptive statistics
Valid measure
A sample
47. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
The sample space
Statistical dispersion
Type I errors & Type II errors
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
48. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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49. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Confounded variables
A probability distribution
Pairwise independence
Variability
50. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
covariance of X and Y
A Statistical parameter
Type II errors
Count data