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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.
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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. Is the length of the smallest interval which contains all the data.
the sample or population mean
Bias
Skewness
The Range
2. 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
An experimental study
Sampling Distribution
The median value
3. In number theory - scatter plots of data generated by a distribution function may be transformed with familiar tools used in statistics to reveal underlying patterns - which may then lead to
hypotheses
observational study
Mutual independence
Qualitative variable
4. 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.
The variance of a random variable
Interval measurements
applied statistics
nominal - ordinal - interval - and ratio
5. 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.
Estimator
Treatment
Posterior probability
Sampling frame
6. 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
Statistic
Residuals
Inferential statistics
Simpson's Paradox
7. Many statistical methods seek to minimize the mean-squared error - and these are called
Lurking variable
A Random vector
methods of least squares
Sample space
8. A data value that falls outside the overall pattern of the graph.
Individual
Trend
Outlier
the population mean
9. 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
Type 1 Error
Correlation
Mutual independence
Experimental and observational studies
10. To find the average - or arithmetic mean - of a set of numbers:
Divide the sum by the number of values.
P-value
hypotheses
the population mean
11. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
A sample
applied statistics
the sample or population mean
Ordinal measurements
12. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
A sampling distribution
P-value
Correlation coefficient
13. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
expected value of X
Parameter - or 'statistical parameter'
The average - or arithmetic mean
Residuals
14. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Standard error
A sample
Law of Large Numbers
Bias
15. Probability of accepting a false null hypothesis.
Sampling frame
Beta value
Bias
The sample space
16. 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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17. A variable describes an individual by placing the individual into a category or a group.
hypothesis
Qualitative variable
A population or statistical population
Correlation coefficient
18. 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.
quantitative variables
The variance of a random variable
A random variable
Bias
19. Any specific experimental condition applied to the subjects
A random variable
The Range
The average - or arithmetic mean
Treatment
20. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Independent Selection
descriptive statistics
Simple random sample
21. (cdfs) are denoted by upper case letters - e.g. F(x).
A sampling distribution
applied statistics
Cumulative distribution functions
the sample or population mean
22. A subjective estimate of probability.
Credence
nominal - ordinal - interval - and ratio
Simulation
The Mean of a random variable
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.
Parameter
An experimental study
the sample or population mean
Interval measurements
24. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
The Range
Simulation
Parameter
Descriptive
25. 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'
That value is the median value
Type I errors
Conditional probability
Seasonal effect
26. Is the probability distribution - under repeated sampling of the population - of a given statistic.
The Range
Null hypothesis
Residuals
A sampling distribution
27. A measurement such that the random error is small
Lurking variable
observational study
variance of X
Reliable measure
28. Statistical methods can be used for summarizing or describing a collection of data; this is called
Particular realizations of a random variable
The Covariance between two random variables X and Y - with expected values E(X) =
A Probability measure
descriptive statistics
29. 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.
s-algebras
Simple random sample
Ordinal measurements
Placebo effect
30. 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 Random vector
The median value
Probability density
Skewness
31. A list of individuals from which the sample is actually selected.
Sampling frame
Simple random sample
Law of Large Numbers
Alpha value (Level of Significance)
32. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
applied statistics
An event
A random variable
33. When there is an even number of values...
That is the median value
Type 2 Error
Qualitative variable
Simpson's Paradox
34. 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
Step 1 of a statistical experiment
Placebo effect
The median value
A data point
35. 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
Simple random sample
Probability and statistics
The Covariance between two random variables X and Y - with expected values E(X) =
The standard deviation
36. In particular - the pdf of the standard normal distribution is denoted by
s-algebras
f(z) - and its cdf by F(z).
Credence
That is the median value
37. Probability of rejecting a true null hypothesis.
Alpha value (Level of Significance)
Conditional distribution
Statistical dispersion
hypotheses
38. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
experimental studies and observational studies.
Bias
That is the median value
hypothesis
39. Is its expected value. The mean (or sample mean of a data set is just the average value.
quantitative variables
hypothesis
The Mean of a random variable
Binomial experiment
40. Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol. 4. Further examining the data set in secondary analyses - to suggest new hypotheses for future study. 5. Documenting and present
Step 3 of a statistical experiment
Power of a test
Credence
descriptive statistics
41. Gives the probability of events in a probability space.
A data point
The Expected value
A Probability measure
Valid measure
42. 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
Descriptive
Independence or Statistical independence
Type I errors & Type II errors
Statistic
43. The probability of correctly detecting a false null hypothesis.
A data point
categorical variables
Power of a test
Quantitative variable
44. 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
inferential statistics
A likelihood function
Step 2 of a statistical experiment
Standard error
45. 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
Probability density
Conditional distribution
covariance of X and Y
Joint probability
46. A measure that is relevant or appropriate as a representation of that property.
Law of Parsimony
Count data
Valid measure
Variability
47. 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.
Statistical dispersion
Independence or Statistical independence
the population variance
Dependent Selection
48.
the population mean
inferential statistics
Statistical adjustment
Power of a test
49. Is data arising from counting that can take only non-negative integer values.
Count data
Alpha value (Level of Significance)
Bias
A likelihood function
50. A group of individuals sharing some common features that might affect the treatment.
Block
A population or statistical population
the population variance
Placebo effect