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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. 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.
Seasonal effect
Marginal probability
A Random vector
Kurtosis
2. Is that part of a population which is actually observed.
A sample
Sampling Distribution
Parameter - or 'statistical parameter'
quantitative variables
3. When there is an even number of values...
Simpson's Paradox
Mutual independence
Correlation
That is the median value
4. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
A sampling distribution
Dependent Selection
A likelihood function
Type II errors
5. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Type II errors
the population mean
Placebo effect
6. 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
Null hypothesis
Residuals
Step 3 of a statistical experiment
A probability space
7. 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
Confounded variables
Credence
the population correlation
8. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Alpha value (Level of Significance)
Pairwise independence
Simpson's Paradox
Marginal distribution
9. 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
Likert scale
Statistic
The Covariance between two random variables X and Y - with expected values E(X) =
10. Is a sample and the associated data points.
Inferential statistics
The Range
A data set
Step 2 of a statistical experiment
11. ?
the population correlation
Conditional probability
The variance of a random variable
Statistic
12. Gives the probability distribution for a continuous random variable.
A probability density function
Bias
The variance of a random variable
Conditional distribution
13. Is a parameter that indexes a family of probability distributions.
Sample space
Quantitative variable
Skewness
A Statistical parameter
14. 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
descriptive statistics
Step 1 of a statistical experiment
Binary data
Conditional distribution
15. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Conditional probability
Inferential
A Distribution function
Statistical dispersion
16. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
A population or statistical population
P-value
Statistical adjustment
Parameter - or 'statistical parameter'
17. 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
Cumulative distribution functions
the population cumulants
Correlation
Probability
18. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Correlation coefficient
Type I errors & Type II errors
An estimate of a parameter
Likert scale
19. 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.
Type I errors & Type II errors
Joint distribution
The sample space
Conditional distribution
20. Long-term upward or downward movement over time.
Experimental and observational studies
Dependent Selection
A Statistical parameter
Trend
21. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
hypotheses
experimental studies and observational studies.
Independent Selection
Bias
22. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
A probability space
Simulation
The standard deviation
expected value of X
23. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Quantitative variable
A likelihood function
hypotheses
Inferential
24. A measurement such that the random error is small
Reliable measure
That is the median value
descriptive statistics
A statistic
25. ?r
Treatment
the population cumulants
Sampling Distribution
Block
26. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Credence
A population or statistical population
Cumulative distribution functions
Bias
27. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Credence
Marginal probability
Type I errors
the sample or population mean
28. 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.
Statistic
Statistical inference
Cumulative distribution functions
Prior probability
29. 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.
Correlation
A population or statistical population
Seasonal effect
Confounded variables
30. Is the length of the smallest interval which contains all the data.
Law of Large Numbers
Inferential statistics
The Range
Ratio measurements
31. Any specific experimental condition applied to the subjects
A sample
Conditional probability
Treatment
Pairwise independence
32. To find the average - or arithmetic mean - of a set of numbers:
Divide the sum by the number of values.
Probability density functions
the population variance
Step 2 of a statistical experiment
33. (cdfs) are denoted by upper case letters - e.g. F(x).
Probability density
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Trend
Cumulative distribution functions
34. Probability of rejecting a true null hypothesis.
Independence or Statistical independence
Dependent Selection
expected value of X
Alpha value (Level of Significance)
35. 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.
A probability distribution
Probability
Step 3 of a statistical experiment
The variance of a random variable
36. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
Parameter - or 'statistical parameter'
The Mean of a random variable
Alpha value (Level of Significance)
37. Describes a characteristic of an individual to be measured or observed.
experimental studies and observational studies.
Variable
Statistics
Bias
38. Data are gathered and correlations between predictors and response are investigated.
Inferential
Treatment
observational study
Lurking variable
39. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Sample space
That value is the median value
Binomial experiment
Variable
40. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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41. 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.
Parameter - or 'statistical parameter'
Sampling
P-value
That value is the median value
42. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Conditional probability
Divide the sum by the number of values.
Marginal distribution
The standard deviation
43. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
applied statistics
Type II errors
Individual
Sampling frame
44. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that
Null hypothesis
Step 1 of a statistical experiment
the population correlation
hypothesis
45. Failing to reject a false null hypothesis.
Average and arithmetic mean
Bias
Type 2 Error
A statistic
46. 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
Descriptive statistics
Count data
A probability distribution
Probability and statistics
47. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
the sample or population mean
Sampling Distribution
Descriptive statistics
P-value
48. Where the null hypothesis is falsely rejected giving a 'false positive'.
Statistic
inferential statistics
Bias
Type I errors
49. Of a group of numbers is the center point of all those number values.
Coefficient of determination
The average - or arithmetic mean
Simulation
descriptive statistics
50. 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.
Count data
A population or statistical population
Binomial experiment
Inferential statistics