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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. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
Variable
An estimate of a parameter
Power of a test
2. 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
A data set
applied statistics
An estimate of a parameter
3. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Type 1 Error
Sampling Distribution
inferential statistics
The average - or arithmetic mean
4. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Observational study
Variability
Residuals
A Distribution function
5. The standard deviation of a sampling distribution.
Standard error
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Power of a test
Alpha value (Level of Significance)
6. 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
A sampling distribution
Marginal probability
7. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
The average - or arithmetic mean
Binomial experiment
Coefficient of determination
categorical variables
8. 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.
categorical variables
Independent Selection
Alpha value (Level of Significance)
Statistic
9. 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 sampling distribution
Estimator
A random variable
The Covariance between two random variables X and Y - with expected values E(X) =
10. A data value that falls outside the overall pattern of the graph.
The variance of a random variable
Outlier
The median value
Beta value
11. 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
Probability and statistics
Treatment
Estimator
12. Is the length of the smallest interval which contains all the data.
The Range
Correlation coefficient
A data point
Treatment
13. A group of individuals sharing some common features that might affect the treatment.
Outlier
Block
Type 2 Error
Alpha value (Level of Significance)
14. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
A probability density function
A likelihood function
Law of Parsimony
15. Any specific experimental condition applied to the subjects
Interval measurements
experimental studies and observational studies.
Treatment
A data point
16. Is data arising from counting that can take only non-negative integer values.
A Probability measure
Alpha value (Level of Significance)
Law of Parsimony
Count data
17. Probability of accepting a false null hypothesis.
P-value
Beta value
Sampling
Inferential statistics
18. ?
the population correlation
Credence
Confounded variables
Treatment
19. 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.
Estimator
Dependent Selection
Greek letters
Variability
20. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Valid measure
Probability density
Type 1 Error
21. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
Trend
Sampling Distribution
Inferential
22. 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
experimental studies and observational studies.
Bias
methods of least squares
23. 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
the sample or population mean
Binary data
Step 3 of a statistical experiment
The Expected value
24. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Conditional distribution
A sampling distribution
Mutual independence
variance of X
25. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.
Statistics
hypotheses
Interval measurements
Marginal probability
26. 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
Probability and statistics
Mutual independence
Pairwise independence
27. A numerical measure that describes an aspect of a sample.
Statistical inference
Statistic
Experimental and observational studies
Outlier
28. To find the average - or arithmetic mean - of a set of numbers:
Pairwise independence
f(z) - and its cdf by F(z).
Experimental and observational studies
Divide the sum by the number of values.
29. Long-term upward or downward movement over time.
That is the median value
The variance of a random variable
Trend
Prior probability
30. Have meaningful distances between measurements defined - but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit)
Treatment
Placebo effect
Interval measurements
the population cumulants
31. 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
Simulation
A data set
A sampling distribution
32. 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
Sampling Distribution
the sample or population mean
Independence or Statistical independence
Interval measurements
33. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
Coefficient of determination
An Elementary event
the population mean
Prior probability
34. Failing to reject a false null hypothesis.
Placebo effect
observational study
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Type 2 Error
35. 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
experimental studies and observational studies.
The Expected value
Confounded variables
Observational study
36. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
observational study
Treatment
Average and arithmetic mean
37. 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.
A Probability measure
Estimator
Descriptive
Probability and statistics
38. 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.
39. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
methods of least squares
Valid measure
The standard deviation
Statistical inference
40. Is one that explores the correlation between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case - the researchers would collect o
nominal - ordinal - interval - and ratio
Observational study
variance of X
Statistics
41. 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.
Type 1 Error
A Distribution function
Independence or Statistical independence
Variable
42. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Residuals
Coefficient of determination
Greek letters
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
43. A list of individuals from which the sample is actually selected.
Sampling frame
Divide the sum by the number of values.
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Mutual independence
44. Is its expected value. The mean (or sample mean of a data set is just the average value.
the population cumulants
Simple random sample
Pairwise independence
The Mean of a random variable
45. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
Statistical inference
Kurtosis
Pairwise independence
P-value
46. Some commonly used symbols for population parameters
A sampling distribution
the population mean
Outlier
quantitative variables
47. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Statistical inference
Reliable measure
A Random vector
An estimate of a parameter
48. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
Coefficient of determination
A statistic
The Range
49. Have no meaningful rank order among values.
categorical variables
Type 1 Error
The standard deviation
Nominal measurements
50. Gives the probability distribution for a continuous random variable.
Beta value
Type I errors & Type II errors
A probability density function
Bias