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Test your basic knowledge 
CLEP General Mathematics: Probability And Statistics
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clep
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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 reenforces your understanding as you take the test each time.
1. Longterm upward or downward movement over time.
Trend
Posterior probability
Type 2 Error
the population correlation
2. (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.
An Elementary event
Marginal probability
applied statistics
Divide the sum by the number of values.
3. Data are gathered and correlations between predictors and response are investigated.
observational study
A sampling distribution
Marginal distribution
Law of Large Numbers
4. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
hypotheses
Sampling
The Mean of a random variable
Type II errors
5. Is a measure of the asymmetry of the probability distribution of a realvalued random variable. Roughly speaking  a distribution has positive skew (rightskewed) if the higher tail is longer and negative skew (leftskewed) if the lower tail is longe
Skewness
A likelihood function
Joint distribution
Probability density
6. Rejecting a true null hypothesis.
Sampling Distribution
The Expected value
categorical variables
Type 1 Error
7. The errors  or difference between the estimated response y^i and the actual measured response yi  collectively
Inferential
Conditional distribution
Law of Large Numbers
Residuals
8. 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.
Dependent Selection
Binomial experiment
categorical variables
An experimental study
9. 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
Pvalue
Probability density functions
A probability distribution
inferential statistics
10. Many statistical methods seek to minimize the meansquared error  and these are called
The sample space
the population correlation
Sample space
methods of least squares
11. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
Beta value
Average and arithmetic mean
Cumulative distribution functions
12. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Confounded variables
A data set
Sampling Distribution
Conditional probability
13. 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
Law of Parsimony
experimental studies and observational studies.
Simple random sample
Prior probability
14. Another name for elementary event.
Estimator
Block
Atomic event
Descriptive statistics
15. Gives the probability distribution for a continuous random variable.
The Covariance between two random variables X and Y  with expected values E(X) =
A probability density function
Observational study
Alpha value (Level of Significance)
16. 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.
Law of Large Numbers
Individual
Simple random sample
Parameter  or 'statistical parameter'
17. Describes a characteristic of an individual to be measured or observed.
Kurtosis
Variable
Probability density functions
Sampling
18. 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.
Cumulative distribution functions
Law of Parsimony
Statistics
hypotheses
19. Is its expected value. The mean (or sample mean of a data set is just the average value.
Experimental and observational studies
Inferential statistics
hypotheses
The Mean of a random variable
20. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
Residuals
Statistic
Probability density functions
21. In the long run  as the sample size increases  the relative frequencies of outcomes approach to the theoretical probability.
Statistical inference
experimental studies and observational studies.
Law of Large Numbers
Quantitative variable
22. Some commonly used symbols for sample statistics
Type I errors & Type II errors
observational study
the sample mean  the sample variance s2  the sample correlation coefficient r  the sample cumulants kr.
Ordinal measurements
23. A measure that is relevant or appropriate as a representation of that property.
That is the median value
Likert scale
methods of least squares
Valid measure
24. Is defined as the expected value of random variable (X 
The Covariance between two random variables X and Y  with expected values E(X) =
A data point
hypothesis
the population mean
25. (pdfs) and probability mass functions are denoted by lower case letters  e.g. f(x).
Posterior probability
Probability density functions
Step 1 of a statistical experiment
Average and arithmetic mean
26. 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).
That is the median value
An event
Outlier
Prior probability
27. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Dependent Selection
Prior probability
Likert scale
Pairwise independence
28. S^2
Marginal probability
Sampling
Power of a test
the population variance
29. Descriptive statistics and inferential statistics (a.k.a.  predictive statistics) together comprise
Conditional probability
applied statistics
Simpson's Paradox
quantitative variables
30. 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
Pairwise independence
Type 2 Error
Treatment
31. Is the probability of some event A  assuming event B. Conditional probability is written P(AB)  and is read 'the probability of A  given B'
A data set
Conditional probability
Skewness
hypothesis
32. A group of individuals sharing some common features that might affect the treatment.
Block
Sampling frame
Variability
Inferential statistics
33. 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
hypotheses
Sample space
Count data
Observational study
34. 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 Probability measure
applied statistics
An experimental study
A random variable
35. (e.g. ?  b) are commonly used to denote unknown parameters (population parameters).
observational study
The Range
Greek letters
Step 2 of a statistical experiment
36. Probability of accepting a false null hypothesis.
Beta value
A statistic
covariance of X and Y
Law of Large Numbers
37. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
nominal  ordinal  interval  and ratio
Type II errors
methods of least squares
A Random vector
38. A subjective estimate of probability.
Credence
The median value
Statistical dispersion
Coefficient of determination
39. Var[X] :
Valid measure
An experimental study
variance of X
Type 2 Error
40. 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.
Step 2 of a statistical experiment
Sampling
Ordinal measurements
Power of a test
41. A variable describes an individual by placing the individual into a category or a group.
Experimental and observational studies
Greek letters
Qualitative variable
Dependent Selection
42. Is data that can take only two values  usually represented by 0 and 1.
Binary data
Seasonal effect
The median value
A data point
43. 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 Expected value
Posterior probability
An event
Step 3 of a statistical experiment
44. Statistical methods can be used for summarizing or describing a collection of data; this is called
Interval measurements
The variance of a random variable
descriptive statistics
Inferential statistics
45. 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.
Confounded variables
Quantitative variable
Estimator
Conditional probability
46. Where the null hypothesis is falsely rejected giving a 'false positive'.
Sampling frame
Simple random sample
A population or statistical population
Type I errors
47. Is the most commonly used measure of statistical dispersion. It is the square root of the variance  and is generally written s (sigma).
applied statistics
Seasonal effect
Divide the sum by the number of values.
The standard deviation
48. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically  sometimes they are grouped together as
Individual
Parameter  or 'statistical parameter'
categorical variables
An Elementary event
49. Gives the probability of events in a probability space.
f(z)  and its cdf by F(z).
Type 2 Error
Estimator
A Probability measure
50. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Sampling Distribution
Ratio measurements
A sampling distribution
nominal  ordinal  interval  and ratio