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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. 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
Inferential statistics
Correlation coefficient
An estimate of a parameter
A Probability measure
2. Is its expected value. The mean (or sample mean of a data set is just the average value.
f(z) - and its cdf by F(z).
The Mean of a random variable
Lurking variable
Correlation coefficient
3. Where the null hypothesis is falsely rejected giving a 'false positive'.
variance of X
P-value
Confounded variables
Type I errors
4. A data value that falls outside the overall pattern of the graph.
Block
Outlier
Cumulative distribution functions
Residuals
5. Is a parameter that indexes a family of probability distributions.
An Elementary event
A Statistical parameter
Nominal measurements
Probability density functions
6. 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
Observational study
Step 1 of a statistical experiment
Null hypothesis
the population correlation
7. Gives the probability of events in a probability space.
Parameter - or 'statistical parameter'
A Probability measure
Simple random sample
Average and arithmetic mean
8. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
Standard error
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Qualitative variable
Skewness
9. S^2
the population variance
Experimental and observational studies
hypothesis
Step 2 of a statistical experiment
10. When there is an even number of values...
That is the median value
the population mean
Type 2 Error
An Elementary event
11. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Sampling Distribution
Reliable measure
Posterior probability
the population mean
12. 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.
Block
Parameter
The median value
Conditional distribution
13. Is a sample and the associated data points.
A data set
The Expected value
Statistics
Descriptive
14. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.
Marginal probability
the population cumulants
the population mean
Statistical inference
15. Is data arising from counting that can take only non-negative integer values.
Pairwise independence
Count data
Seasonal effect
An experimental study
16. 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
An estimate of a parameter
the population cumulants
Step 3 of a statistical experiment
Random variables
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
Observational study
Probability
The Mean of a random variable
A probability density function
18. 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
Power of a test
That is the median value
Type I errors
19. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
descriptive statistics
A data point
Conditional distribution
Ordinal measurements
20. Is the probability of two events occurring together. The joint probability of A and B is written P(A and B) or P(A - B).
Joint probability
Correlation
Step 2 of a statistical experiment
Beta value
21. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
A probability density function
Individual
categorical variables
Bias
22. 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.
quantitative variables
Marginal distribution
Ratio measurements
The Covariance between two random variables X and Y - with expected values E(X) =
23. 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.
Placebo effect
Independence or Statistical independence
Sampling
Probability density functions
24. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Prior probability
quantitative variables
Statistical adjustment
The Range
25. 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.
A population or statistical population
Marginal probability
Statistical inference
Ordinal measurements
26. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
An estimate of a parameter
Random variables
Probability density functions
Sampling Distribution
27. Is the length of the smallest interval which contains all the data.
The Range
Type 2 Error
Alpha value (Level of Significance)
Simulation
28. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Type 2 Error
Greek letters
Trend
Confounded variables
29. Have imprecise differences between consecutive values - but have a meaningful order to those values
Beta value
covariance of X and Y
Ordinal measurements
Step 1 of a statistical experiment
30. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
expected value of X
Parameter
applied statistics
31. 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
Descriptive
the population variance
Binomial experiment
Null hypothesis
32. 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'
The standard deviation
Greek letters
Treatment
Conditional probability
33. 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.
Statistical dispersion
Likert scale
Seasonal effect
categorical variables
34. Have no meaningful rank order among values.
Nominal measurements
Pairwise independence
A probability density function
The Expected value
35. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Block
Statistical adjustment
The standard deviation
A probability density function
36. 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
Experimental and observational studies
Step 2 of a statistical experiment
Probability density functions
Standard error
37. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Sampling Distribution
quantitative variables
Confounded variables
38. A group of individuals sharing some common features that might affect the treatment.
Step 1 of a statistical experiment
Block
Bias
Step 2 of a statistical experiment
39. The proportion of the explained variation by a linear regression model in the total variation.
Probability density functions
Pairwise independence
Coefficient of determination
Qualitative variable
40. 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
Conditional probability
A population or statistical population
Greek letters
inferential statistics
41. 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
Type I errors & Type II errors
Descriptive statistics
Step 1 of a statistical experiment
Probability
42. 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).
Independent Selection
Pairwise independence
Statistic
An event
43. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
A probability density function
Pairwise independence
Placebo effect
Law of Large Numbers
44. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
covariance of X and Y
Law of Large Numbers
Bias
The standard deviation
45. Data are gathered and correlations between predictors and response are investigated.
Correlation
observational study
A Random vector
Valid measure
46. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
the population mean
Parameter - or 'statistical parameter'
A probability density function
The standard deviation
47. To find the average - or arithmetic mean - of a set of numbers:
Residuals
A Distribution function
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Divide the sum by the number of values.
48. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Type 1 Error
A sampling distribution
Correlation
The median value
49. Some commonly used symbols for sample statistics
applied statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A data point
The variance of a random variable
50. Rejecting a true null hypothesis.
Type 1 Error
Prior probability
Sampling frame
Dependent Selection