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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.
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. Probability of accepting a false null hypothesis.
The Covariance between two random variables X and Y - with expected values E(X) =
Mutual independence
Beta value
Statistical dispersion
2. 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.
The Covariance between two random variables X and Y - with expected values E(X) =
Binary data
Marginal probability
Probability
3. A numerical measure that describes an aspect of a population.
Probability
methods of least squares
Parameter
Probability and statistics
4. 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
The variance of a random variable
Dependent Selection
A Random vector
Skewness
5. 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
expected value of X
Type 1 Error
Statistical dispersion
Independence or Statistical independence
6. Of a group of numbers is the center point of all those number values.
Mutual independence
Beta value
The average - or arithmetic mean
Simpson's Paradox
7. 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
Simple random sample
Lurking variable
Step 1 of a statistical experiment
That is the median value
8. Where the null hypothesis is falsely rejected giving a 'false positive'.
A probability density function
Estimator
Ratio measurements
Type I errors
9. 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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10. 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
Observational study
f(z) - and its cdf by F(z).
An estimate of a parameter
Greek letters
11. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Sampling
Count data
Descriptive statistics
12. 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
That is the median value
Outlier
Null hypothesis
Credence
13. Many statistical methods seek to minimize the mean-squared error - and these are called
Seasonal effect
methods of least squares
That is the median value
Marginal distribution
14. 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
Null hypothesis
observational study
Bias
hypotheses
15. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
the sample or population mean
The sample space
the population variance
experimental studies and observational studies.
16. Have no meaningful rank order among values.
Binomial experiment
Nominal measurements
quantitative variables
the population cumulants
17. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Conditional distribution
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
The average - or arithmetic mean
the sample or population mean
18. A variable that has an important effect on the response variable and the relationship among the variables in a study but is not one of the explanatory variables studied either because it is unknown or not measured.
Descriptive
Lurking variable
Joint probability
descriptive statistics
19. Any specific experimental condition applied to the subjects
A Distribution function
Treatment
Estimator
Likert scale
20. Is its expected value. The mean (or sample mean of a data set is just the average value.
Joint distribution
The Mean of a random variable
Type II errors
Statistics
21. Is defined as the expected value of random variable (X -
Joint distribution
Random variables
Simple random sample
The Covariance between two random variables X and Y - with expected values E(X) =
22. The collection of all possible outcomes in an experiment.
Residuals
Descriptive
Sample space
Dependent Selection
23. (cdfs) are denoted by upper case letters - e.g. F(x).
the population mean
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Cumulative distribution functions
experimental studies and observational studies.
24. A numerical facsimilie or representation of a real-world phenomenon.
Individual
Simulation
Lurking variable
Statistics
25. Is data arising from counting that can take only non-negative integer values.
Confounded variables
Count data
Statistic
Divide the sum by the number of values.
26. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
That is the median value
Coefficient of determination
nominal - ordinal - interval - and ratio
Quantitative variable
27. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Variability
Particular realizations of a random variable
Statistical dispersion
the sample or population mean
28. Another name for elementary event.
Atomic event
A Statistical parameter
A sampling distribution
Type II errors
29. The proportion of the explained variation by a linear regression model in the total variation.
Cumulative distribution functions
the population mean
Step 2 of a statistical experiment
Coefficient of determination
30. Gives the probability of events in a probability space.
Divide the sum by the number of values.
A Probability measure
nominal - ordinal - interval - and ratio
Statistical inference
31. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A probability density function
A statistic
Statistical dispersion
A probability distribution
32. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Block
Experimental and observational studies
Probability density
categorical variables
33. When there is an even number of values...
Credence
Lurking variable
That is the median value
Law of Parsimony
34. Gives the probability distribution for a continuous random variable.
Seasonal effect
A likelihood function
Inferential
A probability density function
35. A subjective estimate of probability.
variance of X
Simpson's Paradox
Credence
A Probability measure
36. 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
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
the population mean
Bias
37. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Sample space
Descriptive statistics
Treatment
Law of Large Numbers
38. To find the average - or arithmetic mean - of a set of numbers:
A sample
hypotheses
The Covariance between two random variables X and Y - with expected values E(X) =
Divide the sum by the number of values.
39. A list of individuals from which the sample is actually selected.
The sample space
Sampling Distribution
Sampling frame
The variance of a random variable
40. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
Placebo effect
Posterior probability
Parameter - or 'statistical parameter'
Kurtosis
41. 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
Simple random sample
Skewness
Type II errors
42. Some commonly used symbols for population parameters
Law of Large Numbers
the population mean
Beta value
Probability density functions
43. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
A statistic
An Elementary event
Atomic event
P-value
44. Is data that can take only two values - usually represented by 0 and 1.
Binary data
Bias
Block
experimental studies and observational studies.
45. Is a parameter that indexes a family of probability distributions.
A likelihood function
Null hypothesis
A Statistical parameter
Step 2 of a statistical experiment
46. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Binomial experiment
covariance of X and Y
inferential statistics
Bias
47. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
A Random vector
nominal - ordinal - interval - and ratio
A sampling distribution
Prior probability
48. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Sampling Distribution
The sample space
Confounded variables
Seasonal effect
49. Is the length of the smallest interval which contains all the data.
The Mean of a random variable
expected value of X
The Range
Average and arithmetic mean
50. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Marginal distribution
the population cumulants
Binomial experiment