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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. 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}.
A statistic
The sample space
Variable
Conditional probability
2. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
observational study
Power of a test
Qualitative variable
3. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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4. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
An estimate of a parameter
A Random vector
s-algebras
applied statistics
5. Gives the probability of events in a probability space.
Simpson's Paradox
the population mean
Correlation
A Probability measure
6. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Joint probability
Sampling Distribution
Marginal probability
Null hypothesis
7. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Simple random sample
Law of Large Numbers
Independent Selection
Standard error
8. 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.
expected value of X
That value is the median value
Parameter
Joint probability
9. Another name for elementary event.
Seasonal effect
Atomic event
applied statistics
Conditional probability
10. Is used to describe probability in a continuous probability distribution. For example - you can't say that the probability of a man being six feet tall is 20% - but you can say he has 20% of chances of being between five and six feet tall. Probabilit
categorical variables
Binary data
A Distribution function
Probability density
11. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Prior probability
Valid measure
Outlier
12.
Inferential statistics
the population mean
Simple random sample
covariance of X and Y
13. Describes a characteristic of an individual to be measured or observed.
f(z) - and its cdf by F(z).
Descriptive
A Probability measure
Variable
14. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
The median value
The average - or arithmetic mean
Sampling
15. A numerical measure that describes an aspect of a population.
the population cumulants
A sampling distribution
The variance of a random variable
Parameter
16. The standard deviation of a sampling distribution.
Atomic event
Correlation
Descriptive statistics
Standard error
17. Two variables such that their effects on the response variable cannot be distinguished from each other.
Credence
Sampling Distribution
Confounded variables
Reliable measure
18. When you have two or more competing models - choose the simpler of the two models.
The median value
Valid measure
Law of Parsimony
the population correlation
19. 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 probability density function
Statistical adjustment
Statistics
Statistical inference
20. 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
A sampling distribution
Placebo effect
Probability
nominal - ordinal - interval - and ratio
21. Data are gathered and correlations between predictors and response are investigated.
expected value of X
observational study
Null hypothesis
Greek letters
22. Probability of accepting a false null hypothesis.
Bias
Dependent Selection
Beta value
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
23. Is its expected value. The mean (or sample mean of a data set is just the average value.
Nominal measurements
Law of Large Numbers
The Mean of a random variable
Marginal probability
24. Is a parameter that indexes a family of probability distributions.
covariance of X and Y
The variance of a random variable
Statistics
A Statistical parameter
25. (cdfs) are denoted by upper case letters - e.g. F(x).
A probability density function
Cumulative distribution functions
expected value of X
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
26. 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.
Kurtosis
inferential statistics
Seasonal effect
An event
27. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Parameter - or 'statistical parameter'
s-algebras
An Elementary event
quantitative variables
28. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
The average - or arithmetic mean
Marginal probability
Binomial experiment
s-algebras
29. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Count data
Statistical inference
variance of X
30. 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.
Type I errors
Marginal distribution
Step 1 of a statistical experiment
Interval measurements
31. E[X] :
the population correlation
Null hypothesis
experimental studies and observational studies.
expected value of X
32. 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
Simulation
Observational study
A Statistical parameter
Standard error
33. A subjective estimate of probability.
Statistics
Credence
Type 2 Error
Sampling
34. Have both a meaningful zero value and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data
Conditional probability
Ratio measurements
Step 3 of a statistical experiment
Bias
35. Is data that can take only two values - usually represented by 0 and 1.
That is the median value
Marginal probability
Sampling
Binary data
36. ?r
the population cumulants
Inferential statistics
Bias
Credence
37. S^2
The Expected value
Inferential
the population variance
quantitative variables
38. The collection of all possible outcomes in an experiment.
Bias
Sample space
Mutual independence
A Statistical parameter
39. 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.
experimental studies and observational studies.
A probability density function
applied statistics
An experimental study
40. 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.
descriptive statistics
hypotheses
Count data
Estimator
41. 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
hypotheses
Alpha value (Level of Significance)
The Mean of a random variable
experimental studies and observational studies.
42. 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 Distribution function
A random variable
Joint distribution
Likert scale
43. 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
Pairwise independence
Skewness
Sampling frame
s-algebras
44. 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
Joint distribution
Sampling
Probability and statistics
45. 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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46. Is a sample space over which a probability measure has been defined.
Probability
The Expected value
A probability space
Outlier
47. 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.
Type 1 Error
variance of X
That value is the median value
Dependent Selection
48. Have imprecise differences between consecutive values - but have a meaningful order to those values
Count data
Ordinal measurements
Average and arithmetic mean
Estimator
49. Have no meaningful rank order among values.
variance of X
Sampling
Nominal measurements
Atomic event
50. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
the population mean
Statistical inference
A Distribution function