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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
Start Test
Study First
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. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Statistic
Greek letters
Independent Selection
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
2. 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.
Simulation
Type I errors
Marginal distribution
Skewness
3. 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.
Type II errors
Binary data
Marginal distribution
Seasonal effect
4. 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.
Standard error
An experimental study
experimental studies and observational studies.
A likelihood function
5. Is data that can take only two values - usually represented by 0 and 1.
Marginal probability
Interval measurements
A statistic
Binary data
6. A variable describes an individual by placing the individual into a category or a group.
The Range
Qualitative variable
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
categorical variables
7. 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
A sample
Inferential statistics
hypotheses
The variance of a random variable
8. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Inferential statistics
The standard deviation
A likelihood function
An Elementary event
9. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Independence or Statistical independence
Law of Parsimony
Estimator
Law of Large Numbers
10. 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.
The median value
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Type I errors & Type II errors
Mutual independence
11. 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.
Probability density
Marginal probability
Estimator
That value is the median value
12. 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
Descriptive
Bias
Observational study
Step 3 of a statistical experiment
13. 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.
Independence or Statistical independence
Step 1 of a statistical experiment
Dependent Selection
Marginal probability
14. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
A probability distribution
The Range
Greek letters
15. 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
Probability density functions
the sample or population mean
Kurtosis
16. To find the average - or arithmetic mean - of a set of numbers:
Ordinal measurements
Divide the sum by the number of values.
Type I errors
The sample space
17. 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
Experimental and observational studies
Step 3 of a statistical experiment
Null hypothesis
The variance of a random variable
18. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
Kurtosis
Estimator
the population mean
19. Are simply two different terms for the same thing. Add the given values
The Range
Average and arithmetic mean
observational study
Atomic event
20. Failing to reject a false null hypothesis.
Probability and statistics
the population mean
Type 2 Error
A probability distribution
21. 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.
Simple random sample
A data point
Likert scale
Average and arithmetic mean
22. 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.
Atomic event
Statistical inference
the population correlation
Estimator
23. 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)
A Statistical parameter
Conditional probability
Interval measurements
An event
24. Where the null hypothesis is falsely rejected giving a 'false positive'.
The Expected value
Type I errors
Credence
Greek letters
25. 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).
The Mean of a random variable
An event
hypotheses
Independent Selection
26. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
The average - or arithmetic mean
A statistic
categorical variables
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
27. When there is an even number of values...
Seasonal effect
Beta value
That is the median value
A probability distribution
28. Another name for elementary event.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Ratio measurements
Prior probability
Atomic event
29. Is a set of entities about which statistical inferences are to be drawn - often based on random sampling. One can also talk about a population of measurements or values.
A population or statistical population
Dependent Selection
Type 1 Error
Probability density
30. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Placebo effect
variance of X
Statistical dispersion
A Statistical parameter
31. ?
Nominal measurements
Type 2 Error
Placebo effect
the population correlation
32. Rejecting a true null hypothesis.
Probability and statistics
The variance of a random variable
Type 1 Error
A sampling distribution
33. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential statistics
quantitative variables
An experimental study
Inferential
34. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
P-value
A Statistical parameter
experimental studies and observational studies.
35. In particular - the pdf of the standard normal distribution is denoted by
observational study
A Statistical parameter
f(z) - and its cdf by F(z).
Residuals
36. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
Statistical inference
Type I errors & Type II errors
Sampling frame
37. Var[X] :
Placebo effect
Joint distribution
Likert scale
variance of X
38. 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
Prior probability
Joint probability
Random variables
39. Any specific experimental condition applied to the subjects
the population mean
Sample space
Ordinal measurements
Treatment
40. 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
Seasonal effect
Independence or Statistical independence
Greek letters
Sampling
41. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Marginal probability
A probability distribution
Law of Parsimony
quantitative variables
42. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
A statistic
hypothesis
Statistical inference
P-value
43. Describes the spread in the values of the sample statistic when many samples are taken.
Standard error
A sampling distribution
Parameter - or 'statistical parameter'
Variability
44. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Statistical dispersion
Independent Selection
Individual
Beta value
45. 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'
Type 2 Error
Type I errors
Conditional probability
nominal - ordinal - interval - and ratio
46. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.
Kurtosis
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
the population mean
The standard deviation
47. Also called correlation coefficient - is a numeric measure of the strength of linear relationship between two random variables (one can use it to quantify - for example - how shoe size and height are correlated in the population). An example is the P
Correlation
Step 3 of a statistical experiment
Step 2 of a statistical experiment
Conditional distribution
48. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Mutual independence
quantitative variables
Dependent Selection
Statistical adjustment
49. Some commonly used symbols for sample statistics
Ordinal measurements
P-value
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Count data
50. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Greek letters
descriptive statistics
An Elementary event
A sampling distribution