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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. Any specific experimental condition applied to the subjects
Greek letters
A population or statistical population
Treatment
A Random vector
2. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Statistical dispersion
Credence
the sample or population mean
The Mean of a random variable
3. Is its expected value. The mean (or sample mean of a data set is just the average value.
Coefficient of determination
A statistic
The Mean of a random variable
Type I errors & Type II errors
4. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Lurking variable
Independence or Statistical independence
An estimate of a parameter
Probability density
5. S^2
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
the population variance
Divide the sum by the number of values.
covariance of X and Y
6. Gives the probability of events in a probability space.
Joint distribution
Alpha value (Level of Significance)
Ordinal measurements
A Probability measure
7. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
That value is the median value
Joint probability
Observational study
8. Is the probability distribution - under repeated sampling of the population - of a given statistic.
The average - or arithmetic mean
A data point
A probability space
A sampling distribution
9. 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
Count data
An Elementary event
A sample
Null hypothesis
10. 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
Beta value
Ratio measurements
Step 2 of a statistical experiment
11. 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.
quantitative variables
Sampling
Probability and statistics
applied statistics
12. The proportion of the explained variation by a linear regression model in the total variation.
A data point
applied statistics
Coefficient of determination
Quantitative variable
13. 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
A random variable
Independence or Statistical independence
The variance of a random variable
A probability space
14. 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.
Null hypothesis
Estimator
Ratio measurements
A sample
15. 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
A probability density function
A Random vector
16. Is a sample space over which a probability measure has been defined.
Valid measure
Conditional distribution
A probability space
Estimator
17. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Nominal measurements
Parameter
methods of least squares
Inferential
18. 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
Residuals
Type I errors & Type II errors
A statistic
Skewness
19. 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.
Statistical inference
Correlation
Lurking variable
The average - or arithmetic mean
20. Are usually written in upper case roman letters: X - Y - etc.
variance of X
nominal - ordinal - interval - and ratio
Random variables
Null hypothesis
21. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Likert scale
Qualitative variable
s-algebras
Posterior probability
22. The probability of correctly detecting a false null hypothesis.
Confounded variables
A data set
Probability
Power of a test
23. Var[X] :
Quantitative variable
Inferential statistics
variance of X
An Elementary event
24. Is a parameter that indexes a family of probability distributions.
A Statistical parameter
Descriptive statistics
The variance of a random variable
Block
25. 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.
Independence or Statistical independence
Statistics
Variable
the sample or population mean
26. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
quantitative variables
Likert scale
Statistics
Ordinal measurements
27. Have no meaningful rank order among values.
The sample space
Nominal measurements
Residuals
Law of Large Numbers
28. ?r
the population cumulants
Descriptive
The Expected value
Inferential
29. Describes a characteristic of an individual to be measured or observed.
The Range
Variable
The Mean of a random variable
Alpha value (Level of Significance)
30. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Nominal measurements
A random variable
Greek letters
The average - or arithmetic mean
31. Failing to reject a false null hypothesis.
Sampling
experimental studies and observational studies.
Treatment
Type 2 Error
32. 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
Sampling frame
Sampling Distribution
s-algebras
Ratio measurements
33. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Valid measure
Marginal distribution
Type II errors
An estimate of a parameter
34. 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).
Descriptive statistics
An event
expected value of X
That value is the median value
35. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
Bias
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
the population mean
The Expected value
36. 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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37. 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
Binary data
Greek letters
Probability
Statistical adjustment
38. 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
That is the median value
Correlation
Dependent Selection
Correlation coefficient
39. (or just likelihood) is a conditional probability function considered a function of its second argument with its first argument held fixed. For example - imagine pulling a numbered ball with the number k from a bag of n balls - numbered 1 to n. Then
Alpha value (Level of Significance)
A probability space
Seasonal effect
A likelihood function
40. 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.
Individual
Statistics
A population or statistical population
Credence
41. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
Correlation
Individual
Bias
42. 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
Descriptive statistics
Conditional distribution
Alpha value (Level of Significance)
43. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Step 3 of a statistical experiment
Type I errors
Likert scale
The Range
44. Is a sample and the associated data points.
Residuals
A data set
A Distribution function
Type I errors & Type II errors
45. A group of individuals sharing some common features that might affect the treatment.
the population correlation
Step 2 of a statistical experiment
Probability and statistics
Block
46. A numerical measure that assesses the strength of a linear relationship between two variables.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Correlation coefficient
Inferential statistics
covariance of X and Y
47. 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.
the population cumulants
Statistic
Seasonal effect
Probability density
48. Is data that can take only two values - usually represented by 0 and 1.
Nominal measurements
Binary data
Average and arithmetic mean
the population mean
49. 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.
A probability distribution
Reliable measure
Probability density functions
That value is the median value
50. Cov[X - Y] :
Skewness
A Probability measure
Divide the sum by the number of values.
covariance of X and Y