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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. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Type I errors & Type II errors
Variable
The standard deviation
2. When you have two or more competing models - choose the simpler of the two models.
Type II errors
Law of Parsimony
Standard error
Cumulative distribution functions
3. The proportion of the explained variation by a linear regression model in the total variation.
A population or statistical population
Likert scale
the population cumulants
Coefficient of determination
4. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
Likert scale
Conditional probability
Lurking variable
5. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
A probability distribution
s-algebras
Lurking variable
The standard deviation
6. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Placebo effect
The median value
An estimate of a parameter
7. 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
Probability density
A likelihood function
Statistic
Particular realizations of a random variable
8. 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).
Residuals
Joint probability
Type I errors
Cumulative distribution functions
9. 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).
An event
Simpson's Paradox
Marginal distribution
Placebo effect
10. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Prior probability
Inferential
A data set
11. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
A probability density function
Mutual independence
The variance of a random variable
12. To find the average - or arithmetic mean - of a set of numbers:
That is the median value
A data set
Divide the sum by the number of values.
Joint probability
13. 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
Ratio measurements
Alpha value (Level of Significance)
A probability distribution
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.
Estimator
Coefficient of determination
Trend
Simpson's Paradox
15. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
Correlation coefficient
Sample space
Trend
16. 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
Type II errors
Skewness
Type 1 Error
descriptive statistics
17. (cdfs) are denoted by upper case letters - e.g. F(x).
Probability density
Cumulative distribution functions
hypothesis
A data set
18. 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
The Covariance between two random variables X and Y - with expected values E(X) =
P-value
Correlation
The sample space
19. A list of individuals from which the sample is actually selected.
Placebo effect
Joint distribution
An experimental study
Sampling frame
20. A common goal for a statistical research project is to investigate causality - and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables or response.
A Statistical parameter
Experimental and observational studies
Credence
Valid measure
21. A variable describes an individual by placing the individual into a category or a group.
Bias
Qualitative variable
Type 1 Error
A random variable
22. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Likert scale
Descriptive statistics
Statistical inference
categorical variables
23. 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.
Nominal measurements
Statistical inference
The sample space
Standard error
24. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Nominal measurements
A sampling distribution
A data set
A sample
25. A data value that falls outside the overall pattern of the graph.
Outlier
Step 2 of a statistical experiment
P-value
Cumulative distribution functions
26. Is data arising from counting that can take only non-negative integer values.
The Covariance between two random variables X and Y - with expected values E(X) =
Alpha value (Level of Significance)
A random variable
Count data
27. 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'
Conditional probability
Variability
That value is the median value
Conditional distribution
28. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Power of a test
Statistical dispersion
Step 2 of a statistical experiment
categorical variables
29. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
The sample space
Joint distribution
Conditional probability
Correlation
30. Some commonly used symbols for sample statistics
applied statistics
Bias
Beta value
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
31. Is a sample and the associated data points.
Interval measurements
A data set
Seasonal effect
variance of X
32. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Simple random sample
Type 1 Error
Seasonal effect
Quantitative variable
33. 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)
Interval measurements
A Distribution function
Type II errors
Binomial experiment
34. A measurement such that the random error is small
The sample space
Reliable measure
The variance of a random variable
Divide the sum by the number of values.
35. Is the length of the smallest interval which contains all the data.
hypothesis
applied statistics
The average - or arithmetic mean
The Range
36. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
Marginal probability
Mutual independence
Alpha value (Level of Significance)
37. Statistical methods can be used for summarizing or describing a collection of data; this is called
Sampling
descriptive statistics
An estimate of a parameter
categorical variables
38. 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.
The median value
Sampling
Count data
experimental studies and observational studies.
39. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
the population mean
A sample
Observational study
40. Are usually written in upper case roman letters: X - Y - etc.
An event
Ratio measurements
Random variables
Type II errors
41. Gives the probability of events in a probability space.
covariance of X and Y
Sampling frame
Posterior probability
A Probability measure
42. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
methods of least squares
Step 2 of a statistical experiment
A data point
Conditional distribution
43. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Parameter - or 'statistical parameter'
An Elementary event
Step 2 of a statistical experiment
nominal - ordinal - interval - and ratio
44. In particular - the pdf of the standard normal distribution is denoted by
A Random vector
f(z) - and its cdf by F(z).
Sampling Distribution
Individual
45. 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
Likert scale
Parameter
The standard deviation
Null hypothesis
46. 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.
the population mean
Bias
Block
nominal - ordinal - interval - and ratio
47. Probability of accepting a false null hypothesis.
Beta value
Mutual independence
nominal - ordinal - interval - and ratio
An Elementary event
48. Are simply two different terms for the same thing. Add the given values
Correlation
Variability
Null hypothesis
Average and arithmetic mean
49. (or atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.
Likert scale
Dependent Selection
Variable
An Elementary event
50. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
An Elementary event
That is the median value
Mutual independence
Individual