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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.
the population mean
Interval measurements
An event
A sample
2. A subjective estimate of probability.
methods of least squares
Simple random sample
Treatment
Credence
3. ?r
Cumulative distribution functions
Type II errors
Probability and statistics
the population cumulants
4. E[X] :
expected value of X
Independent Selection
An Elementary event
Outlier
5. A collection of events is mutually independent if for any subset of the collection - the joint probability of all events occurring is equal to the product of the joint probabilities of the individual events. Think of the result of a series of coin-fl
Mutual independence
Statistical dispersion
applied statistics
the population variance
6. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
The variance of a random variable
categorical variables
Average and arithmetic mean
Law of Large Numbers
7. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Step 3 of a statistical experiment
Particular realizations of a random variable
That value is the median value
A likelihood function
8. 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.
Confounded variables
expected value of X
Dependent Selection
Experimental and observational studies
9. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
A data point
A sampling distribution
Count data
Ratio measurements
10. The collection of all possible outcomes in an experiment.
A Random vector
Bias
A probability space
Sample space
11. Samples are drawn from two different populations such that the sample data drawn from one population is completely unrelated to the selection of sample data from the other population.
Experimental and observational studies
Independent Selection
the population variance
A probability distribution
12. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Valid measure
Statistical dispersion
hypotheses
Inferential statistics
13. Have no meaningful rank order among values.
Treatment
Standard error
Nominal measurements
Descriptive statistics
14. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Statistical adjustment
hypothesis
Sampling Distribution
Independence or Statistical independence
15. Are simply two different terms for the same thing. Add the given values
Simple random sample
hypothesis
Average and arithmetic mean
Standard error
16. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Prior probability
experimental studies and observational studies.
Inferential
the sample or population mean
17. 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.
the population cumulants
Outlier
Beta value
Experimental and observational studies
18. 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.
Outlier
Descriptive
Seasonal effect
Valid measure
19. (cdfs) are denoted by upper case letters - e.g. F(x).
Reliable measure
Cumulative distribution functions
quantitative variables
applied statistics
20. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Marginal probability
Placebo effect
Inferential
Step 3 of a statistical experiment
21. Describes a characteristic of an individual to be measured or observed.
Probability
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Parameter
Variable
22. Probability of accepting a false null hypothesis.
Beta value
methods of least squares
A data set
Sampling frame
23. Is its expected value. The mean (or sample mean of a data set is just the average value.
The Mean of a random variable
Type 1 Error
An event
Probability density functions
24. Is a sample space over which a probability measure has been defined.
Variable
Simple random sample
A probability space
Type I errors & Type II errors
25. Some commonly used symbols for population parameters
Skewness
the population mean
Greek letters
the population cumulants
26. There are two major types of causal statistical studies: In both types of studies - the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies
experimental studies and observational studies.
Confounded variables
Conditional probability
Lurking variable
27. Are two related but separate academic disciplines. Statistical analysis often uses probability distributions - and the two topics are often studied together. However - probability theory contains much that is of mostly of mathematical interest and no
Step 3 of a statistical experiment
Probability and statistics
A data point
That is the median value
28. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
Statistics
The Mean of a random variable
Credence
29. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Descriptive
Step 1 of a statistical experiment
Type 1 Error
quantitative variables
30. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
A statistic
A likelihood function
A Random vector
Placebo effect
31. Describes the spread in the values of the sample statistic when many samples are taken.
Binomial experiment
Variability
inferential statistics
Sampling frame
32. Cov[X - Y] :
s-algebras
Simpson's Paradox
covariance of X and Y
Variable
33. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Joint distribution
Kurtosis
Likert scale
Probability and statistics
34. 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
Bias
Qualitative variable
Step 3 of a statistical experiment
the population variance
35. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
The Covariance between two random variables X and Y - with expected values E(X) =
descriptive statistics
applied statistics
Greek letters
36. A measurement such that the random error is small
Reliable measure
Probability and statistics
A statistic
s-algebras
37. A numerical measure that describes an aspect of a population.
The Mean of a random variable
Correlation
Parameter
Statistic
38. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Marginal probability
A probability density function
Outlier
Quantitative variable
39. To find the average - or arithmetic mean - of a set of numbers:
Sampling frame
Divide the sum by the number of values.
Joint distribution
The median value
40. Any specific experimental condition applied to the subjects
Residuals
Posterior probability
Treatment
Probability
41. 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
Statistical adjustment
Probability density
inferential statistics
That is the median value
42. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Ordinal measurements
Bias
Descriptive
Random variables
43. Var[X] :
Conditional distribution
variance of X
the population variance
Sample space
44. 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
An experimental study
Simpson's Paradox
Null hypothesis
Average and arithmetic mean
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. Failing to reject a false null hypothesis.
Block
A likelihood function
Type 2 Error
Statistical adjustment
47. (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.
observational study
Count data
Binary data
An Elementary event
48. Summarize the population data by describing what was observed in the sample numerically or graphically. Numerical descriptors include mean and standard deviation for continuous data types (like heights or weights) - while frequency and percentage are
Descriptive statistics
Estimator
Marginal distribution
Type II errors
49. In particular - the pdf of the standard normal distribution is denoted by
Likert scale
covariance of X and Y
f(z) - and its cdf by F(z).
Prior probability
50. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Ratio measurements
A data point
inferential statistics
Prior probability