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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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clep
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Instructions:
Answer 50 questions in 15 minutes.
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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 a parameter that indexes a family of probability distributions.
Marginal distribution
A Statistical parameter
Statistics
A random variable
2. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Descriptive statistics
Credence
Type II errors
Probability
3. 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
Ratio measurements
Simulation
Step 3 of a statistical experiment
A sampling distribution
4. Describes a characteristic of an individual to be measured or observed.
Variable
the sample or population mean
Step 2 of a statistical experiment
A sample
5. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
A population or statistical population
Binary data
Experimental and observational studies
6. Is a sample and the associated data points.
A Statistical parameter
Probability
A data set
Parameter - or 'statistical parameter'
7. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)
s-algebras
Simulation
Statistical adjustment
Mutual independence
8. Any specific experimental condition applied to the subjects
the sample or population mean
An experimental study
Treatment
Sampling Distribution
9. The proportion of the explained variation by a linear regression model in the total variation.
observational study
the population mean
Coefficient of determination
Conditional probability
10. Long-term upward or downward movement over time.
The median value
the population variance
the population cumulants
Trend
11. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
The average - or arithmetic mean
Correlation
Descriptive statistics
Statistical dispersion
12. Data are gathered and correlations between predictors and response are investigated.
Ratio measurements
Statistics
observational study
The average - or arithmetic mean
13. 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.
Type I errors
Atomic event
the population mean
Statistical inference
14. 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.
Experimental and observational studies
Residuals
covariance of X and Y
An Elementary event
15. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Qualitative variable
Probability and statistics
Treatment
16. Some commonly used symbols for population parameters
Probability density functions
Law of Large Numbers
A probability density function
the population mean
17. The result of a Bayesian analysis that encapsulates the combination of prior beliefs or information with observed data
Sampling
Correlation coefficient
variance of X
Posterior probability
18. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Correlation
A probability distribution
the population variance
Posterior probability
19. The probability of correctly detecting a false null hypothesis.
methods of least squares
Power of a test
Count data
Treatment
20. 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.
That value is the median value
Sampling
Statistical adjustment
hypotheses
21. 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 random variable
Mutual independence
hypotheses
Placebo effect
22. Have no meaningful rank order among values.
Type I errors & Type II errors
Nominal measurements
the population cumulants
Step 1 of a statistical experiment
23. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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24. Cov[X - Y] :
observational study
covariance of X and Y
Dependent Selection
Null hypothesis
25. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Correlation coefficient
Step 1 of a statistical experiment
The sample space
26. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
Correlation coefficient
covariance of X and Y
An estimate of a parameter
27. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Binomial experiment
descriptive statistics
Prior probability
A Statistical parameter
28. Rejecting a true null hypothesis.
Type 1 Error
Random variables
Binomial experiment
Outlier
29. 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
An event
Observational study
Type 2 Error
Statistical adjustment
30. 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).
Reliable measure
Ordinal measurements
An event
Type I errors
31. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Greek letters
Lurking variable
methods of least squares
the population correlation
32. 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
Probability and statistics
Descriptive statistics
Sample space
A probability distribution
33. (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.
An Elementary event
Probability
Cumulative distribution functions
A data point
34. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Null hypothesis
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Alpha value (Level of Significance)
applied statistics
35. 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
Law of Parsimony
Correlation
Beta value
variance of X
36. 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.
A population or statistical population
Likert scale
The sample space
An experimental study
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
descriptive statistics
Probability
Individual
Standard error
38. A numerical measure that describes an aspect of a population.
A Distribution function
Beta value
Parameter
Type I errors
39. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Probability
f(z) - and its cdf by F(z).
A sampling distribution
The Covariance between two random variables X and Y - with expected values E(X) =
40. A subjective estimate of probability.
the population mean
Type 2 Error
Credence
That is the median value
41. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
Posterior probability
Type 1 Error
A sampling distribution
42. The standard deviation of a sampling distribution.
Type I errors
Likert scale
Valid measure
Standard error
43. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
A probability space
A Statistical parameter
Binomial experiment
applied statistics
44. Is a sample space over which a probability measure has been defined.
Simulation
Probability density functions
Step 3 of a statistical experiment
A probability space
45. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Simulation
Pairwise independence
P-value
Bias
46. Probability of accepting a false null hypothesis.
Beta value
Conditional distribution
Count data
Law of Parsimony
47. 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.
Law of Parsimony
A population or statistical population
Type 2 Error
Random variables
48. A data value that falls outside the overall pattern of the graph.
Seasonal effect
Outlier
Reliable measure
Ratio measurements
49. 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.
Conditional distribution
Kurtosis
Marginal probability
Trend
50. Gives the probability distribution for a continuous random variable.
Probability density functions
Statistics
A probability density function
A likelihood function