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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 data arising from counting that can take only non-negative integer values.
Experimental and observational studies
Count data
quantitative variables
Type I errors & Type II errors
2. 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 sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Nominal measurements
Ordinal measurements
The median value
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
Valid measure
Step 3 of a statistical experiment
Bias
Binary data
4. 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.
Treatment
Sampling
the population mean
Prior probability
5. 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).
Parameter
Statistical adjustment
Residuals
An event
6. Is denoted by - pronounced 'x bar'.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Step 3 of a statistical experiment
Independence or Statistical independence
Simpson's Paradox
7. Is its expected value. The mean (or sample mean of a data set is just the average value.
Outlier
The Range
Conditional distribution
The Mean of a random variable
8. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Interval measurements
nominal - ordinal - interval - and ratio
Reliable measure
A Statistical parameter
9. 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
Skewness
Independent Selection
inferential statistics
Probability
10. 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.
Seasonal effect
the population variance
Credence
Sampling
11. Gives the probability of events in a probability space.
Prior probability
Count data
A Probability measure
experimental studies and observational studies.
12. Is a measure of its statistical dispersion - indicating how far from the expected value its values typically are. The variance of random variable X is typically designated as - - or simply s2.
The variance of a random variable
Statistical dispersion
The median value
Observational study
13. 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.
Valid measure
Step 1 of a statistical experiment
methods of least squares
Lurking variable
14. A numerical measure that describes an aspect of a sample.
Qualitative variable
Experimental and observational studies
Nominal measurements
Statistic
15. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
A sample
quantitative variables
experimental studies and observational studies.
Statistic
16. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
A Probability measure
Simulation
Outlier
Binomial experiment
17. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Observational study
Descriptive statistics
applied statistics
Greek letters
18. Is a sample space over which a probability measure has been defined.
Sample space
Divide the sum by the number of values.
Independence or Statistical independence
A probability space
19. (cdfs) are denoted by upper case letters - e.g. F(x).
Sampling
Cumulative distribution functions
A probability density function
Inferential
20. 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
s-algebras
Statistical adjustment
Correlation
Probability density
21. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
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22. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
experimental studies and observational studies.
observational study
Joint distribution
23. A group of individuals sharing some common features that might affect the treatment.
Block
An event
Interval measurements
A data point
24. Long-term upward or downward movement over time.
A data point
Trend
A random variable
The standard deviation
25. (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
A Statistical parameter
Coefficient of determination
That is the median value
26. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Ordinal measurements
A statistic
Type 2 Error
the sample or population mean
27. Probability of rejecting a true null hypothesis.
Simulation
Alpha value (Level of Significance)
experimental studies and observational studies.
Nominal measurements
28. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
P-value
Kurtosis
applied statistics
Placebo effect
29. 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
A data set
Treatment
Trend
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.
Sampling Distribution
Statistical dispersion
Probability density functions
categorical variables
31. To find the average - or arithmetic mean - of a set of numbers:
Probability and statistics
Bias
Divide the sum by the number of values.
Qualitative variable
32. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
experimental studies and observational studies.
Mutual independence
the sample or population mean
Ratio measurements
33. A subjective estimate of probability.
Statistical inference
Qualitative variable
Credence
Confounded variables
34. Statistical methods can be used for summarizing or describing a collection of data; this is called
Independence or Statistical independence
Kurtosis
descriptive statistics
the population mean
35. ?r
the population cumulants
Probability density functions
An estimate of a parameter
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
36. 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
the population mean
Statistical inference
Step 2 of a statistical experiment
experimental studies and observational studies.
37. Any specific experimental condition applied to the subjects
Treatment
the population cumulants
Estimator
Type 1 Error
38. Is defined as the expected value of random variable (X -
Interval measurements
Type I errors & Type II errors
Simpson's Paradox
The Covariance between two random variables X and Y - with expected values E(X) =
39. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
Seasonal effect
The sample space
Probability
Beta value
40. Is a sample and the associated data points.
Independence or Statistical independence
Lurking variable
Posterior probability
A data set
41. 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
A Random vector
covariance of X and Y
Correlation
Nominal measurements
42. Two variables such that their effects on the response variable cannot be distinguished from each other.
Outlier
Independent Selection
Statistic
Confounded variables
43. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
experimental studies and observational studies.
Individual
Variable
Inferential
44. Is data that can take only two values - usually represented by 0 and 1.
Binary data
Sampling Distribution
Independence or Statistical independence
Variability
45. The collection of all possible outcomes in an experiment.
Sample space
Qualitative variable
Statistical dispersion
Seasonal effect
46. Failing to reject a false null hypothesis.
Type 2 Error
categorical variables
Type I errors
A Random vector
47. Many statistical methods seek to minimize the mean-squared error - and these are called
Cumulative distribution functions
Individual
Treatment
methods of least squares
48. To prove the guiding theory further - these predictions are tested as well - as part of the scientific method. If the inference holds true - then the descriptive statistics of the new data increase the soundness of that
A statistic
Lurking variable
hypothesis
the population mean
49. Are usually written in upper case roman letters: X - Y - etc.
Atomic event
Random variables
Sampling
Statistical inference
50. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Cumulative distribution functions
Qualitative variable
Standard error
categorical variables