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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. (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
A likelihood function
Marginal probability
Coefficient of determination
The Range
2. 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.
Correlation
Sample space
A population or statistical population
hypothesis
3. Have no meaningful rank order among values.
the population mean
Null hypothesis
Conditional distribution
Nominal measurements
4. 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
Statistical inference
Ordinal measurements
Observational study
An Elementary event
5.
Simulation
variance of X
the population mean
A Random vector
6. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Sample space
A probability distribution
Bias
Alpha value (Level of Significance)
7. Rejecting a true null hypothesis.
Marginal distribution
A Distribution function
Mutual independence
Type 1 Error
8. 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.
Quantitative variable
Statistics
The variance of a random variable
Probability density functions
9. 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.
Inferential statistics
Independent Selection
Greek letters
Statistical dispersion
10. Have imprecise differences between consecutive values - but have a meaningful order to those values
Ordinal measurements
Probability
Standard error
Atomic event
11. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.
Simulation
Null hypothesis
An estimate of a parameter
A data set
12. A numerical facsimilie or representation of a real-world phenomenon.
Simulation
the population cumulants
Estimator
The Range
13. 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
The Expected value
Ratio measurements
covariance of X and Y
Step 3 of a statistical experiment
14. 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
Credence
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Independence or Statistical independence
experimental studies and observational studies.
15. Gives the probability distribution for a continuous random variable.
Statistical dispersion
A probability density function
A population or statistical population
A statistic
16. Some commonly used symbols for population parameters
A likelihood function
A random variable
the population mean
f(z) - and its cdf by F(z).
17. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Average and arithmetic mean
expected value of X
Type II errors
A probability space
18. 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.
Type 2 Error
Type II errors
Statistic
Sampling
19. 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.
Type II errors
The median value
Trend
Sample space
20. Is a sample space over which a probability measure has been defined.
Pairwise independence
A probability space
A likelihood function
A random variable
21. When you have two or more competing models - choose the simpler of the two models.
Probability
Parameter - or 'statistical parameter'
A population or statistical population
Law of Parsimony
22. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
A likelihood function
Sampling
the population mean
Binomial experiment
23. 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
Statistical dispersion
Sampling frame
A random variable
24. E[X] :
Binary data
Prior probability
Sampling frame
expected value of X
25. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
The variance of a random variable
Prior probability
A likelihood function
Bias
26. Is denoted by - pronounced 'x bar'.
Skewness
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Observational study
An event
27. Design of experiments - using blocking to reduce the influence of confounding variables - and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage - the experimenters a
Confounded variables
Step 2 of a statistical experiment
Standard error
Experimental and observational studies
28. Is data arising from counting that can take only non-negative integer values.
categorical variables
That is the median value
Descriptive statistics
Count data
29. Two variables such that their effects on the response variable cannot be distinguished from each other.
Confounded variables
A data point
Sampling frame
A probability space
30. 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.
Pairwise independence
A data point
the population variance
Experimental and observational studies
31. Can be - for example - the possible outcomes of a dice roll (but it is not assigned a value). The distribution function of a random variable gives the probability of different results. We can also derive the mean and variance of a random variable.
the population mean
A random variable
A population or statistical population
descriptive statistics
32. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
hypothesis
Credence
Residuals
The standard deviation
33. 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
Null hypothesis
A population or statistical population
Type 1 Error
The Expected value
34. A variable describes an individual by placing the individual into a category or a group.
Individual
Prior probability
An experimental study
Qualitative variable
35. Another name for elementary event.
Valid measure
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Likert scale
Atomic event
36. 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.
Variability
Joint probability
Ordinal measurements
That value is the median value
37. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
38. A group of individuals sharing some common features that might affect the treatment.
the sample or population mean
Block
Random variables
Simulation
39. ?r
A sample
the population cumulants
Likert scale
A likelihood function
40. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Probability
A data set
Statistic
41. 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
methods of least squares
s-algebras
Prior probability
42. 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
A sampling distribution
hypothesis
Sample space
Probability and statistics
43. 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 variance of a random variable
Block
descriptive statistics
44. Is the length of the smallest interval which contains all the data.
the population cumulants
the population variance
A probability density function
The Range
45. Describes the spread in the values of the sample statistic when many samples are taken.
A probability distribution
An Elementary event
Variability
Individual
46. 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.
Particular realizations of a random variable
The average - or arithmetic mean
Dependent Selection
hypotheses
47. 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
Particular realizations of a random variable
hypotheses
Power of a test
s-algebras
48. The collection of all possible outcomes in an experiment.
Likert scale
Sample space
Type I errors
The Covariance between two random variables X and Y - with expected values E(X) =
49. 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.
That value is the median value
Lurking variable
An estimate of a parameter
Joint distribution
50. Working from a null hypothesis two basic forms of error are recognized:
descriptive statistics
Type I errors & Type II errors
A statistic
Simple random sample