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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. 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.
expected value of X
A population or statistical population
experimental studies and observational studies.
Statistical adjustment
2. 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).
Independent Selection
Joint probability
Sampling Distribution
A statistic
3. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
An Elementary event
Probability
Simpson's Paradox
4. 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.
covariance of X and Y
Lurking variable
The variance of a random variable
Conditional distribution
5. ?r
inferential statistics
Skewness
covariance of X and Y
the population cumulants
6. When there is an even number of values...
Sampling
inferential statistics
That is the median value
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
7. 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.
Conditional distribution
hypothesis
An experimental study
Type I errors
8. The probability of correctly detecting a false null hypothesis.
Type 2 Error
Power of a test
Sampling
A Statistical parameter
9. Var[X] :
Qualitative variable
variance of X
A Distribution function
quantitative variables
10. A numerical measure that describes an aspect of a sample.
Variable
Likert scale
Conditional distribution
Statistic
11. 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
Probability
Estimator
methods of least squares
The average - or arithmetic mean
12. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
Credence
Likert scale
applied statistics
Statistical inference
13. Have imprecise differences between consecutive values - but have a meaningful order to those values
Statistics
Reliable measure
Ordinal measurements
Type I errors & Type II errors
14. 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
Joint distribution
Sampling
Correlation
15. Statistical methods can be used for summarizing or describing a collection of data; this is called
categorical variables
descriptive statistics
Particular realizations of a random variable
A sampling distribution
16. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
Type II errors
A sampling distribution
Dependent Selection
17. 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
Seasonal effect
Descriptive statistics
Inferential statistics
A sampling distribution
18. When you have two or more competing models - choose the simpler of the two models.
Law of Parsimony
Probability
methods of least squares
Descriptive
19. E[X] :
Atomic event
expected value of X
A population or statistical population
Average and arithmetic mean
20. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
the population mean
Lurking variable
Pairwise independence
21. Is data that can take only two values - usually represented by 0 and 1.
A Statistical parameter
A probability distribution
Binary data
Parameter - or 'statistical parameter'
22. Given two jointly distributed random variables X and Y - the marginal distribution of X is simply the probability distribution of X ignoring information about Y.
Variable
A random variable
Statistic
Marginal distribution
23. Probability of rejecting a true null hypothesis.
Sample space
Observational study
Alpha value (Level of Significance)
Variable
24. Describes the spread in the values of the sample statistic when many samples are taken.
Probability density
Experimental and observational studies
Variability
An estimate of a parameter
25. 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
hypothesis
nominal - ordinal - interval - and ratio
Marginal distribution
Probability
26. Is the length of the smallest interval which contains all the data.
The Range
Statistical adjustment
Bias
Qualitative variable
27. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Joint distribution
Placebo effect
Correlation
the population correlation
28. 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)
Prior probability
Interval measurements
Reliable measure
Particular realizations of a random variable
29. In particular - the pdf of the standard normal distribution is denoted by
f(z) - and its cdf by F(z).
The variance of a random variable
the population correlation
Estimator
30. Is that part of a population which is actually observed.
Marginal distribution
A data set
A sample
Skewness
31. Two variables such that their effects on the response variable cannot be distinguished from each other.
Parameter
s-algebras
Confounded variables
descriptive statistics
32. Are usually written in upper case roman letters: X - Y - etc.
Type I errors & Type II errors
hypotheses
expected value of X
Random variables
33. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
Greek letters
Variable
the sample or population mean
Cumulative distribution functions
34. The proportion of the explained variation by a linear regression model in the total variation.
Coefficient of determination
Simple random sample
A population or statistical population
s-algebras
35. 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.
Qualitative variable
Independence or Statistical independence
That value is the median value
Beta value
36. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Type II errors
Confounded variables
Count data
Particular realizations of a random variable
37. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Parameter
Statistical inference
Lurking variable
A statistic
38. A numerical measure that assesses the strength of a linear relationship between two variables.
Correlation coefficient
Divide the sum by the number of values.
Variability
the population mean
39. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
the population mean
A data point
Particular realizations of a random variable
The variance of a random variable
40. Describes a characteristic of an individual to be measured or observed.
A Statistical parameter
Variable
Law of Parsimony
Posterior probability
41. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability distribution
Type I errors & Type II errors
Step 3 of a statistical experiment
Simpson's Paradox
42. 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.
Step 3 of a statistical experiment
Statistical inference
Count data
Statistic
43. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Independence or Statistical independence
Joint distribution
Pairwise independence
Binary data
44. Occurs when a subject receives no treatment - but (incorrectly) believes he or she is in fact receiving treatment and responds favorably.
An experimental study
Placebo effect
Probability
The Range
45. A sample selected in such a way that each individual is equally likely to be selected as well as any group of size n is equally likely to be selected.
Type II errors
Conditional distribution
Probability density
Simple random sample
46. Is its expected value. The mean (or sample mean of a data set is just the average value.
Inferential statistics
Statistical dispersion
The Mean of a random variable
Mutual independence
47. A list of individuals from which the sample is actually selected.
Marginal distribution
Conditional probability
A sampling distribution
Sampling frame
48. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.
P-value
Kurtosis
Probability density functions
A sampling distribution
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.
Marginal probability
Credence
A random variable
Type II errors
50. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Outlier
Sampling Distribution
Joint distribution
Prior probability