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Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
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Subjects
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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. (cdfs) are denoted by upper case letters - e.g. F(x).
P-value
Cumulative distribution functions
f(z) - and its cdf by F(z).
Ordinal measurements
2. 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.
Statistical dispersion
A statistic
categorical variables
Sampling
3. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
quantitative variables
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
applied statistics
Mutual independence
4. A group of individuals sharing some common features that might affect the treatment.
A Random vector
Block
Count data
Estimator
5. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Joint distribution
Probability and statistics
Inferential
nominal - ordinal - interval - and ratio
6. 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
A Distribution function
Individual
Mutual independence
the population variance
7. Are usually written in upper case roman letters: X - Y - etc.
Joint probability
Posterior probability
Variability
Random variables
8. Working from a null hypothesis two basic forms of error are recognized:
Mutual independence
Type I errors & Type II errors
The Expected value
The average - or arithmetic mean
9. 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.
Marginal distribution
Likert scale
An event
A random variable
10. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
The Mean of a random variable
Observational study
A statistic
Block
11. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Binary data
nominal - ordinal - interval - and ratio
observational study
Alpha value (Level of Significance)
12. 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
hypotheses
A random variable
Probability
Trend
13. (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
Divide the sum by the number of values.
Valid measure
A Statistical parameter
14. 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.
Quantitative variable
Average and arithmetic mean
the population cumulants
An experimental study
15. Are simply two different terms for the same thing. Add the given values
inferential statistics
Random variables
descriptive statistics
Average and arithmetic mean
16. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
That is the median value
the sample or population mean
Greek letters
Skewness
17. A subjective estimate of probability.
Step 2 of a statistical experiment
Treatment
observational study
Credence
18. 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.
A Distribution function
Marginal probability
Simple random sample
variance of X
19. Data are gathered and correlations between predictors and response are investigated.
Greek letters
An Elementary event
hypotheses
observational study
20. Have imprecise differences between consecutive values - but have a meaningful order to those values
A sampling distribution
Cumulative distribution functions
Ordinal measurements
Law of Large Numbers
21. 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
A data set
categorical variables
P-value
Observational study
22. 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.
The average - or arithmetic mean
A probability distribution
A sample
Dependent Selection
23. Describes a characteristic of an individual to be measured or observed.
Joint distribution
Variable
Independent Selection
Count data
24. 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.
Independence or Statistical independence
Seasonal effect
Statistical inference
Probability
25. Statistical methods can be used for summarizing or describing a collection of data; this is called
descriptive statistics
Experimental and observational studies
categorical variables
Atomic event
26. A measure that is relevant or appropriate as a representation of that property.
f(z) - and its cdf by F(z).
Observational study
Valid measure
Coefficient of determination
27. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
Probability density
Joint distribution
Reliable measure
The standard deviation
28. 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
Particular realizations of a random variable
Simulation
Sample space
29. The standard deviation of a sampling distribution.
A probability distribution
the sample or population mean
Inferential statistics
Standard error
30. Some commonly used symbols for population parameters
Estimator
Probability and statistics
Type I errors & Type II errors
the population mean
31. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
An event
A probability distribution
the population mean
Quantitative variable
32. The collection of all possible outcomes in an experiment.
Probability density functions
Sample space
Simulation
Observational study
33. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
The Range
Marginal distribution
Sampling frame
Likert scale
34. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Ratio measurements
Law of Large Numbers
Observational study
nominal - ordinal - interval - and ratio
35. Is a function that gives the probability of all elements in a given space: see List of probability distributions
A probability density function
An estimate of a parameter
Dependent Selection
A probability distribution
36. 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.
Lurking variable
A sample
A population or statistical population
A probability density function
37. Is defined as the expected value of random variable (X -
Joint distribution
The Covariance between two random variables X and Y - with expected values E(X) =
Conditional probability
Skewness
38. Cov[X - Y] :
covariance of X and Y
Random variables
Variable
Joint probability
39. Failing to reject a false null hypothesis.
Sampling Distribution
Statistical adjustment
Type 2 Error
A Distribution function
40. The probability of correctly detecting a false null hypothesis.
Power of a test
Sampling frame
hypotheses
Binomial experiment
41. Another name for elementary event.
Power of a test
Atomic event
Null hypothesis
Step 2 of a statistical experiment
42. Many statistical methods seek to minimize the mean-squared error - and these are called
Binomial experiment
methods of least squares
covariance of X and Y
Correlation
43. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Probability density functions
Observational study
Individual
Estimator
44. 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
A probability space
inferential statistics
Descriptive
Statistical adjustment
45. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'
Probability density functions
A population or statistical population
Conditional probability
The average - or arithmetic mean
46. 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
inferential statistics
nominal - ordinal - interval - and ratio
Type II errors
Probability
47. Uses patterns in the sample data to draw inferences about the population represented - accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing) - estimating numerical characteris
Power of a test
Quantitative variable
Inferential statistics
Step 3 of a statistical experiment
48. 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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49. 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)
Statistics
Ordinal measurements
Interval measurements
Type I errors
50. Planning the research - including finding the number of replicates of the study - using the following information: preliminary estimates regarding the size of treatment effects - alternative hypotheses - and the estimated experimental variability. Co
Binomial experiment
A probability space
Step 1 of a statistical experiment
A probability distribution