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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. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Joint distribution
Joint probability
Simulation
Binomial experiment
2. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Trend
Sample space
The Mean of a random variable
3. Is the study of the collection - organization - analysis - and interpretation of data. It deals with all aspects of this - including the planning of data collection in terms of the design of surveys and experiments.
Outlier
the population correlation
Statistics
Correlation coefficient
4. A numerical measure that assesses the strength of a linear relationship between two variables.
Bias
Correlation coefficient
Trend
Inferential statistics
5. 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
applied statistics
Divide the sum by the number of values.
Posterior probability
experimental studies and observational studies.
6. ?
the population correlation
The Covariance between two random variables X and Y - with expected values E(X) =
A data point
Experimental and observational studies
7. ?r
Probability
the population cumulants
Inferential
the sample or population mean
8. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Type I errors & Type II errors
Independence or Statistical independence
hypothesis
The standard deviation
9. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Power of a test
Conditional distribution
Observational study
A Random vector
10. Is a function that gives the probability of all elements in a given space: see List of probability distributions
Statistics
Atomic event
The standard deviation
A probability distribution
11. 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.
Qualitative variable
Variable
A random variable
A population or statistical population
12. Is data that can take only two values - usually represented by 0 and 1.
Bias
The Range
Step 1 of a statistical experiment
Binary data
13. (cdfs) are denoted by upper case letters - e.g. F(x).
variance of X
Cumulative distribution functions
Confounded variables
A statistic
14. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
An event
Law of Large Numbers
Variable
hypotheses
15. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
A probability density function
Quantitative variable
Individual
A data point
16. 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
Particular realizations of a random variable
The standard deviation
Step 1 of a statistical experiment
Power of a test
17. 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'
the population mean
Conditional probability
An experimental study
Individual
18. 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.
Statistical inference
Divide the sum by the number of values.
Average and arithmetic mean
A population or statistical population
19. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Residuals
Greek letters
Descriptive statistics
Posterior probability
20. Some commonly used symbols for population parameters
A probability density function
Residuals
the population mean
nominal - ordinal - interval - and ratio
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
The standard deviation
categorical variables
Observational study
Dependent Selection
22. 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
Observational study
Probability
Trend
the population mean
23. Is denoted by - pronounced 'x bar'.
Atomic event
Null hypothesis
The sample space
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
24. 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.
Correlation coefficient
The average - or arithmetic mean
An experimental study
Coefficient of determination
25. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Type II errors
Probability density functions
The average - or arithmetic mean
Pairwise independence
26. 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
Probability density
Step 2 of a statistical experiment
Variability
Type 1 Error
27. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
Experimental and observational studies
Correlation coefficient
Type II errors
A sampling distribution
28. Are usually written in upper case roman letters: X - Y - etc.
the population mean
hypothesis
Conditional distribution
Random variables
29. Failing to reject a false null hypothesis.
Seasonal effect
Type I errors & Type II errors
Type 2 Error
nominal - ordinal - interval - and ratio
30.
Dependent Selection
the population mean
A Distribution function
That value is the median value
31. Working from a null hypothesis two basic forms of error are recognized:
Inferential
Placebo effect
Type I errors & Type II errors
Type II errors
32. 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
Independence or Statistical independence
Simple random sample
Coefficient of determination
Probability density
33. 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).
An event
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
The Range
A Distribution function
34. 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.
Correlation coefficient
Marginal distribution
Nominal measurements
The Covariance between two random variables X and Y - with expected values E(X) =
35. The proportion of the explained variation by a linear regression model in the total variation.
experimental studies and observational studies.
hypotheses
Coefficient of determination
Individual
36. Is the probability distribution - under repeated sampling of the population - of a given statistic.
Nominal measurements
That is the median value
A data set
A sampling distribution
37. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
Correlation coefficient
Step 2 of a statistical experiment
the sample or population mean
38. 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.
A random variable
variance of X
Marginal distribution
Law of Parsimony
39. A list of individuals from which the sample is actually selected.
Statistic
Atomic event
Sampling frame
Likert scale
40. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
Prior probability
A statistic
A probability space
An Elementary event
41. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
Statistical dispersion
Inferential statistics
Joint distribution
42. Is the function that gives the probability distribution of a random variable. It cannot be negative - and its integral on the probability space is equal to 1.
Block
A Distribution function
A Random vector
Seasonal effect
43. 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 Probability measure
Simple random sample
Conditional distribution
That is the median value
44. Given two jointly distributed random variables X and Y - the conditional probability distribution of Y given X (written 'Y | X') is the probability distribution of Y when X is known to be a particular value.
A probability density function
Correlation coefficient
categorical variables
Conditional distribution
45. Describes a characteristic of an individual to be measured or observed.
Correlation
Credence
Variable
Beta value
46. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Quantitative variable
Bias
Simulation
Type II errors
47. A measurement such that the random error is small
Ordinal measurements
The sample space
categorical variables
Reliable measure
48. (or expectation) of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ('value'). Thus - it represents the average amount one 'expects' to win per bet if bets with identical odds are re
Inferential statistics
The Expected value
Treatment
Marginal distribution
49. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
P-value
Placebo effect
Treatment
50. A data value that falls outside the overall pattern of the graph.
Variability
The standard deviation
Outlier
Joint probability