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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. 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
Probability and statistics
the population cumulants
covariance of X and Y
Binomial experiment
2. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
The Covariance between two random variables X and Y - with expected values E(X) =
Sampling frame
A sampling distribution
3. Describes the spread in the values of the sample statistic when many samples are taken.
Inferential statistics
Variability
Parameter
Type 2 Error
4. Is that part of a population which is actually observed.
Probability and statistics
observational study
A sample
Mutual independence
5. Data are gathered and correlations between predictors and response are investigated.
Particular realizations of a random variable
observational study
Posterior probability
An estimate of a parameter
6. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Experimental and observational studies
Type 1 Error
Descriptive
methods of least squares
7. 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
Random variables
Interval measurements
Marginal distribution
Descriptive statistics
8. Var[X] :
Correlation
Experimental and observational studies
variance of X
Standard error
9. When there is an even number of values...
Simpson's Paradox
Null hypothesis
That is the median value
Statistic
10. Is data that can take only two values - usually represented by 0 and 1.
Binary data
Conditional distribution
A probability density function
A likelihood function
11. Where the null hypothesis is falsely rejected giving a 'false positive'.
Type I errors
Statistical dispersion
Atomic event
Descriptive
12. Cov[X - Y] :
Law of Large Numbers
Placebo effect
Residuals
covariance of X and Y
13. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.
s-algebras
Posterior probability
Joint distribution
That value is the median value
14. A pairwise independent collection of random variables is a set of random variables any two of which are independent.
Pairwise independence
s-algebras
A random variable
Sampling Distribution
15. 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.
That value is the median value
A population or statistical population
applied statistics
Nominal measurements
16. 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.
Experimental and observational studies
Count data
Nominal measurements
Outlier
17. 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
Particular realizations of a random variable
Correlation
the population mean
Quantitative variable
18. Is a measure of the asymmetry of the probability distribution of a real-valued random variable. Roughly speaking - a distribution has positive skew (right-skewed) if the higher tail is longer and negative skew (left-skewed) if the lower tail is longe
Skewness
A random variable
Step 1 of a statistical experiment
A Distribution function
19. Failing to reject a false null hypothesis.
Type 2 Error
categorical variables
Inferential
hypotheses
20. A group of individuals sharing some common features that might affect the treatment.
Ratio measurements
Residuals
Block
Step 3 of a statistical experiment
21. Are usually written in upper case roman letters: X - Y - etc.
covariance of X and Y
Random variables
Reliable measure
f(z) - and its cdf by F(z).
22. A measure that is relevant or appropriate as a representation of that property.
P-value
Reliable measure
Valid measure
nominal - ordinal - interval - and ratio
23. 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 median value
Count data
the population cumulants
Descriptive
24. 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 population cumulants
Qualitative variable
Simulation
25. Is the length of the smallest interval which contains all the data.
quantitative variables
The Range
Observational study
A data point
26. 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.
observational study
hypothesis
Independent Selection
A sampling distribution
27. 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
Probability
Joint probability
A probability space
Inferential statistics
28. 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
Mutual independence
Nominal measurements
Marginal probability
Statistic
29. In Bayesian inference - this represents prior beliefs or other information that is available before new data or observations are taken into account.
Prior probability
Interval measurements
Pairwise independence
Type 2 Error
30. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Greek letters
Outlier
Treatment
the population cumulants
31. Is a parameter that indexes a family of probability distributions.
Count data
Posterior probability
A Statistical parameter
hypothesis
32. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
Parameter
Statistical adjustment
A Random vector
A sampling distribution
33. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Step 2 of a statistical experiment
Parameter
Placebo effect
The standard deviation
34. A numerical measure that assesses the strength of a linear relationship between two variables.
The Expected value
Correlation coefficient
Particular realizations of a random variable
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
35. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.
Reliable measure
Quantitative variable
the population variance
Sampling
36. Is denoted by - pronounced 'x bar'.
A Distribution function
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Standard error
Type 1 Error
37. Working from a null hypothesis two basic forms of error are recognized:
A Statistical parameter
hypotheses
The Expected value
Type I errors & Type II errors
38. Is its expected value. The mean (or sample mean of a data set is just the average value.
expected value of X
The Mean of a random variable
Inferential statistics
Pairwise independence
39. (cdfs) are denoted by upper case letters - e.g. F(x).
Cumulative distribution functions
Type I errors
Type II errors
Individual
40. A numerical facsimilie or representation of a real-world phenomenon.
The Covariance between two random variables X and Y - with expected values E(X) =
categorical variables
covariance of X and Y
Simulation
41. The collection of all possible outcomes in an experiment.
Placebo effect
Conditional probability
Sample space
quantitative variables
42. Have imprecise differences between consecutive values - but have a meaningful order to those values
A Statistical parameter
Simpson's Paradox
Nominal measurements
Ordinal measurements
43. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Step 2 of a statistical experiment
Random variables
A data set
Binomial experiment
44. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
Dependent Selection
Sampling frame
Observational study
quantitative variables
45. Gives the probability of events in a probability space.
Binary data
The Range
A Probability measure
Sampling Distribution
46. 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).
Joint probability
Kurtosis
Cumulative distribution functions
The variance of a random variable
47. Probability of rejecting a true null hypothesis.
Residuals
The sample space
Alpha value (Level of Significance)
Standard error
48. 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
Simple random sample
Descriptive statistics
Valid measure
49. Rejecting a true null hypothesis.
Statistical adjustment
Law of Parsimony
Type 1 Error
Simpson's Paradox
50. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
the population mean
Null hypothesis
expected value of X
Law of Large Numbers