SUBJECTS
|
BROWSE
|
CAREER CENTER
|
POPULAR
|
JOIN
|
LOGIN
Business Skills
|
Soft Skills
|
Basic Literacy
|
Certifications
About
|
Help
|
Privacy
|
Terms
|
Email
Search
Test your basic knowledge |
CLEP General Mathematics: Probability And Statistics
Start Test
Study First
Subjects
:
clep
,
math
Instructions:
Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can
study here
.
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. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
A sampling distribution
Probability
Type I errors
2. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Independence or Statistical independence
Particular realizations of a random variable
Parameter
3. A data value that falls outside the overall pattern of the graph.
Outlier
categorical variables
An event
Type 1 Error
4. ?r
the population cumulants
Ratio measurements
Probability density
Nominal measurements
5. Rejecting a true null hypothesis.
Type 1 Error
Type 2 Error
Seasonal effect
The median value
6. 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
Descriptive
A data point
Skewness
Sample space
7. Error also refers to the extent to which individual observations in a sample differ from a central value - such as
the sample or population mean
applied statistics
observational study
Bias
8. Of a group of numbers is the center point of all those number values.
The average - or arithmetic mean
hypothesis
A Random vector
A Probability measure
9. ?
That value is the median value
observational study
the population correlation
Type II errors
10. (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
the population variance
Estimator
Type 1 Error
A likelihood function
11. 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
Independent Selection
Step 2 of a statistical experiment
Posterior probability
Parameter - or 'statistical parameter'
12. Failing to reject a false null hypothesis.
Type II errors
the population mean
A data point
Type 2 Error
13. Is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of data. The mean can be used as an estimator.
expected value of X
A population or statistical population
Estimator
Posterior probability
14. 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
Correlation coefficient
Law of Large Numbers
Random variables
hypothesis
15. The standard deviation of a sampling distribution.
Probability density functions
Standard error
Variability
A population or statistical population
16. 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 Mean of a random variable
The Range
The Covariance between two random variables X and Y - with expected values E(X) =
17. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.
18. Is a typed measurement - it can be a boolean value - a real number - a vector (in which case it's also called a data vector) - etc.
Placebo effect
Conditional probability
A data point
Joint distribution
19. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
nominal - ordinal - interval - and ratio
Ordinal measurements
the population correlation
Bias
20. Many statistical methods seek to minimize the mean-squared error - and these are called
methods of least squares
Nominal measurements
Greek letters
Residuals
21. Some commonly used symbols for population parameters
Inferential
the population variance
the population mean
Marginal distribution
22. Probability of rejecting a true null hypothesis.
quantitative variables
Skewness
Inferential statistics
Alpha value (Level of Significance)
23. 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
Estimator
Descriptive statistics
Probability and statistics
Confounded variables
24. When there is an even number of values...
Confounded variables
the population mean
Sampling
That is the median value
25. Is a sample space over which a probability measure has been defined.
A probability space
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
expected value of X
Type I errors & Type II errors
26. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
the population variance
A statistic
Lurking variable
Marginal distribution
27. (e.g. ? - b) are commonly used to denote unknown parameters (population parameters).
Nominal measurements
Greek letters
Sample space
Individual
28. Data are gathered and correlations between predictors and response are investigated.
the population mean
Sampling frame
Individual
observational study
29. Is its expected value. The mean (or sample mean of a data set is just the average value.
Nominal measurements
The average - or arithmetic mean
The Mean of a random variable
Likert scale
30. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Law of Parsimony
hypotheses
categorical variables
the population mean
31. 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
Step 3 of a statistical experiment
Joint probability
Simple random sample
32. Is a parameter that indexes a family of probability distributions.
Independent Selection
A Statistical parameter
Parameter - or 'statistical parameter'
Prior probability
33. 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.
Variable
Simple random sample
Random variables
observational study
34. 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
applied statistics
Prior probability
Step 3 of a statistical experiment
the population cumulants
35. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Variable
Statistical inference
Statistical dispersion
experimental studies and observational studies.
36. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Residuals
the sample or population mean
Kurtosis
Likert scale
37. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.
Descriptive
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Qualitative variable
An experimental study
38. 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.
Independent Selection
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
methods of least squares
Dependent Selection
39. 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
Type 1 Error
Probability and statistics
Descriptive
A probability distribution
40. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Binomial experiment
Law of Parsimony
Null hypothesis
Divide the sum by the number of values.
41.
Simulation
the population mean
Divide the sum by the number of values.
The Range
42. Working from a null hypothesis two basic forms of error are recognized:
Particular realizations of a random variable
An estimate of a parameter
The standard deviation
Type I errors & Type II errors
43. Is the length of the smallest interval which contains all the data.
Posterior probability
The Range
Step 1 of a statistical experiment
Prior probability
44. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
The standard deviation
nominal - ordinal - interval - and ratio
A Distribution function
The Range
45. (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
Estimator
The Expected value
Block
the population correlation
46. 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.
Type II errors
The variance of a random variable
Lurking variable
Joint distribution
47. 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.
A likelihood function
Lurking variable
Greek letters
Variability
48. Any specific experimental condition applied to the subjects
methods of least squares
Probability density
Seasonal effect
Treatment
49. Is defined as the expected value of random variable (X -
Credence
The Covariance between two random variables X and Y - with expected values E(X) =
f(z) - and its cdf by F(z).
observational study
50. 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)
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A random variable
Interval measurements
Bias