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. A measure that is relevant or appropriate as a representation of that property.
Statistics
Valid measure
Statistic
Binary data
2. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
the population variance
the sample or population mean
Statistical adjustment
Sampling Distribution
3. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
Pairwise independence
Statistical dispersion
Alpha value (Level of Significance)
Individual
4. When there is an even number of values...
That is the median value
methods of least squares
Joint distribution
A statistic
5. 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
Joint probability
A data set
Residuals
6. 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.
P-value
Statistics
Credence
Individual
7. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
Trend
Inferential statistics
Step 2 of a statistical experiment
8. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.
hypothesis
Observational study
the population cumulants
A statistic
9. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Correlation coefficient
Binary data
Step 1 of a statistical experiment
applied statistics
10. Working from a null hypothesis two basic forms of error are recognized:
Parameter - or 'statistical parameter'
Independence or Statistical independence
Type I errors & Type II errors
hypothesis
11. 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.
Statistical inference
A Distribution function
Correlation
Experimental and observational studies
12. Probability of rejecting a true null hypothesis.
Particular realizations of a random variable
Alpha value (Level of Significance)
Parameter
applied statistics
13. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
A data set
categorical variables
quantitative variables
14. 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
Type I errors & Type II errors
the sample or population mean
That value is the median value
15. 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
Statistics
Bias
Reliable measure
16. 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
Inferential statistics
hypothesis
The median value
Experimental and observational studies
17. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Confounded variables
Variable
Estimator
The standard deviation
18. Describes a characteristic of an individual to be measured or observed.
Variable
Valid measure
inferential statistics
Marginal distribution
19. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
The Mean of a random variable
Sampling Distribution
experimental studies and observational studies.
An experimental study
20. Have no meaningful rank order among values.
Block
Residuals
Nominal measurements
An event
21. Have imprecise differences between consecutive values - but have a meaningful order to those values
A probability space
Ordinal measurements
The standard deviation
Type I errors
22. (cdfs) are denoted by upper case letters - e.g. F(x).
variance of X
Sampling Distribution
Cumulative distribution functions
Type I errors
23. 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
Sampling Distribution
Marginal probability
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
24. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
the population cumulants
A likelihood function
An estimate of a parameter
25. 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
Standard error
Probability and statistics
Statistical inference
Type II errors
26. 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.
Sampling Distribution
Independent Selection
Lurking variable
An Elementary event
27. 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
Simulation
Mutual independence
Step 2 of a statistical experiment
Probability density functions
28. S^2
Nominal measurements
Correlation
Binary data
the population variance
29. 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
Individual
Residuals
A data set
Skewness
30. 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
Mutual independence
methods of least squares
Step 1 of a statistical experiment
Bias
31. 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
A sampling distribution
The Range
Inferential statistics
A probability space
32. In particular - the pdf of the standard normal distribution is denoted by
The sample space
Step 2 of a statistical experiment
Null hypothesis
f(z) - and its cdf by F(z).
33. 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.
The Mean of a random variable
Alpha value (Level of Significance)
categorical variables
Experimental and observational studies
34. 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)
nominal - ordinal - interval - and ratio
Statistical inference
Interval measurements
A sampling distribution
35. Is defined as the expected value of random variable (X -
Ordinal measurements
Prior probability
The Covariance between two random variables X and Y - with expected values E(X) =
the population cumulants
36. A numerical measure that assesses the strength of a linear relationship between two variables.
Variable
Correlation coefficient
categorical variables
Type II errors
37. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Law of Large Numbers
A random variable
A sample
Simple random sample
38. A subjective estimate of probability.
Credence
the population variance
inferential statistics
Law of Parsimony
39. Are simply two different terms for the same thing. Add the given values
Sampling Distribution
Average and arithmetic mean
Simpson's Paradox
Quantitative variable
40. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as
Particular realizations of a random variable
categorical variables
Credence
The Range
41. The standard deviation of a sampling distribution.
A Distribution function
Valid measure
P-value
Standard error
42. Rejecting a true null hypothesis.
Trend
Interval measurements
Type 1 Error
the population mean
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.
Skewness
covariance of X and Y
quantitative variables
Simple random sample
44. A variable describes an individual by placing the individual into a category or a group.
Variable
Binomial experiment
Qualitative variable
A sample
45. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.
Bias
Type I errors
Simple random sample
Simpson's Paradox
46. A numerical facsimilie or representation of a real-world phenomenon.
quantitative variables
Sampling frame
Simulation
nominal - ordinal - interval - and ratio
47. 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
An Elementary event
Correlation
Probability
Mutual independence
48. Some commonly used symbols for sample statistics
Binary data
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Marginal probability
Seasonal effect
49. 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
Treatment
Count data
Beta value
Probability density
50. Two variables such that their effects on the response variable cannot be distinguished from each other.
The sample space
Step 3 of a statistical experiment
Confounded variables
Conditional distribution