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. The proportion of the explained variation by a linear regression model in the total variation.
Marginal probability
Step 2 of a statistical experiment
Coefficient of determination
Law of Parsimony
2. 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.
Valid measure
The Mean of a random variable
Statistics
Experimental and observational studies
3. 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.
Joint distribution
Probability density
Conditional distribution
Ordinal measurements
4. Gives the probability distribution for a continuous random variable.
Type 2 Error
A probability density function
observational study
Credence
5. There are four main levels of measurement used in statistics: Each of these have different degrees of usefulness in statistical research.
Count data
Statistical inference
nominal - ordinal - interval - and ratio
A statistic
6. 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.
Simulation
Estimator
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
An event
7. Are simply two different terms for the same thing. Add the given values
Joint probability
The Range
Average and arithmetic mean
Probability density functions
8. 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.
variance of X
A data point
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Joint probability
9. 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
An event
Step 1 of a statistical experiment
categorical variables
variance of X
10. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.
Posterior probability
Statistical adjustment
Type II errors
Conditional probability
11. Is the most commonly used measure of statistical dispersion. It is the square root of the variance - and is generally written s (sigma).
Variable
The standard deviation
Divide the sum by the number of values.
A random variable
12. Is denoted by - pronounced 'x bar'.
A probability distribution
Inferential statistics
Binary data
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
13. Var[X] :
variance of X
Inferential statistics
The sample space
A Probability measure
14. A measure that is relevant or appropriate as a representation of that property.
Valid measure
Kurtosis
Residuals
That value is the median value
15. A measurement such that the random error is small
Reliable measure
Joint probability
Nominal measurements
Interval measurements
16. 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)
Individual
A data point
categorical variables
Interval measurements
17. Long-term upward or downward movement over time.
Average and arithmetic mean
Trend
Credence
inferential statistics
18. Is a sample space over which a probability measure has been defined.
Atomic event
A probability space
A population or statistical population
s-algebras
19. A numerical measure that describes an aspect of a sample.
Observational study
Independence or Statistical independence
Statistic
A statistic
20. A numerical measure that describes an aspect of a population.
the sample or population mean
An Elementary event
Parameter
Step 1 of a statistical experiment
21. 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 population or statistical population
Quantitative variable
Interval measurements
inferential statistics
22. Is the length of the smallest interval which contains all the data.
Descriptive statistics
The Range
Parameter
Experimental and observational studies
23. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.
the population cumulants
A probability space
Statistical dispersion
s-algebras
24. Consists of a number of independent trials repeated under identical conditions. On each trial - there are two possible outcomes.
Beta value
Binomial experiment
Coefficient of determination
Conditional distribution
25. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.
Type II errors
the population correlation
Law of Large Numbers
the population mean
26. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Law of Parsimony
Likert scale
Joint probability
27. Working from a null hypothesis two basic forms of error are recognized:
Type I errors & Type II errors
quantitative variables
Pairwise independence
Statistical adjustment
28. A data value that falls outside the overall pattern of the graph.
Trend
Inferential
Outlier
hypotheses
29. 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
Binary data
Divide the sum by the number of values.
Probability density
A sample
30. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)
Individual
Sampling
The Mean of a random variable
Step 1 of a statistical experiment
31. Are usually written in upper case roman letters: X - Y - etc.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Treatment
An experimental study
Random variables
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
Individual
Binomial experiment
the population correlation
Independence or Statistical independence
33. 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
Average and arithmetic mean
hypotheses
Law of Parsimony
Placebo effect
34. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as
s-algebras
categorical variables
quantitative variables
applied statistics
35. 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.
Sampling
Inferential statistics
A population or statistical population
Parameter - or 'statistical parameter'
36. Statistics involve methods of using information from a sample to draw conclusions regarding the population.
Inferential
Statistical adjustment
Confounded variables
Correlation
37. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.
P-value
Descriptive
A sample
descriptive statistics
38. 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
hypotheses
Mutual independence
That value is the median value
39. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
quantitative variables
An event
Particular realizations of a random variable
the sample or population mean
40. (cdfs) are denoted by upper case letters - e.g. F(x).
Trend
Probability density functions
Greek letters
Cumulative distribution functions
41. A numerical measure that assesses the strength of a linear relationship between two variables.
An estimate of a parameter
Correlation coefficient
Conditional probability
Sampling Distribution
42. 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
Descriptive
Count data
experimental studies and observational studies.
Correlation
43. Some commonly used symbols for sample statistics
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
P-value
methods of least squares
The Expected value
44. Two variables such that their effects on the response variable cannot be distinguished from each other.
Statistic
Confounded variables
A sampling distribution
Joint probability
45. Describes the spread in the values of the sample statistic when many samples are taken.
Variability
Dependent Selection
descriptive statistics
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
46. 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.
f(z) - and its cdf by F(z).
A likelihood function
A probability space
Independent Selection
47. A subjective estimate of probability.
Step 3 of a statistical experiment
Credence
A Probability measure
Bias
48. Is defined as the expected value of random variable (X -
Divide the sum by the number of values.
Probability
The Covariance between two random variables X and Y - with expected values E(X) =
That value is the median value
49. Rejecting a true null hypothesis.
Type 1 Error
Beta value
Pairwise independence
Sampling Distribution
50. Failing to reject a false null hypothesis.
Type 2 Error
Independence or Statistical independence
the sample or population mean
A data point