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. (or multivariate random variable) is a vector whose components are random variables on the same probability space.
A Random vector
the population mean
Estimator
The Covariance between two random variables X and Y - with expected values E(X) =
2. To find the average - or arithmetic mean - of a set of numbers:
Skewness
Type I errors & Type II errors
Divide the sum by the number of values.
Parameter - or 'statistical parameter'
3. 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.
The variance of a random variable
Joint probability
Inferential
Marginal distribution
4. Where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a 'false negative'.
A Probability measure
Type II errors
applied statistics
the population mean
5. Many statistical methods seek to minimize the mean-squared error - and these are called
Outlier
methods of least squares
The standard deviation
Null hypothesis
6. A subjective estimate of probability.
Credence
An estimate of a parameter
Simple random sample
A data set
7. Is a parameter that indexes a family of probability distributions.
Type 1 Error
A Statistical parameter
applied statistics
Sampling Distribution
8. Is denoted by - pronounced 'x bar'.
Kurtosis
Posterior probability
Type II errors
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
9. 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.
Conditional distribution
Likert scale
Joint distribution
The median value
10. Describes the spread in the values of the sample statistic when many samples are taken.
That value is the median value
The Range
Kurtosis
Variability
11. Is the set of possible outcomes of an experiment. For example - the sample space for rolling a six-sided die will be {1 - 2 - 3 - 4 - 5 - 6}.
Coefficient of determination
Average and arithmetic mean
The sample space
Type I errors & Type II errors
12. A group of individuals sharing some common features that might affect the treatment.
Block
the sample mean - the sample variance s2 - the sample correlation coefficient r - the sample cumulants kr.
Conditional distribution
Greek letters
13. 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.
hypotheses
Experimental and observational studies
Divide the sum by the number of values.
Statistics
14. The probability distribution of a sample statistic based on all the possible simple random samples of the same size from a population.
Simpson's Paradox
Sampling Distribution
Law of Large Numbers
Prior probability
15. (cdfs) are denoted by upper case letters - e.g. F(x).
the population cumulants
Cumulative distribution functions
Law of Parsimony
Dependent Selection
16. Two variables such that their effects on the response variable cannot be distinguished from each other.
Simple random sample
Confounded variables
Step 1 of a statistical experiment
Type II errors
17. Where the null hypothesis is falsely rejected giving a 'false positive'.
Skewness
Prior probability
Residuals
Type I errors
18. A measurement such that the random error is small
Variable
Reliable measure
A data point
Lurking variable
19. 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.
Law of Large Numbers
A sample
Inferential
A random variable
20. 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
Nominal measurements
Correlation
Probability
That is the median value
21. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.
Simulation
Probability density functions
Particular realizations of a random variable
Trend
22. Working from a null hypothesis two basic forms of error are recognized:
s-algebras
Type I errors & Type II errors
Variability
Correlation coefficient
23. When you have two or more competing models - choose the simpler of the two models.
An event
methods of least squares
Sampling
Law of Parsimony
24. (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
Kurtosis
Statistical inference
Sampling Distribution
The Expected value
25. (pdfs) and probability mass functions are denoted by lower case letters - e.g. f(x).
Probability density functions
hypothesis
Bias
the population variance
26. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively
Divide the sum by the number of values.
Residuals
That is the median value
Variability
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
An event
Step 3 of a statistical experiment
Power of a test
Inferential statistics
28. 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
categorical variables
the population variance
A random variable
Observational study
29. 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.
Kurtosis
That value is the median value
Correlation
A data set
30. 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.
Dependent Selection
Qualitative variable
Sampling frame
P-value
31. Is its expected value. The mean (or sample mean of a data set is just the average value.
A sample
The Mean of a random variable
categorical variables
Probability and statistics
32. Is defined as the expected value of random variable (X -
Probability and statistics
the population mean
The Covariance between two random variables X and Y - with expected values E(X) =
covariance of X and Y
33. A list of individuals from which the sample is actually selected.
A random variable
An event
Variability
Sampling frame
34. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise
Correlation coefficient
A likelihood function
An Elementary event
applied statistics
35. 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
Statistic
methods of least squares
variance of X
Independence or Statistical independence
36. 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
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
A population or statistical population
Joint distribution
Null hypothesis
37. 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
Binary data
Correlation
Law of Parsimony
A population or statistical population
38. Are usually written in upper case roman letters: X - Y - etc.
Nominal measurements
A likelihood function
The average - or arithmetic mean
Random variables
39. Can refer either to a sample not being representative of the population - or to the difference between the expected value of an estimator and the true value.
expected value of X
Outlier
Bias
Atomic event
40. Another name for elementary event.
Type 1 Error
Step 1 of a statistical experiment
Atomic event
hypotheses
41. 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
Qualitative variable
Sampling
Parameter - or 'statistical parameter'
42. Changes over time that show a regular periodicity in the data where regular means over a fixed interval; the time between repetitions is called the period.
The arithmetic mean of a set of numbers x1 - x2 - ... - xn
Seasonal effect
Type I errors
Type 1 Error
43. 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.
Estimator
Step 3 of a statistical experiment
An experimental study
experimental studies and observational studies.
44. Describes a characteristic of an individual to be measured or observed.
Variable
descriptive statistics
Qualitative variable
Conditional distribution
45. 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
hypothesis
An experimental study
the population correlation
Probability density
46. Is a process of selecting observations to obtain knowledge about a population. There are many methods to choose on which sample to do the observations.
Sampling
Probability density
Descriptive
Coefficient of determination
47. Failing to reject a false null hypothesis.
Type 2 Error
methods of least squares
Joint distribution
P-value
48. 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
Ratio measurements
Inferential statistics
Step 3 of a statistical experiment
Marginal distribution
49. 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.
Type II errors
A data point
Statistic
A population or statistical population
50. The collection of all possible outcomes in an experiment.
The Covariance between two random variables X and Y - with expected values E(X) =
Sample space
Binomial experiment
Nominal measurements