Test your basic knowledge |

CLEP General Mathematics: Probability And Statistics

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 atomic event) is an event with only one element. For example - when pulling a card out of a deck - 'getting the jack of spades' is an elementary event - while 'getting a king or an ace' is not.






2. The probability of the observed value or something more extreme under the assumption that the null hypothesis is true.






3. Gives the probability distribution for a continuous random variable.






4. Another name for elementary event.






5. Data are gathered and correlations between predictors and response are investigated.






6. Is inference about a population from a random sample drawn from it or - more generally - about a random process from its observed behavior during a finite period of time.






7. Is the probability of some event A - assuming event B. Conditional probability is written P(A|B) - and is read 'the probability of A - given B'






8. Of a group of numbers is the center point of all those number values.






9. 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.






10. 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.






11. A group of individuals sharing some common features that might affect the treatment.






12. Are usually written in upper case roman letters: X - Y - etc.






13. 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.






14. A scale that represents an ordinal scale such as looks on a scale from 1 to 10.






15. The objects described by a set of data: person (animal) - place - and - thing. (SUBJECTS)






16. 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






17. Many statistical methods seek to minimize the mean-squared error - and these are called






18. Are usually written with upper case calligraphic (e.g. F for the set of sets on which we define the probability P)






19. 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






20. 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.






21. 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).






22. 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






23. Some commonly used symbols for sample statistics






24. To find the average - or arithmetic mean - of a set of numbers:






25. A numerical measure that describes an aspect of a population.






26. A variable has a value or numerical measurement for which operations such as addition or averaging make sense.






27. Failing to reject a false null hypothesis.






28. (or multivariate random variable) is a vector whose components are random variables on the same probability space.






29. 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.






30. 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






31. Long-term upward or downward movement over time.






32. The collection of all possible outcomes in an experiment.






33. Can be a population parameter - a distribution parameter - an unobserved parameter (with different shades of meaning). In statistics - this is often a quantity to be estimated.

Warning: Invalid argument supplied for foreach() in /var/www/html/basicversity.com/show_quiz.php on line 183


34. Involves taking measurements of the system under study - manipulating the system - and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.






35. Is its expected value. The mean (or sample mean of a data set is just the average value.






36. A numerical measure that describes an aspect of a sample.






37. 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






38. Given two random variables X and Y - the joint distribution of X and Y is the probability distribution of X and Y together.






39. Gives the probability of events in a probability space.






40. Describes the spread in the values of the sample statistic when many samples are taken.






41. In the long run - as the sample size increases - the relative frequencies of outcomes approach to the theoretical probability.






42. (also called statistical variability) is a measure of how diverse some data is. It can be expressed by the variance or the standard deviation.






43. Is the result of applying a statistical algorithm to a data set. It can also be described as an observable random variable.






44. 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






45. 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






46. 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






47. Is data that can take only two values - usually represented by 0 and 1.






48. Is a parameter that indexes a family of probability distributions.






49. 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






50. Have imprecise differences between consecutive values - but have a meaningful order to those values