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






2. 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)






3. 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.






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






5. 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






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






7. (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.






8. Describes a characteristic of an individual to be measured or observed.






9. 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.






10. 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


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






12. Have no meaningful rank order among values.






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






14. The proportion of the explained variation by a linear regression model in the total variation.






15. Working from a null hypothesis two basic forms of error are recognized:






16. Statistics involve methods of organizing - picturing - and summarizing information from samples or population.






17. Descriptive statistics and inferential statistics (a.k.a. - predictive statistics) together comprise






18. Error also refers to the extent to which individual observations in a sample differ from a central value - such as






19. 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






20. Is often denoted by placing a caret over the corresponding symbol - e.g. - pronounced 'theta hat'.






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






22. Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically - sometimes they are grouped together as






23. Is a measure of the 'peakedness' of the probability distribution of a real-valued random variable. Higher kurtosis means more of the variance is due to infrequent extreme deviations - as opposed to frequent modestly sized deviations.






24. ?r






25. Is the probability of an event - ignoring any information about other events. The marginal probability of A is written P(A). Contrast with conditional probability.






26. Is a sample and the associated data points.






27. 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.






28. Statistics involve methods of using information from a sample to draw conclusions regarding the population.






29. 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.






30. 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






31. 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.






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






33. The probability of correctly detecting a false null hypothesis.






34. A variable describes an individual by placing the individual into a category or a group.






35. Is a sample space over which a probability measure has been defined.






36. 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






37. 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'






38. Probability of accepting a false null hypothesis.






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






40. 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






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






42. Some commonly used symbols for population parameters






43. A consistent - repeated deviation of the sample statistic from the population parameter in the same direction when many samples are taken.






44. 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.






45. (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






46. Some commonly used symbols for sample statistics






47. 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.






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






49. 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.






50. 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.