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. 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.






2. 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






3. 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}.






4. The standard deviation of a sampling distribution.






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






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






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






8. 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






9. 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






10. 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.






11. Some commonly used symbols for sample statistics






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






13. The errors - or difference between the estimated response y^i and the actual measured response yi - collectively






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






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






16. 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.






17. 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






18. 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.






19. 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






20. 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.






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






22. 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.






23. Where the null hypothesis is falsely rejected giving a 'false positive'.






24. Ratio and interval measurements which can be either discrete or continuous - due to their numerical nature are grouped together as






25. 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.






26. 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






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






28. 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.






29. Are written in corresponding lower case letters. For example x1 - x2 - ... - xn could be a sample corresponding to the random variable X.






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






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






32. A measurement such that the random error is small






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






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






35. 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






36. 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.






37. A measure that is relevant or appropriate as a representation of that property.






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






39. When info. in a contingency table is re-organized into more or less categories - relationships seen can change or reverse.

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


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






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






42. Failing to reject a false null hypothesis.






43. 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






44. 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.






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






46. Used to reduce bias - this measure weights the more relevant information higher than less relevant info.






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






48. 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)






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






50. 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