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AP Statistics Vocab

Subjects : statistics, ap
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. Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups






2. The natural tendency of randomly drawn samples to differ






3. A sampling design in which the population is divided into several subpopulations - and random samples are then drawn from each stratum






4. Shows a bar representing the count of each category in a categorical variable






5. Summarized with the standard deviation - interquartile range - and range






6. An observational study in which subjects are selected and then their previous conditions or behaviors are determined






7. To be valid - an experiment must assign experimental units to treatment groups at random






8. Shows how a 'whole' divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category






9. A sampling design in which entire groups are chosen at random






10. The specific values that the experimenter chooses for a factor






11. An equation or formula that simplifies and represents reality






12. This - b0 - gives a starting value in y-units; it's the y-hat-value when x is 0






13. We do this by taking the logarithm - the square root - the reciprocal - or some other mathematical operation on all values in the data set






14. Consists of the individuals who are conveniently available






15. Adding a constant to each data value adds the same constant to the mean - the median - and the quartiles - but does not change the standard deviation or IQR






16. The difference between the first and third quartiles






17. Numerically valued attribute of a model






18. A numerically valued attribute of a model for a population






19. A variable whose levels are controlled by the experimenter






20. A numerical summary of how tightly the values are clustered around the 'center'






21. A hump or local high point in the shape of the distribution of a variable; the apparent locations of these can change as the scale of a histogram is changed






22. When averages are taken across different groups - they can appear to contradict the overall averages

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23. Displays counts and - sometimes - percentages of individuals falling into named categories on two or more variables; categorizes the individuals on all variables at once - to reveal possible patterns in one variable that may be contingent on the cate






24. Ideally tells who was measured - what was measured - how the data were collected - where the data were collected - and when and why the study was performed






25. In a normal model - about 68% of values fall within 1 standard deviation of the mean - about 95% fall within 2 standard deviations of the mean - and about 99.7% fall within 3 standard deviations of the mean






26. If data consist of two or more groups that have been thrown together - it is usually best to fit different linear models to each group than to try to fit a single model to all of the data






27. A sampling scheme that biases the sample in a way that gives a part of the population less representation than it has in the population






28. A variable that is not explicitly part of a model but affects the way the variables in the model appear to be related






29. A value that attempts the impossible by summarizing the entire distribution with a single number - a 'typical' value






30. The tendency of many human subjects (often 20% or more of experiment subjects) to show a response even when administered a placebo






31. The sequence of several components representing events that we are pretending will take place






32. Control - randomize - replicate - block






33. When doing this - consider their shape - center - and spread






34. Gives a value in 'y-units per x-unit'; changes of one unit in x are associated with changes of b1 units in predicted values of y






35. A scatterplot shows an association that is this if there is little scatter around the underlying relationship






36. Done to eliminate units; values can be compared and combined even if the original variables had different units and magnitudes






37. Sampling schemes that combine several sampling methods






38. The process - intervention - or other controlled circumstance applied to randomly assigned experimental units






39. A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population






40. A numerical measure of the direction and strength of a linear association






41. A list of individuals from whom the sample is drawn






42. Holds information about the same characteristic for many cases






43. Manipulates factor levels to create treatments - randomly assigns subjects to these treatment levels - and then compares the responses of the subject groups across treatment levels






44. In a statistical display - each data value should be represented by the same amount of area






45. A sample drawn by selecting individuals systematically from a sampling frame






46. These are hard to generate - but several websites offer an unlimited supply of equally likely random values






47. To describe this aspect of a distribution - look for single vs. multiple modes - and symmetry vs. skewness






48. Anything in a survey design that influences response






49. Having one mode; this is a useful term for describing the shape of a histogram when it's generally mound-shaped






50. Models random events by using random numbers to specify event outcomes with relative frequencies that correspond to the true real-world relative frequencies we are trying to model