AP Stats
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Created by:
braydenadurnell on April 18, 2011
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99 terms
Terms | Definitions |
|---|---|
 Data | Systematically recorded information |
| Population | All cases we wish we knew about |
| Sample | All cases we actually examine |
| Categorical Variable | All variables that name categories |
| Quantitative Variable | A variable in which the numbers act as numberical values |
| Frequency Table | A table that lists categories i a categorical variable and gives counts of observations for each category |
| Area Principle | Each data value should be represented by same amount of area |
| Marginal Distribution | In a contingency table, the distribution of either variable alone |
| Contingency Table | Displays counts (%) of individuals falling into categories on two or more variables |
| Simpson's Paradox | When averages are taken across different groups, they can appear to contradict the overall average |
| Histogram | Used adjacent bars to show distribution of quantitative variable |
| Stem and Leaf Display | Shows quantitative data values in a way that sketches the distributions of the data |
| Dot Plot | Graphs a dot for each case against a single axis |
| Mode | Hump or local high point in shape of a distribution |
| Unimodal | Having one mode (bump) |
| Bimodal | Having two modes (bumps) |
| Multimodal | Having more than two modes (bumps) |
| Uniform | A distribution that is roughly flat |
| Symmetric | A distribution where two sides are mirror images of each other |
| Skewed | When a grpah is not symmetric, you look at which tail stretches out further. |
| Outliers | Any value that is more than 1.5 (IQR) from either end of a box plot. A point that does not fit |
| Far Outlier | Any value that is more than 3 (IQR) from either end of a box plot |
| Median | Middle Value. Usually used when data is skewed |
| Range | High - Low |
| 1st Quartile | 25% of data lies below |
| 3rd Quartile | 75% of data lies below |
| Interquartile Range (IQR) | Q3 - Q1 |
| 5 Number Summary | Same as a box and whisker plot (min, max, Q1, Q3, Median |
| Mean | A measure of center used when data are symmetric |
| Standard Deviation | Measure of spread - tells the average distance each value is away from the mean |
| Time Plot | Dsiplays data that change over time |
| Parameter | A population measure |
| Statistics | A sample measure that is calculated from a set of data |
| Z-Score | Tells how many standard deviations a value is away from the means. They do not have units, so we can compare two different values |
| 65-95-99.7 Rule | Says 68% fall within 1 Standard Deviation, 95% of data fall within 2 standard deviations, 99.7% of data fall within 3 standard deviations |
| Scatterplots | Shows relationship between two quantitative variables |
| Response Variable | Variabvles that you hope to predict or explain (Y) |
| Explanatory Variable | Variables that you can use to account for, explain or predict the y variable (X) |
| Correlation Coefficient | A number that measures direction and strength of a linear association |
| Lurking Variable | A variable other than x and y that affects both variables accounting for hte correlation between the two |
| Residual | Differences between data values and corresponding predicted values |
| Slope | B1 gives a vlue in y units per x |
| Intercept | B0 is the value of y when x is zero |
| Se | Standard Deviation of residuals |
| R(squared) | Overall measure of how successful the regression is in linearly relating y to x |
| Extrapolation | When we use regression to predict the future |
| Leverage | Data points that pull the line close to them so they can have a great effect on slope and intercept |
| Influential Point | A point that when omitted will give very different results |
| Ladder of Powers | Places an order to any re-expressoin we must do |
| Bias | Any systematic failure of a sampling method to represent its population |
| Census | A sample that consists of the entire population |
| Simple Random Sample (SRS) | A sample size where each element has an equal chance of being selected |
| Stratified Random Sample | When population is divided into several sub-populations |
| Cluster Sample | Where entire groups are chosen at random |
| Multistage Sample | When we combing several sampling methods |
| Systematic Sample | A sample drawn by taking every n^th person |
| Pilot | Small trial run of a survey to see if questions are clear |
| Convenience sample | When a sample is created by subjects that are convenient |
| Undercoverage | When part of a population is represented less than another |
| Voluntary Response Bias | When we receive biased results due to the responses received |
| Non-response bias | When we receive biased results due to the resonses not received |
| Response Bias | Anything in a survey design that influences responses |
| Observational Study | A study based on data where no manipulation has been employed |
| Retrospective Study | An observational study where subjects are selected and their previous conditions are determined |
| Prospective Study | An observational study where subjects are followed to observe future outcomes |
| Experiment | An experiment that manipulates factor levels to create treatments |
| Factor | A variable whose levels are manipulated by the experimentor |
| Statistically Significant | When an observed difference is too large for us to believe that it is likely to have occurred naturally |
| Control Group | Our baseline in an experiment |
| Blinding | Any individual in an experiment that is unaware of how subjects have been treated |
| Single-Blind | When one (subject or evaluators) is blinded |
| Double-Blind | When subject and evaluator is blind |
| Placebo | A treatment that has no effect |
| Placebo Effect | Tendency of humans to show a response even when they have been given a placebo |
| Blocking | When groups of experimental units are similar |
| Matching | In a retrospective or prospective study, subjects who are similar in ways not under study may be matched and compared to each other |
| Confounding | When the levels of one factor are associated with the levels of another factor in such a way that their effects cannot be seperated. |
| Trial | A single attempt or realization of a random phenomenon |
| Outcome | Value that is measure or reported from a trial |
| Sample Space | Collection of all possible outcomes |
| Law of Large Numbers | The long run probability will approach its theoretical probability |
| Independence | When the probability of one even occurring has no affect on the probability of a second event occurring |
| Probability | A number between 0 and 1 that describes the chance of an event occurring |
| Disjoint | When two events share no common outcomes |
| Mutually Exclusive | When two events share no common outcomes |
| Expected Value | The theoretical long-run average value. AKA The Mean |
| Bernoulli Trials | Have 2 outcomes, probability of a success is constant, trials are independent |
| Geometric probability model | Counts the Number of Bernoulli trials until the first success |
| Binomial Probability Model | Counts the number of successes in n trials |
| Central Limit Theorem | The sampling distribution model of the sample mean and proportion from a random sample is approximately normal for large n regardless of the distribuion of the population as long as observations are independent |
| Standard Error | When we estimate the standard deviation of a sampling distribution using statistics found from the data |
| Confidence Interval | An inverval used for estimating a parameter |
| Margin of Error | Tells you the "give or take" from a confidence interval |
| Alpha Level | The "threshold" p value that determines whether we reject a null hypothesis |
| Statistically Significant | When the p value falls below the alpha level |
| Significance Level | Another name for alpha level |
| Type I Error | Rejecting a null hypothesis when it is true (also called a false positive) usually denoted by a fancy a |
| Type II Error | Failing to rejet a null hypothesis when it is false. (also called a false positive) usually denoted by a special B |
| Power | The probability that a hypothesis test will correctly reject a false null |
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