| 2012583830 | Standardizing | We ________ to eliminate units | | 0 |
| 2012583831 | Standardized Value | Value found by subtracting the mean and dividing by the standard deviation. | | 1 |
| 2012583832 | Shifting | Adding a constant to the mean, the median, and the quartiles, but does not change the standard deviation or IQR. | | 2 |
| 2012583833 | Rescaling | Multiple each data value by a constant multiplies both the measures of position and the measures of spread by that constant. | | 3 |
| 2012583834 | Normal Model | A useful family of models for unimodel, symmetric distributions. | | 4 |
| 2012583835 | Parameter | A numerically valued attribute of a model. | | 5 |
| 2012583836 | Statistic | A value calculated from data to summarize aspects of the data. | | 6 |
| 2012583837 | Z-score | Tells how many standard deviations a value is from the mean. | | 7 |
| 2012583838 | Boxplot | Displays the 5-number summary as a central box with whiskers that extend to the non-outlying data values. | | 8 |
| 2012583839 | Far Outlier | If a point is more than 3.0 IQR from either end of the box in a boxplot. | | 9 |
| 2012583840 | Comparing Distributions | Consider: shape, center, spread | | 10 |
| 2012583841 | Comparing Boxplots | Compare Shapes; Compare Medians; Compare IQRS; Check for outliers | | 11 |
| 2012583842 | Timeplot | Displays data that change overtime. | | 12 |
| 2012583843 | Standard Deviation | Square Root of the Var. | | 13 |
| 2012583844 | Variance | The sum of squared dev. from the mean, divided by the count minus 1. | | 14 |
| 2012583845 | Resistant | A calculated summary is said to be ________ if outliers have only a small effect on it. | | 15 |
| 2012583846 | Mean | Found by summing all the data values and dividing by the count. | | 16 |
| 2012583847 | 5 Number Summary | Reports the min., Q1, the median, Q3 and the max. | | 17 |
| 2012583848 | Percentile | The # that falls above i% of the data. | | 18 |
| 2012583849 | Interquartile Range (IQR) | The difference between the 1st and 3rd Quartiles. | | 19 |
| 2012583850 | Range | The difference between the lowest and highest values in a data set. Range = Max-Mir | | 20 |
| 2012583851 | Median | Middle value, if it is not an even #, you take the average of the 2 middle #'s. | | 21 |
| 2012583852 | Outliers | Extreme values that don't appear to belong with the rest of the data. Any point more than 1.5 IQR from either end of the box in a Boxplot. | | 22 |
| 2012583853 | Skewed | Distribution is _________ if it's not symmetric and 1 tail stretches out farther than the other. | | 23 |
| 2012583854 | Tails | The parts that typically trail off on either side. | | 24 |
| 2012583855 | Symmetric | 2 Halves on either side of the center look approximately like mirror images of each other. | | 25 |
| 2012583856 | Uniform | A distribution that's roughly flat. | | 26 |
| 2012583857 | Unimodal | 1 mode | | 27 |
| 2012583858 | Bimodal | 2 modes | | 28 |
| 2012583859 | Multimodal | More than 2 modes | | 29 |
| 2012583860 | Mode | A hump or local high point in the shape of the distribution of a var. | | 30 |
| 2012583861 | Spread | A numerical summary of how tightly the values are clustered around the center. Measures: IQR, Standard Dev. | | 31 |
| 2012583862 | Center | The place in the distribution of a variable that you'd point to if you wanted to attempt the impossible by summarizing the entire distribution with a single #. Measures: Mean, Median | | 32 |
| 2012583863 | Shape | To describe the _____ of a distribution, look for: single vs. mult. modes; symmetry vs skewness; outliers and gaps. | | 33 |
| 2012583864 | Dotplot | Graphs a dot for each case against a single axis. | | 34 |
| 2012583865 | Stem and Leaf Display | Shows quantitative data values in a way that sketches the distribution of the data. | | 35 |
| 2012583866 | Gap | A region of the distribution where there are no values. | | 36 |
| 2012583867 | Histogram | Uses adjacent bars to show the distribution of a quantitative var. | | 37 |
| 2012583868 | Frequency Table (Relative Frequency Table) | Lists the categories in a categorical var. and gives the count of percentages of each categories observation. | | 38 |
| 2012583869 | Distribution | The _____________ of a var. gives: possible values of the variance; the relative frequency of each value. | | 39 |
| 2012583870 | Area Principle | In a statistical display, each data value should be represented by the same amount of area. | | 40 |
| 2012583871 | Bar Chart | Shows a bar whose area represents the count (or percentage) of observations for each category of a categorical variance. | | 41 |
| 2012583872 | Pie Chart | Show how a "whole" divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category. | | 42 |
| 2012583873 | Contingency Table | Displays counts and, sometimes, percentages of individuals falling into named categories on 2 or more var. | | 43 |
| 2012583874 | Marginal Distribution | In a contingency table, the distribution of either var. alone. | | 44 |
| 2012583875 | Conditional Distribution | The distribution of a var. restricting the who to consider only a smaller group of individuals. | | 45 |
| 2012583876 | Independence | Variables are ________ if the conditional distribution of one variables is the same for each category of the other. | | 46 |
| 2012583877 | Segmented Bar Chart | Displays the conditional distribution of a categorical var. within each category of another var. | | 47 |
| 2012583878 | Simpson's paradox | When averages are taken across different groups, they can appear to contradict the overall averages. | | 48 |
| 2012583879 | Context | Tells who was measured, what was measured, how the data were collected, where the data was collected, and when and why the study was performed. | | 49 |
| 2012583880 | Data | Systematically recorded info., whether #'s or labels, together with its contact. | | 50 |
| 2012583881 | Data Table | An arrangement of data in which each row represents a case and each column represents a variable. | | 51 |
| 2012583882 | Case | Individual about whom or which we have data. | | 52 |
| 2012583883 | Population | All the cases we wish we knew about. | | 53 |
| 2012583884 | Sample | The cases we actually examine in seeking to understand the much larger population. | | 54 |
| 2012583885 | Variable | Holds info about the same characteristic for many cases. | | 55 |
| 2012583886 | Units | A quantity or amount adopted as a standard of measurement, such as dollars, hours, or grams. | | 56 |
| 2012583887 | Categorical Variable | A variable that names categories (words/numbers) | | 57 |
| 2012583888 | Quantitative Variable | A variable in which the numbers act as numerical values - always have units. | | 58 |
| 2027682233 | Random Phenomenon | If we know what outcomes could happen, but not which particular valves will happen. | | 59 |
| 2027682234 | Trial | A single attempt or realization of a random phenomenon. | | 60 |
| 2027682235 | Outcome | The value measured, observed, or reported for an individual instance of that trial. | | 61 |
| 2027682236 | Event | A collection of outcomes. | | 62 |
| 2027682237 | Sample Space | The collection of all possible outcome values. | | 63 |
| 2027682238 | Law of Large Numbers | States that the long run-run relative frequency of repeated independent events gets closer and closer to the true relative frequency as the number of trials increases. | | 64 |
| 2027682239 | Independence | If one event occurs it does not change the probability thta that the other event occurs. | | 65 |
| 2027682240 | Empirical Probability | The probability comes from the long-run relative frequency of the event's occurence. | | 66 |
| 2027682241 | Theoretical Probability | When the probability comes from a model. | | 67 |
| 2027682242 | Personal Probability | When the probability is subjective and represents your personal degree of belief. | | 68 |
| 2027682243 | Observational Study | A study based on data in which no manipulation of factors has been employed. | | 69 |
| 2027682244 | Retrospective Study | An observational study in which subjects are selected and then their previous conditions or behaviors are determined. | | 70 |
| 2027682245 | Prospective Study | An observational study in which subjects are followed to observe future outcomes. | | 71 |
| 2027682246 | Experiment | Manipulates factor levels to create treatments. Randomly assigns subjects to these treatment levels. Compares the responses of the subject groups across treatment levels. | | 72 |
| 2027682247 | Factor | A variance whose levels are manipulated by the experiment. | | 73 |
| 2027682248 | Response | A variance whose values are compared across different treatments. | | 74 |
| 2027682249 | Experimental Units | Individuals on whom an experiment is performed. | | 75 |
| 2027682250 | Level | The specific values that the experimenter chooses for a factor. | | 76 |
| 2027682251 | Treatment | The process, intervention, or other controlled circumstance applied to randomly assigned experimental units. | | 77 |
| 2027682252 | Priciples of Experimental Design | Control; Randomize; Replicate; Block | | 78 |
| 2027682253 | Control Group | The experimental units assigned to a basseline treatment level. | | 79 |
| 2027682254 | Placebo Effect | The tendency of many human subjects to show a response even when adminstered a placebo. | | 80 |
| 2027682255 | Blinding | Any individual associated with an experiment who is not aware of how subjects have been allocated to treatment groups. | | 81 |
| 2027682256 | Placebo | A treatment known to have no affect. | | 82 |
| 2027682257 | Confounding | Levels of one factor are associated with the levels of another factor in such a way that their effects cannot be separated. | | 83 |
| 2027682258 | Sample Survey | A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population. | | 84 |
| 2027682259 | Bias | Any systematic failure of a sampling method. | | 85 |
| 2027682260 | Randomization | The best defense against bias; each individual is given a fair, random chance of selection. | | 86 |
| 2027682261 | Sample Size | Number of individuals in a sample represents the population. | | 87 |
| 2027682262 | Census | Sample that consists of the entire population. | | 88 |
| 2027682263 | Population Parameter | Numericlaly valued attribute of a model for a population. | | 89 |
| 2027682264 | Representative | A sample is said to be ___________ if the stats computed from it accurately reflect the corresponding population parameters. | | 90 |
| 2027682265 | Simple Random Sample (SRS) | A sample in which each set of "n" elements in the population has an equal chance of selection. | | 91 |
| 2027682266 | SRS | Simple Random Sample | | 92 |
| 2027682267 | Sampling Frame | List of individuals from whom the same is drawn. | | 93 |
| 2027682268 | Sampling Variability | The natural tendency of randomly drawn samples to differ, one from another. | | 94 |
| 2027682269 | Stratified Random Sample | A sampling design in which the population is divided into several subpopulations, or strata, and random samples are then drawn from each stratum. | | 95 |
| 2027682270 | Cluster Sample | A sampling design in which entire groups are chosen at random. | | 96 |
| 2027682271 | Multistage Sample | Sampling schemes that combine several sampling methods. | | 97 |
| 2027682272 | Systematic Sample | A sample drawn by selecting individuals systematically from a sampling frame. | | 98 |
| 2027682273 | Pilot | A small trial run of a survey to check whether questions are clear. | | 99 |
| 2027682274 | Voluntary Response Bias | Bias introduced to a sample when individuals can choose on their own whether to participate in the sample. | | 100 |
| 2027682275 | Convenience Sample | Consists of the individuals who are conveniently available to sample. | | 101 |
| 2027682276 | Undercoverage | A sampling scheme that biases the sample in a way that gives a part of the population less representation. | | 102 |
| 2027682277 | Nonresponse Bias | Bias introduced when a large fraction of those sampled fails to respond. | | 103 |
| 2027682278 | Response Bias | Anything in a survey design that influences response. | | 104 |
| 2027682279 | Random | If we know the possible values it can have, but not which particular value it takes. | | 105 |
| 2027682280 | Simulation | Models a real-world situation by using random-digit outcomes to mimic the uncertainty of a response variance of interest. | | 106 |
| 2027682281 | Simulation Component | A component uses equally likely random digits to model simple random occurrences whose outcomes may not be equally likely. | | 107 |
| 2027682282 | Trial (Chapter 11) | The sequence of several componets representing events that we are pretending will take place. | | 108 |
| 2027682283 | Re-expression | We _______ data by taking the logarithm, the square root, the reciprocal, or some other mathematical operation on all values of a variance. | | 109 |
| 2027682284 | Ladder of Powers | Places in order the effects that many re-expressions have on the data. | | 110 |
| 2027682285 | Correlation Coefficient | Numerical measure of the direciton and strength of a line or association. | | 111 |
| 2027682286 | Scatterplot | Shows relationship between two quantitative variables measured on the same cases. | | 112 |
| 2027682287 | Lurking Variable | A variable other than x and y that simultaneously affects both variables, accounting for the correlation between the two. | | 113 |
| 2027682288 | Model | An equation of formula that simplifies and represents reality. | | 114 |
| 2027682289 | Linear Model | An equation of a line. To interpret a linear model, we need to know the variables and their units. | | 115 |
| 2027682290 | Predicted Value | The value of y^ found for a given x-value in the data. This is found by substituting the x-value in reg. equation. | | 116 |
| 2027682291 | Residuals | Difference between data values and the corresponding values predicted by the regression model. Observed Value minus predicted value (e= y-y^) | | 117 |
| 2027682292 | Least Squares | Specifics the unique line that minimizes the variance of the residuals or, equivalently, the sum of the squared residuals. | | 118 |
| 2027682293 | Regression to the mean | Because correlation is always less than 1.0 in magnitude, each predicted y^ tends to be fewer standard deviation from its mean than its corresponding x was from its mean. | | 119 |
| 2027682294 | Intercept | The intercept b (little o), gives a starting value in y-units. It's the y^ - value when x = 0. | | 120 |
| 2027682295 | Extrapolation | Although linear models provide an easy way to predict values of y for a given value of x, it is unsafe to predict for values of x far from the ones used to find the linear model equation. | | 121 |
| 2027682296 | Leverage | Data points whose x-value are far from the man of x, are said to exert _____________ on a linear model. | | 122 |
| 2027682297 | Influential Point | If omitting a point from the data results in a very different regression model. | | 123 |
| 2031495160 | Disjoint(mutually exclusive) | 2 events share no outcomes in common. | | 124 |
