AP Stats Ch 1-12
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| 9322895299 | Describe Distributions (3) | SHAPE, symmetric, skew, bimodal CENTER, mean or median SPREAD, range, standard deviation, Outliers | 0 | |
| 9322895300 | 5 Number summary | Min, Q1, Median, Q3, Max |  | 1 |
| 9322895359 | Standard deviation | A computed measure of how much scores vary around the mean score |  | 2 |
| 9322895301 | Variance | Standard deviation squared |  | 3 |
| 9322895302 | Outliers (defn and 1.5xIQR rule) | Outliers an individual that falls outside the overall pattern. Defined as outlier if observation falls more than 1.5xIQR above Q3 or below Q1 | 4 | |
| 9322895303 | Resistant to Outliers | Large or small values do not affect. Median is resistant to outliers. Mean is affected by outliers. | 5 | |
| 9322895304 | IQR | Q3 - Q1 | 6 | |
| 9322895305 | z-score | standard deviations above or below the mean | 7 | |
| 9322895306 | Percentile | Percent of observation that lie below the value | 8 | |
| 9322895393 | skew right |  | 9 | |
| 9322895394 | skew left |  | 10 | |
| 9322895360 | Scatter plot (how to describe) | STRENGTH (strong/weak) DIRECTION (pos/neg), FORM (linear/non linear) | 11 | |
| 9322895361 | Correlation (defn) | (r)- measures the strength and direction of the linear relationship between two quantitative variables | 12 | |
| 9322895362 | Correlation (properties) | -values close to 1 or -1 indicate scatterplot close to a straight line - values near 0 indicate weak relationship -correlation is not resistant, r is strongly affected by a few outliers" | 13 | |
| 9322895363 | Regression Line | a line that describes how a response variable y changes as an explanatory variable x changes. Used to predict. | 14 | |
| 9322895364 | Slope in context | The predcited increase/ decrease in Y given a change in X | 15 | |
| 9322895365 | Y-intercept in context | When X is zero, the predicted Y value | 16 | |
| 9322895366 | Extrapolation | use of regression line for prediction outside the range of values of explanatory variable. Such predictions are not accurate. | 17 | |
| 9322895367 | Residuals | observed y - predicted y | 18 | |
| 9322895368 | Coefficient of determination | the fraction of the variation in the values of y that is explained by the least square regression line of y on x |  | 19 |
| 9322895369 | Residual Plot | scatterplot of the regression residual. should not show obvious pattern | 20 | |
| 9322895370 | Outlier | observation that lies outside the overall pattern | 21 | |
| 9322895371 | Influential observation | if removed, would markedly change the result of the calculation | 22 | |
| 9322895372 | Lurking variable | A variable that is not among explanatory or response variables, yet may influence interpretation of relationship among variable | 23 | |
| 9322895307 | Experiment | impose a treatment on individuals | 24 | |
| 9322895308 | Observational study | observe individuals and measure variables, but do not attempt to influence response | 25 | |
| 9322895408 | Establish cause and effect relationship | An experiment must be used | 26 | |
| 9322895309 | Population (N) | entire group of individuals we want information about. | 27 | |
| 9322895310 | Sample (n) | part of population we examine in order to gather information | 28 | |
| 9322895311 | Census | attempts to contact every individual in the entire population | 29 | |
| 9322895312 | Voluntary Response sample | individuals choose themselves (ex. Radio call in show) | 30 | |
| 9322895313 | Convenience sample | choosing individuals who are easiest to reach (ex. Stand in front of grocery store) | 31 | |
| 9322895314 | Simple Random Sample (SRS) | every individual has an equal chance to be selected | 32 | |
| 9322895315 | Stratified Random Sample | Divide population into strata that are similar in some way. Then choose a separate SRS in each stratum and combine these SRS to form a full sample | 33 | |
| 9322895316 | Cluster Sample | divide population into groups. Some clusters are randomly selected, then all individuals in clusters are selected to be in sample. | 34 | |
| 9322895317 | Biased sampling method | systematically favors certain outcomes | 35 | |
| 9322895318 | Under-coverage | when some groups are left out of the process of choosong the sample- | 36 | |
| 9322895319 | Non-response | an individual in a sample can't be contacted or does not cooperate | 37 | |
| 9322895320 | Placebo effect | people feel effect of sham treatment | 38 | |
| 9322895321 | Control | we rely on a controlled environment of laboratory to protect from lurking variables or other outside factors | 39 | |
| 9322895322 | Control group | group of patients that receive a sham treatment | 40 | |
| 9322895323 | Randomization | use impersonal chance to assign experimental units to treatments | 41 | |
| 9322895324 | Principals of Experimental Design (3) | Control. Replicate. Randomize | 42 | |
| 9322895325 | Statistical significance | observed effect is so large it would rarely occur by chance | 43 | |
| 9322895326 | Completely Randomized Design | when all experimental units are allocated at random among all treatments | 44 | |
| 9322895327 | Block Design | random assignments of units to treatments is carried out separately within each block. Block- a group of experimental units that are similar in some way | 45 | |
| 9322895328 | Matched pair design | Subjects matched in pairs, each pair receive different treatments | 46 | |
| 9322895329 | Double Blind | neither subject, nor those who measure response variable know which treatment subject received | 47 | |
| 9322895333 | Conditional Probability | P(B | A) | 48 | |
| 9322895334 | Independence | knowing probability of one event does not change the probability that the other occurs | 49 | |
| 9322895335 | Disjoint | mutually exclusive, cannot happen at the same time | 50 | |
| 9322895336 | Mutually exclusive | Cannot happen at the same time | 51 | |
| 9322895337 | Binomial Setting | Check BINS. Binary, Independent, Number of trials fixed. Same probability of success for each trial | 52 | |
| 9322895397 | Binomial Distribution (formula) |  | 53 | |
| 9322895338 | binomial cdf | P(X < or =k) | 54 | |
| 9322895339 | binomial pdf | P(X = k) | 55 | |
| 9322895341 | Distribution of random variables | The values and probability each variable takes. | 56 | |
| 9322895342 | Combining Independent Random variables (means, variance, standard deviations) | Mean add or subtract. Variances add. Standard deviations do NOT. | 57 | |
| 9322895343 | Parameter | describes the population | 58 | |
| 9322895344 | Statistics | describes the sample | 59 | |
| 9322895399 | Standard Deviation for sampling proportion |  | 60 | |
| 9322895402 | Standard Deviation of sampling mean |  | 61 | |
| 9322895345 | Central Limit Theorem | When sample size is large (n>30), the sampling distribution is approximately Normal even if population distribution is skewed | 62 | |
| 9322895347 | Inference | provides methods for drawing conclusions about a population from a sample | 63 | |
| 9322895348 | Confidence interval (formula and definition) | estimate + or - margin of error | 64 | |
| 9322895349 | Confidence Level | probability that the interval will capture the true parameter value in repeated samples | 65 | |
| 9322895350 | Confidence Interval for a mean (population standard deviation known) | ... |  | 66 |
| 9322895403 | Confidence Interval for a mean (population standard deviation unknown) |  | 67 | |
| 9322895352 | Confidence Interval for a proportion | ... |  | 68 |
| 9322895354 | Interpret a confidence interval | We are ____ % confident the true population parameter lies between________. | 69 | |
| 9322895373 | Null Hypothesis | a statement of "no effect", "no difference", or "no change" from historical values | 70 | |
| 9322895374 | Alternative hypothesis | Hypothesis that we are trying to prove (opposite of the null hypothesis) | 71 | |
| 9322895376 | p-value | probability that the observed outcome would take a value as extreme than that actually observed | 72 | |
| 9322895377 | alpha level (significance level) | the probability level used by researchers to indicate the cutoff probability level (highest value) that allows them to reject the null hypothesis | 73 | |
| 9322895380 | Type 1 error | Rejecting the Null Hypothesis when it is true | 74 | |
| 9322895381 | Type 2 error | Failing to reject the Null Hypothesis, when it is false. | 75 | |
| 9322895382 | Probabilities of type 1 error | is the significance level (alpha) of any fixed level test. | 76 | |
| 9322895383 | Probabilities of type 2 error | The probability that the test will fail to reject the Null Hypothesis | 77 | |
| 9322895384 | Power of a test | 1 - Beta (probability of a type 2 test) | 78 | |
| 9322895385 | To increase power of a test | Increase significance level (alpha) Increase sample size (n) Decrease (standard deviation) | 79 | |
| 9322895415 | One sample t-Test mean (formula) |  | 80 | |
| 9322895416 | One sample t-interval (formula) |  | 81 | |
| 9322895387 | Paired t-test | First take difference within each pair, then use the one sample t- procedure | 82 | |
| 9322895417 | One proportion significance test (formula) |  | 83 | |
| 9322895418 | One proportion confidence interval (formula) |  | 84 | |
| 9322895404 | 2 sample t- significance test for means |  | 85 | |
| 9322895405 | 2 sample confidence interval for mean |  | 86 | |
| 9322895406 | 2-sample significance test for proportions |  | 87 | |
| 9322895407 | 2 sample confidence interval for proportion |  | 88 | |
| 9322895419 | Chi- square formula |  | 89 | |
| 9322895390 | Types of chi-square problems | Goodness of fit Homogeneity of populations Association/ Independence | 90 | |
| 9322895391 | Conditions for Chi Square Test | SRS Expected outcomes at least 5 Independence | 91 | |
| 9322895420 | Expected Counts for Matrices |  | 92 | |
| 9322895411 | Degrees of Freedom for Goodness of Fit | (k-1) degrees of freedom k=number of outcome categories | 93 | |
| 9322895412 | Chi square Independence/ Association Degree's of Freedom | (r-1)(c-1) degrees of freedom | 94 | |
| 9322895421 | Confidence Interval for Regression Slope |  | 95 | |
| 9322895414 | Equation for Significance Test for Regression Slope | Hypothesized slope is 0 |  | 96 |
| 9322895423 | Null and Alternative Hypothesis for Regression Slope |  | 97 |
