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CHAPTER MODELING 2: DISTRIBUTIONS QUANTTATIVE SECTION 2.1:Describing Location in Distribution a Percentiles * - pio of less than to it, Standardized scores (z score) tells how many us From the = value where, the mean Falls, and in what direction. value = x n in a distribution value standard deviations M z individual For a n - - equal or mean = o=standard deviation z-score interpretation: "(context) score) is standard deviations &above / below) [+11 SECTION (mean of 2.2: the mean trnit)." - curves is · I underneath it. and under the curve The area ⑳ proportion of all -f · · that Fall observations in 12 interval. 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There unusual features <positive or Equation: L SRL ↑ " y explanatory +bxx a = variable. reachdiving updatednotable. ↳ slope (a + bx) · Residual in 2) Calculate Calculator: equation LSRL 4.) 3 - - - of I - I S Highlight is tickcli is this residual plot. a #identifies if a L INEAR MODEL is (y 5) .weloonyFormula LEETOVER CURVES ↑ ATTERN. the LSRL equation: NotA=E-b The predicted range can Influential points: Outliers: out of of outside which the LSRL greatly affect was correlation of the calculated and regression calculations. pattern (large residuals) Power model: Option Option or 1: very x-values raise the explanatory 2: take response Exponential & Logarithmic - take the OF * one the values of the vanable pth by root an integer, p. of the variable Models: logarithm (log (base 10) variables. or both check the LSRL scatterplot Always before concluding it a LINEAR or In (basee)) & Residual Plot MODEL is 3 (# incontext). x= for the increase (x-context) (increases or the predicted ly-context) by (slope decreases) "When predicted and -rage: very large value was unit of y x] Ix-context) ly-context) is is 0, the ly-int). " e ve n one * data (above 1 below) &residual) - ly-context) actual deviation ly-context) (s): is typically (s+ unit) away by the LSRL "The with from x = actual about the number context). predicted " "About in the 12%% (y+context) LSRL at of the variability accounted for is (x context). by " + - - - - EXPLANATORY Find "The - - ↓ " -cient of determination (re]: APPROPRIATE. NO is and <strong, moderate, weak). negative) residuals & . & between association ted): --- r & confirms = (response) and causation. imply -b in : "For every ) seem y-int v units. number. No a does not of linear the only m linear this relationship. in correlation Cexplanatory) strong) explanatory variable between the - has graph shown. a - linear association. correlation a - Interpretation: scatterplot. this is asks you to when it & ESCRIBE the relationship write DUFS a Fown There of to correlation (r):- only applies preferably, · irection (positive, negative, none * Unusual Feature ("outliers") ... " outcome the study. a ReGRESSION Least-SQUARES Variable (output) Response · variable. response a appropriate. simple random sample (SRS) · CHAPTER 4: COLLECTING DATA SRS? · Technology: individual Label: Label each n - RNG to an get that might to the integers. the sample. strata are * n umerical label with a distinct with the same number of digits two digits use using if (e.g., use Oto NN, o r three digits NNN) to 001 Randomize: Read consecutive the appropriate groups of digits of across a length from left to right line in table D, lignore repeats, if necessary) until number of selected. size desired are sample Label: estimates of unknown population values paper: · letters or in a bowl hat, shuttle the papers let individuals take easier (no replacement). based Select: Group individuals paper they got. on the slip of from observes - but of interest a to ttempt the influence 20 treatments of size 2. In units are paired two treatments assigned individuals unit receives a compare Group (10) F number generator - & additionalencompare -> worker Group 2 -> a productivity the same lighting DESION (h) I Treatment I combine (n) (n) - random assignment & compare Treatment) compare (r) f t -> treatments (x) example: random eshmen # a randomly grade levels - -> number ↑ generator - (100) - &ophomores -> (400) within each pair. In others, each in -> & Block -> MPD, and the are assigned 7 2->Treatment 2 Ch) very similar experimental two (n) Group uses blocks some to assign effective. Treatment 1 -> number generator X I compare 1 sources a inClass combine results & compare X compare in-class -> scores a 2208) A track coach wants to know whether his long-distance runners are faster running the track clockwise or counterclockwise. Design an experiment that uses a matched-pairs design to investigate this question. Explain your method of pairing. in such effects way a cannot be distinguished from each other. · that placebo: treatment active otherwise no ingredient, an · has is like other treatments. ·treatment: applied but a specific condition the individuals in to to which a subjects: human are experimental units. variable Factors: an explanatory that cause is manipulated a change variable. survey question. Description: - Form blocks based on grade level (Individuals + Blocks) because scores on the geometry final exam are likely to vary by grade level since Freshmen who takes geometry tend to be more advanced in their math coursework. - Assign each individual student from 1 to 100 for Freshmen. Use a random number generator to obtain 50 random integers (random assignment), select these students and assign them to online (Block 1 + Treatment 1). The remaining students are assigned to teacher taught (Block 1 + Treatment 2). - Assigned each individual student from 1 to 400 for Sophomores. Use a random number generator again to obtain 200 random integers (random assignment), select these students and assign them to online (Block 2 + Treatment 1). The remaining students are assigned to teacher taught (Block 2 + Treatment 2). - At the end of the course, let them take the same geometry final exam and compare the scores (compare). - Once all students have taken the test, and the scores have been compared for each treatment for each block, then combine the results and compare (combine and compare). and may in the response levels inactive treatment. ·double-blind: neither the · · can subject is receiving. either the subjects the people who interact or with them and measure the response variable don't know which treatment a units using are a subject is experimental assigned to treatments chance process. experimental each a treatment group of experimental units that are experiment before known the to be similar in some to affect way that is expected the response to the treatments. · single-blind: divingassignment: rare all distinguished. be block: subj a for replication: giving units so enough experimental that any difference in the effects · those treatment control: keeping other variables constant units. who interact with them and measure the which response variable know nor · beings x control group: used to provide baseline for comparing the a even an treatment is the · a effects of other treatments. placebo effect: describes the fact that some subjects in an experiment will respond favorably to any treatment, unit: the object randomly assigned. · Factors · experiment. experimental a * response variable on systematic to combination of treatments? when associated that their are two variables values of Factor- occurs ·confounding: levels: different · VOCABS988 random order. Description: Have each long distance runner race 1 mile in each direction. Some runners are faster than others, so using each runner as his or her own “pair” accounts for variation in 1-mile race times among the runners. For each runner, randomly assign the order in which the treatments (clockwise a nd counterclockwise) are assigned — by flipping a coin. Heads indicates the runner will race clockwise first and counterclockwise second; tails indicates the runner will race counterclockwise first and clockwise second. Allow adequate recovery time between the races. For each runner, record the 1-mile race times for each direction. 1503 online (200) random experimental both treatments example: online a Description: - Number the companies from 1 to 20 (20 individuals) - Use a random number generator to produce 10 different random integers from 1 to 20 (random assignment) - Select the first 10 different integers (Group 1) and assign them to additional lighting (Treatment 1) - Select the remaining companies (Group 2) and assign them to the same lighting (Treatment 2). - Compare the increase in worker productivity between the two groups. treatments. groups. For pattern of inaccurate (n) BLOCK DESIGN design for comparing two that X Group" is there on between differences chance random experimental common a is treatment a treatment chosen sample can't be contacted. answers tabled) likely when response bias: occurs Treatments ED moman PAIRS more of the less are individual the not (slips of paper, RN6, units to treatments. This helps create Block individuals to measure their responses. on an is population units so enough experimental treatments can be distinguished effects of the assignment companies BOMIZED delibaretly impose treatments or members population monomn andreassignmenttorepeaterintothebroch (conditions) -> scompare tex random ↑ respons. Experimental - if decide each random individuals does not some representative of a chance process example: measures variables and a WRONG ??? occurs when undercoverage: population. compares two that design -> Observational · pattern a of the study. be be chosen or cannot to chosen in a sample. individual. when occurs in -nonresponse: the sample may the ordered, - RANDOMIZED DESIGN STUDIES OF iF there's over or an AVOID BIAS, WE MIGHT else can go but what k to * the way the lead to can underestimate from Ito first individual, unitsare mmmminLEET experimental individuals TYPES to the that or paper, method to which leads EXPERIMENTAL DESIGN: a Replication: Impose and one the open invitation. sampling of these BIAS, study b/c of sampling and choose every kth be sampling individuals choose be a part of the both * treatments are imposed roughly equivalent groups before avoid Control: keep other variables the same for all groups. Control helps variation in responses, making it confounding and reduces · Randomize: Put random value a identify time & money. Random Assignment: Use identical on s lips of paper. select all the experimental Write corresponding numbers Use Comparison: · · OF to individual selects every 4th & based on the population size desired sample size. Randomly but similar between BASIC PRINCIPLES - - select Randomly ichooses individuals to reach ·voluntary systematic · different within (HETEROGENEOUS saves * paper of sampling Convenience easiest x * c lusters are than SRSs. randomly to the and precise more labels. selected Slips of stratified · x ter in the sample. included are samples tend to give individuals that Select: Choose the correspond between, identical slips clusters individuals in the chosen diFF same · iRNEeD these clusters and similar within CHOMOGENEOUS), but individual each Label: Label to SRSS these combine form near each other. SAMPLES remains SAMPLING sure to do WITHOUT REPLACEMENT doing SRS. when - into non-overlapping groups individuals that are located of * make to be chosen chance sampling the population divide - their response. Then choose a stratum & then select: Choose the individuals ↑able D: way) in some affect the given size cluster · separate necessary). who correspond sampling divide the population in different integers (ignore repeats, if random Strata (similar N. 1 to From Randomize: Use stratified possible sample # + HOW TO CHOOSE AN a every WELL xx SAMPLE gives of MPLING mining TYPES OF SAMPLING POPULATION - sampling variability different random samples size from the & iFF. · of the same same pop. produce estimates. Statistically significant: the · bserved are by results of too unusual to be chance alone. a study explained SAMPLING VARIABILIN & SAMPLE random samples tend to Larger true the C RITERIA closer that are produce estimates to SIZE the 1) value than population FOR and the response variable is larger words, estimates from samples are 2) the or STAMSTICALLY SIGNIFICANT % <5%, yes, IF * 3) and significant have happened Alleged 4) by chance alone 0> 5%, * IF and it the alleged PERCENAGE CP-VALUES Identify the difference in 1) 2 make a simulation Identify in or equal to 4)calculate the or equal compare 5) State if to the to the the significant of the greater in the OF From than Random to context plausible. All INFERENCE of the individuals were will much 7 used be Assignment of individuals of error: margin creates interval an of values. ↑lausible +margin of sample error - estimates conducting * random re the use Randomize (sliprointeC select a effect. WERE lot INDIVIDUALS SELECTED? NO and INDIVIDUALS who & ata First. are RANDOMLY RANDOM ALWAYS IS IMPORTANT 888 ASSIGNED TO GROUPS? NO YES YES Inference about causedeFFect: Inference about population: projects collecting your information Inference about population: YES your future collecting of know do you cannot studies is all data. There it's very things since these 12 since through about so YES RAN DOML the essential to chapters in Chapter it and sampling variability In * here, but these notes * individuals SUMMARY: WERE The cause is believable, not f about groups allows inference cause is cause - All individual data must be kept confidential. Only statistical summaries for groups of subjects and chosen. · in the effect assignment: inference about the population which shows the rull problem. Random selection a low might be difference. 5%rule not continued application time. The - All individuals who are subjects in a study must give their informed consent before data are collected. study is statistically or ME SCOPE · the difference mean in individuals don't. - All planned studies must be reviewed in advance by an institutional review board charged with protecting the safety and well-being of the subjects. dotplot percentage of many dots are now mean. step 1. from mean some and ADexam how many dots are 3) greater and group associated with are DATA ETICS IDENTIFYING THE OF cause to one have consistency in the remain It by coincidence only. PROCESS variable specific possible. happened may have of what the 5) significant not statistically precedes effect long it is no, cause link the association. responses. The individuals variable, a response variablethis reduces other some of the explanatory variable values stronger explanatory it is Statistically itmay larger that study explains one explanatory variable Many studies of different kinds shows consistent. the chance the strong. between the explanatory and precise more is association EXPERIMENT: WAH WE CAN'T DO AN The association between strong. is association random samples. In other smaller ESTABLISHING CAUSATION No YES Inference about causedeFFec: Inference about population: Inference about cause deFFect: NO Inference about population: Inference about cause YES No dCFFer: No 5: PROBABILITY CHAPTER . efinitions: D Formulas: · · of outcomes number total of outcomes number PCA) = ↑(A) space in sample 1 · probability law of · Addition Rule: P(AUB) · P(A) + = P(B) - · mutually exclusive · P(A) simulation · to 1 if - we trials of any more and more observe proportion process, the P(ANB) - no - imitates event can random a outcomes are approaches the true happen at process in such time. the same simulated way that a consistent with real-world outcomes. simulation process: Describe * ② Perform ③use · EAS sample space · = Independent purely probability. P(given event occurs P(B(A) determined are 0 and 1. between large numbers ("given that"): Conditional Probabilities that outcomes generates by chance. outcome - events: = General - random P(AVB) P(A) + P(B) · process -must add P(A) - = for mutually exclusive Rule Addition random event A rule: complement · in · conditional - how the result list of probability · Independent events - other P(A(BY P(AIB) = does event the question. one event whether or not will happens given known to have is event knowing it occurred reptition) trial (one outcomes. that probability - one (repetitions) answer to possible another Events: trials many all simulate you will change not one the that happened. has event probability that the happen. = Or (B) P · P(B(AY = = OR Multiplication Rule: General ·P(B/A) P(A(B) P(A) = · P(BIA) Multiplication Rule for Independent · complement AND Events: P(A-1B) P(A) .P(B) = · At probability one least ↑Lat least HOW Rule: one) = DOES SMALL BE TO HAVE IT to FOR TO US Identify & the ② Perform ③ count the the SAY - convincing percentage evidence or proportion From simulation. number number of of dots out of simulation. there is proportion of From THAT question? IF: ⑭ * o problem the total the the dots #3 (5%, convincing evidence based on the question. proportion of * From #3 - (1) intersection Ad - dots 15%, there is not convincing evidence based A A B 1 2 Ba 3 4 o 5% if there is From (V) & UNUSUAL? IS know union PROBABILITY THIS LESS THAN How - 1 -P (none) on the question. oiras & % C B A 213 4 PLBLNO3P(Arc * pCA) I A *(*1AcP(BYB P(C(yy5C -> P(ANB) P(A'1B) P(A'r) - Random Variables & CHAPTER6: Probability Distributions EFINITIONS: D FORMULAS: · Discrete variables Random P(X * P(X () P(X 1k) P(x = * Mx E(X) -x Mx)-(P) kth) P(X + + (xi)(Pi) + ... (xz)(P2) (xz + - k + D +... + = Mx)YP2) = (Xi +... + - Mx)-(pi) size, number shoe * mean random standard deviation of TransForming SHAPE * FEE of the * Linear transformation variable Random My to same My mean: - same # x1 M: 0. b sd.: Ox a = = + the Normal density and independent Combining curve * sum (S Ms mean: y) x Mx = + = 0x" variance:0 Os s.d.: ①Find Find & · My Mp Mx 0, Ob" = Mp.8 or Os the 0 1 Op. or 1b10x distribution x-y) + Linear * combination mean: - = p) - a x + iFX and 4 + difference of the to find the probability. - · (tria:-ip:_ix-value: 1) -> (triai-ip:_ix-value: () 1- binomedF (tria:-ip:_ix-value: 13 deviation: 0x Mx E(X) - it a · Large counts condition - get n X up * TFP when n samplesize; N Population Geometric ~ . P(X x) * = P(X x) = P(X (x) = (1 -> -> P(X (x) -> P(X(x) -> P(x > x) -> - * - · shape · shape · shape ix value: - - is is - tell of trial about a success. probability. trials number of one other trials. took to get same of anything us takes to it Probability of success get of any o trial, 1. with x-values 1) value): Mx E(X) = 0x is small right-skewed, left-shewed, = · Mean: "It ·Standard Deviation: From condition) · Describing p 0.5 = unit). the were (context) by mean Random Variables randomly (unit) the (0+unit) about of SHAPE, were (context) (M+unit) (Discrete, CENER & I · Continuous, Binomial the is " selected, varies " many many, randomly Describe p > 0.5 "It typically mont) average the about(M+ t 101. pages) chancelprobability probability a many, many selected, right-skewed p (0.5 is (context). OF = (without applying the approximately Normal, CONTEXT: IN Probability: "There x-value: of the distribution: always is trials, until we trials success. starting · 1) geometcdf(p: -i x-value: ( x-value: #) 1- geometcdf(p: -i -i x-value: ( 1- geometcdf(p: when the sample size independent. probability approximately Normal. number of the number of trials it distribution as the success. a variable INTERPRETATIONS geometcdf(p: -i standard deviation: * shape Geometric that we the . p)*-(p) mean (expected is the outcome random with Variables geometpdf (p: * · from the mean perform independent we = · · individual each identiful Probability? p of same size = n(1-p) >10 Large Counts condition((cc): up<10; Random us does not o r Trial variable random is not independent, = * Geometric binomial a setting treat to a · of Binary? "success" o r "Fail" outcome Independent? 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"success" W independent. -> P(X(x) ↑(X (x) value of one variable independent do values or the the standard deviation. square of - we - · sum predict us of the other variable. particular outcome bMy P(X x) binompdr binomial · "Op s.d... 0 o normaled Use & = = of (02) R.V. 0E help cannot -knowing the change My = the Find P(X x) (()(p) -(1 * 0x" = the Find Variables Random * - = I when - & standard mean given. 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