[Statistics experts only]
It is Uncertainty, data, and decision class. attatched is the syllabus
The problem set is going to look like the attached files. also has to use radiant program.
I will upload the problem sets at 9:30 pm EST on Oct 23 Sunday.
And then you need to give me the answer within an hour.
This is really important for me to do well so please please if you are not willing to do or not showing up on the time please please don't do it.
please ! thank you
Session Topic(s) Lecture Notes and Deliverable(s) 1 [Course Introduction] Radiant installed «Course structure before class «Requirements and grading Lecture 1 «Introduction to Radiant (Bootcamp) 2 [Data Manipulation and Visualization] Bootcamp «Data visualizaion in Radiant «Data manipulation in Radiant «Data loading and saving in Radiant 3 [Essential Probability: Chd, 5, 6] Cectures 2,3 « Probability (Conditional, Bayes’ Rule) « Discrete random variables 4 + Continuous random variables Lecture 3 5 [Essential Statistics: Ch 2, 3] Lecture 4 « Descriptive statistics « Covariance and correlation § [Statistical Inference: Ch 7, 8, 8, 10, 11] Tecture 5 + Sampling 7 «= Population and sample Lecture 6 « Central Limit Theorem « Confidence intervals. 8 ~ Hypothesis testing Lecture 7 © Type | and Type ll errors o Significance o One-tailed and two-tailed tests 9 MIDTERM EXAM 10 + Comparing means and proportions. Lecture 8 1 + Cross-tabs Lecture 9 12 [Linear Regression: Ch 12, 13] Lecture 10 «Simple linear regression 13 + Multiple linear regression Lecture 11 + R?, p-values «Transformations 14 + Model evaluation Lecture 11 © Model fit Linearity Normality Mulicolinearity Heteroschedasticity Autocorrelation boooo Chap 9 ohm 10 . 24 Null : n o diArena aits43 . a. The hypothesis are used to te St a chain that he Promotion of those A , Alternative : them is a diArena → Want tocheckwhethertherehasbeenasignihiantwhowiuusethl.catR에 " 9에바다에 inaeageintemeandemesticaitetmeandemesticairfa.nlis Greater in The current yan 10% , → Alternative ! than thepreviousyear-yone.si ded teH → Greater : o ne - tail est Ho : M d ≤ o Ho : p ≤ O . 10 H a : M >OH a : P 70 . 10 → sampuizeis.mu ⇒ Use todistribution 坪器 b. p = : # of People with " Yes " n : Simple current Paris diAN" 30 3 45 315 526 463 63 420 462 - 42 216 NG l O 285 재5 IO 405 432 -27 635 585 50 710 650 60 605 545 60 517 547 -30 570 508 62 6 10 58o 30 → P쯚 E = 2쁨 = 23 ( i N = 0.05 hypothesis teH 야FTA쀼 = 38.80 z= 5¥ = 0.13€ ≈ 2 七二 琴喆帖 = 2.054埠裵 df = M = 12-1 의 ( p - value = 0.032 <0.05 →="" righttail="" d="" tehaip="P" (="" z=""> D = p (z < i="" )="0" .="" 1587="" (zscoretable)="" p=""> 0.05 → tail to rejeH Ho Sina p -value is Greater thanthesignihiancek.ve/d.wedonrlrejeHthenull hypothesis , So , te Eagle should My with he Promotion , SIna rValue is USS than a , We refH The null hypothesis and Atom He Alternative one Ha. wecanwndudethatthedometcairfare.is Greater in theorem Year than inthepmiousye.am →expatedb.amN ller E = 5쁨 = 487 preiousyear.ir讐二 464 랴玗一뼡퍖 Chip 12 . 12.x . TH of Independence →usethechisquanedt.05Hoithequali.lyratio is Independent of He onnot Education H a : thequalityr.at ng is not Independent of he own is Education X로 6.573 dftr.DK- 1) → construct The table of Oh teNe d = (3) (4-1)=6 and expectedfrequencie.jp - value= 0.362 → observe.de = (R아댮팖깒땛遐 sina.pevalue is Greater than ois,he null hypothesismuttndberejected.weanwndudthequalityra.cing is Independent of theowne.rs Education , b. Avg : 29 % Outstanding : 46% Exoptical : 25% From they mph and themreNa ye , We could He that not ofthenespondentsthinkthattheirnewlypurchasedautomob.ie ☐ Outstanding So , on H a for are Nine What asaid with theirpurchase.andeuenfeweraneveymuchsati.fied .