# Register now to access 7 million high quality study materials (What's Course Hero?) Course Hero is the premier provider of high quality online educational resources. With millions of study documents, online tutors, digital flashcards and free courseware, Course Hero is helping students learn more efficiently and effectively. Whether you're interested in exploring new subjects or mastering key topics for your next exam, Course Hero has the tools you need to achieve your goals.

7 Pages

### Statistics324_HW6

Course: STAT 324, Fall 2006
School: Wisconsin
Rating:

Word Count: 492

#### Document Preview

324 Statistics Discussion 311 w/ Jack Homework 6 Victoria Yakovleva 1. with(vitcap, t.test(vital.capacity~group, conf.level=0.99)) Welch Two Sample t-test data: vital.capacity by group t = -2.9228, df = 19.019, p-value = 0.008724 alternative hypothesis: true difference in means is not equal to 0 99 percent confidence interval: -2.06447665 -0.02219002 sample estimates: mean in group 1 mean in group 3 3.949167...

Register Now

#### Unformatted Document Excerpt

Coursehero >> Wisconsin >> Wisconsin >> STAT 324

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
324 Statistics Discussion 311 w/ Jack Homework 6 Victoria Yakovleva 1. with(vitcap, t.test(vital.capacity~group, conf.level=0.99)) Welch Two Sample t-test data: vital.capacity by group t = -2.9228, df = 19.019, p-value = 0.008724 alternative hypothesis: true difference in means is not equal to 0 99 percent confidence interval: -2.06447665 -0.02219002 sample estimates: mean in group 1 mean in group 3 3.949167 4.992500 The result of this comparison may be misleading because there are only a total of 24 workers whose vital capacities were collected. 2. Paired t-test data: pre and post t = 11.9414, df = 10, p-value = 3.059e-07 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 1074.072 1566.838 sample estimates: mean of the differences 1320.455 dd<-intake\$post-intake\$pre str(dd) num [1:11] -1350 -1250 -1755 -1020 -745 ... avg<-with(intake,(post+pre)/2) str(avg) num [1:11] 4585 4845 4762 5670 6018 ... xyplot(dd~avg,type=c("g","p"), aspect="iso") If there is less variability in the differences than in the averages, then reducing the data to the differences will eliminate an important source of variability and provide a more sensitive test. 3. Group 1 was given placebo first, Group 2 was given treatment first. A period effect represents a systematic difference between the two groups--for example, if the treatment has a long-lasting effect, then getting the treatment first could make you feel better even when you were taking the placebo later. If we ignore a potential period effect, we can analyze for a drug effect using a simple paired t-test: with(ashina, t.test(vas.active, vas.plac, paired=T)) Paired t-test data: vas.active and vas.plac t = -3.2269, = df 15, p-value = 0.005644 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -71.1946 -14.5554 sample estimates: mean of the differences -42.875 The better way would be to plot the data using a comparative box-and-whisker plot to observe intra-individual differences. If there isn't a period effect, then the distribution of the within-subject differences should be the same for the two groups: diff<-with(ashina, vas.active-vas.plac) bwplot(~diff|grp, aspect = 1, ashina, layout = c(1, 2), type = c("g", "p", "smooth"), auto.key = list(space = "top", columns = 2)) If there were only a period effect present, the intra-individual differences in the two groups would not overlap at all. This isn't the case. This improved method is definitely more informative about the whole set of data. 5. stand <- rnorm(25, m = 0, sd = 1) t.test(stand) One Sample t-test data: stand t = -0.8982, df = 24, p-value = 0.378 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: -0.6245537 0.2457716 sample estimates: mean of x -0.1893911 pvals<- replicate(5000, t.test(rnorm(25))\$p.value) The distribution should be a cumulative distribution--a continuous sigmoidal shaped function that extends from p=0 to p=1. The proportion of the p-values less than 0.05 is 0.0522. We expect the proportion of pvalues less than 0.05 to be 0.05. This fits. pvals2<- replicate(5000, t.test(rt(25, df = 2))\$p.value) pvals3<-replicate(5000, t.test(rexp(25), mu=1)\$p.value) ints <- replicate(1000, t.test(rnorm(25))\$conf.int) sum(ints[1,] * ints[2, ] < 0) 931 intervals contained zero. In theory, we expect 1000 intervals to contain zero.
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

Wisconsin - STAT - 324
Statistics 324 Discussion 311 w/ Jack Homework 7 Victoria Yakovleva 1. xyplot(body.weight~metabolic.rate,data = rmr,type=c(&quot;g&quot;,&quot;p&quot;,&quot;smooth&quot;)According to this graph, a simple linear regression model doesn't seem reasonable. xyplot(body.weight~meta
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
Wisconsin - EMA - 202
UT Arlington - INSY - 3300
Lab 4 Some TCP/IP UtilitiesDue on March 5th Using any Windows XP workstation with internet connection that is accessible to you (e.g., your own computer or university computer at libraries), please finish the following task individually.Objective
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 1 Getting Started with AliceObjectives Design a simple Alice program Build a simple Alice program Animate Alice objects by sending them messages Use the Alice doInOrder and doTogether controlsAlice in Action
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 2 MethodsObjectives Build world-level methods to help organize a story into scenes and shots Build class-level methods to elicit desirable behaviors from objects Reuse a class-level method in multiple worlds U
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 3 Variables and FunctionsObjectives Use variables to store values for use later in a method Use a variable to store the value of an arithmetic expression Use a variable to store the value produced by a function
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 4 Flow ControlObjectives Use the Boolean type and its basic operations Use the if statement to perform some statements while skipping others Use the for and while statements to perform (other) statements more t
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 7 From Alice to JavaObjectives Write some first Java programs Learn the basics of the Eclipse Integrated Development Environment (IDE) Begin making the transition from Alice to JavaAlice in Action with Java
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 8 Types and ExpressionsObjectives Use variables and constants Understand the difference between Javas fundamental and class types Build complex Java expressions Better understand and use Javas Scanner and Prin
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 9 MethodsObjectives Build your own Java methods Define parameters and pass arguments to them Distinguish between class and instance methods Build a method libraryAlice in Action with Java2Methods How to
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 10 Flow Control in JavaObjectives Learn how to use the if statement Learn how to use the switch statement Learn how to use the while loop Learn how to use the for loop Learn how to use the do loopAlice in A
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 11 Files and ExceptionsObjectives Open and close text files Read values from, and write values to, text files Catch, handle, and throw exceptionsAlice in Action with Java2Files and Exceptions Main topics
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 12 Arrays and Lists in JavaObjectives Understand Java's array data structure Solve problems using Java's LinkedList data structure Solve problems using Java's ArrayList data structureAlice in Action with Java
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 13 Object-Oriented ProgrammingObjectives Design and build class hierarchies Understand inheritance and polymorphism Override inherited methods Build abstract classesAlice in Action with Java2Object-Orien
UT Arlington - INSY - 3300
Alice in Action with JavaChapter 14 Events and GUIsObjectives Use Java's event classes to model user events Design and build listener classes to handle user events Design and build applications that have simple graphical user interfaces (GUIs)
Texas A&M - POLS - 206
The Presidency Founding Fathers How did the FF conceive of the presidency? o Fearful of a strong executive o Knew Pres would have to be strong sometimes Biggest problem w/ &quot;Articles&quot; was lack of executive branch o To win approval of the Constitution
Texas A&M - POLS - 206
The Judiciary Iron TrianglesCongressInterest GroupBureaucracyScenario 1Committee/ Subcommittee AgingInterest Group Bureaucratic Agency AARP Social Security Administration Tobacco Industry Defense Contractors US Dept. of Agriculture Defens
Texas A&M - POLS - 206
Ch 14 Bureaucracy Independent Regulatory Commissions Maintain independence from presidency and politicians Deal w/ complex economic or technical issues o Congress wants to keep these decisions out of the hands of politicians Federal elections commi
Texas A&M - MGMT - 209
Contract law- system of rules for enforcing promises Roles of contracts: 1) Facilitation of mutually beneficial trade 2) Allocation of risk of future events Contract may be terminated by: 1) Performance 2) Breach 3) Discharge Executory Contract has
Texas A&M - MATH - 142
Spring 2006i) Drost !\1J\TIJ 142 IX;\M 21\MATH 142 Exam 2 Spring 2006Print: I~a Section:Regular~Part A: There are eIght mu Part B: There are eight wo shown for full credit. Pain problem. &quot;An Aggie does not lie, ct Please sign below that the
Texas A&M - COSC - 253
ANNOUNCEMENTS: Project news If you have not found a working group or need a member to complete your team, please go to Web CT and post your need in the DISCUSSION section Per the project guidelines: Email Mr. Jo your decision to form a group or be
Texas A&M - INFO - 209
William Carter INFO209 - 501 Query Design and Results Query 1:Query1Department Department ID Criminal Law d-18 Environmental Law d-13 Human Resources Law d-15 Intellectual Property Law d-16 International Law d-17 Internet Law d-14 Personal Injury
Texas A&M - INFO - 209
William Carter INFO209 - 501 Query 1:Query1Department Department ID Criminal Law d-18 Environmental Law d-13 Human Resources Law d-15 Intellectual Property Law d-16 International Law d-17 Internet Law d-14 Personal Injury Law d-11 Tax Law d-1203
Wisconsin - AFRO AMER - 210
Sikhuluma in &quot;Sikhuluma&quot; and Ngomba in &quot;Ngomba's Basket&quot; both undergo puberty rites of passage by confronting the negative forces within themselves. In order to fully become adults, they must leave their negative, or childhood sides behind as represe
Wisconsin - PHYS - 201
Physics 201: Lecture 2Kinematics One dimensional motion Equations of motion for constant acceleration3/27/08Physics 201, UW-Madison1Motion in 1 dimension Motion: change in position with time. In 1-D, we usually write position as x(t).
Wisconsin - ANTHRO - 100
Study QuestionsBy: Garrett Thomas 1. The primary criteria for determining the age of a skeleton are using the bones to determine how old the individual was. For example, changes in the long bone epiphysis through time can help determine age. The age
Wisconsin - ANTHRO - 100
Anthropology Study Guide -The first stone tools were made 2.6 million years ago in East Africa. -Beginning 2.5 million years ago, sites in East Africa provide evidence of early Hominids and the genus Homo is made. The early Homo dates to at least 2.4
Wisconsin - ANTHRO - 100
Comp Lit Frankenstein notes monster originates from a violation of a social norm Brunhil, Fafneir is monsterous because Fafneir kills father Chiron didn't violate social norm, seems nice Cohen says 7 main reasons for monsters- The monsters body is a
Wisconsin - ANTHRO - 100
March 3rd Comp Lit Notes Richard is an unusual monster Deprives himself of his own humanity He describes himself as monster in beginning because of physical deformity He is a charmer Says hideous and ugly and dogs bark at him but does well with Anne