# 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.

101 Pages

### IPS6e.PPT.Ch02

Course: STAT 1601, Spring 2012
School: Minnesota
Rating:

Word Count: 5093

#### Document Preview

Chapter LookingatDataRelationships Scatterplots IPS 2.1 2009 W. H. Freeman and Company Objectives(IPSChapter2.1) Scatterplots Scatterplots Explanatory and response variables Interpreting scatterplots Outliers Categorical variables in scatterplots Scatterplot smoothers ExaminingRelationships Most statistical studies involve more than one variable. Questions: What individuals does the...

Register Now

#### Unformatted Document Excerpt

Coursehero >> Minnesota >> Minnesota >> STAT 1601

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.
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:

Minnesota - STAT - 1601
Producing Data Design of ExperimentsIPS Chapters 3.1 2009 W.H. Freeman and CompanyObjectives (IPS Chapters 3.1)Design of experiments Anecdotal and available data Comparative experiments Randomization Randomized comparative experiments Cautions
Minnesota - STAT - 1601
Inference for Distributions for the Mean of a PopulationIPS Chapter 7.1 2009 W.H Freeman and CompanyObjectives (IPS Chapter 7.1)Inference for the mean of a population The t distributions The one-sample t confidence interval The one-sample t test
Minnesota - STAT - 1601
Inference for ProportionsInference for a Single ProportionIPS Chapter 8.1 2009 W.H. Freeman and CompanyObjectives (IPS Chapter 8.1)Inference for a single proportionLarge-sample confidence interval for p &quot;Plus four&quot; confidence interval for p Sign
Minnesota - STAT - 1601
AnalysisofTwoWayTablesInferenceforTwoWayTablesIPS Chapter 9.1 2009 W.H. Freeman and CompanyObjectives(IPSChapter9.1)Inference for two-way tables The hypothesis: no association Expected cell counts The chi-square test The chi-square test and the z t
Minnesota - STAT - 1601
ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 24 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 43 44 45 46 47 48 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 68 69 71 72 74 76 77 78 79 80 83 84 85 86 87 88 89GPA 7.94 8.292 4.643 7.47 8
Minnesota - STAT - 1601
NBA Team New York Knicks Los Angeles Lakers Chicago Bulls Detroit Pistons Cleveland Cavaliers Houston Rockets Dallas Mavericks Phoenix Suns Boston Celtics San Antonio Spurs Toronto Raptors Miami Heat Philadelphia 76ers Utah Jazz Washington Wizards Sacrame
Minnesota - STAT - 1601
Minnesota - STAT - 1601
Minnesota - STAT - 1601
Minnesota - STAT - 1601
STAT 1601, Spring 2012SYLLABUSCourse: Introduction to Statistics Class Time: M.W.F. 9:15 AM -10:20 AM in Science 3610 Prerequisite: High school higher algebra Instructor: Jong-Min Kim, Statistics Office: 2380 Science (Tel:589-6341) Office Hours: 3:30 PM
Minnesota - STAT - 1601
T-2TablesProbabilityTable entry for z is the area under the standard normal curve to the left of z.zTABLE A Standard normal probabilitiesz -3.4 -3.3 -3.2 -3.1 -3.0 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3
Minnesota - STAT - 1601
T-4TablesTABLE B Random digitsLine 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 19223 73676 45467
Minnesota - STAT - 1601
T-6Tables TABLE CBinomial probabilitiesEntry is P(X = k) = p n 2 k 0 1 2 0 1 2 3 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 6 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 .01 .9801 .0198 .0001 .9703 .0294 .0003 .9606 .0388 .0006 .02 .9604 .0392 .0004 .9412 .0576 .0012 .92
Minnesota - STAT - 1601
TablesT-11Table entry for p and C is the critical value t with probability p lying to its right and probability C lying between -t and t .Probability pt*TABLE D t distribution critical valuesUpper-tail probability p df 1 2 3 4 5 6 7 8 9 10 11 12 13
Minnesota - STAT - 1601
T-12TablesTable entry for p is the critical value F with probability p lying to its right.Probability pF*TABLE E F critical valuesDegrees of freedom in the numerator p .100 .050 .025 .010 .001 .100 .050 .025 .010 .001 .100 .050 .025 .010 .001 .100 .
Minnesota - STAT - 1601
T-20TablesTable entry for p is the critical value ( 2 ) with probability p lying to its right.Probability p( 2)*TABLE F 2 distribution critical valuesTail probability p df 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Minnesota - BUS - 265
Business Statistics (Summer Session, 2011)Instructor:Dr. Jong-Min Kim Professor of Statistics, University of Minnesota-Morris E-mail: jongmink@morris.umn.edu or kjonomi@hotmail.com Phone #: 011-9061-3605 Course website: http:/cda.mrs.umn.edu/~ jongmink/
Minnesota - BUS - 265
Minnesota - BUS - 265
Chapter 2GraphicalDescriptiveTechniques12.1 IntroductionDescriptive statistics involves thearrangement, summary, and presentation ofdata, to enable meaningful interpretation, and tosupport decision making.Descriptive statistics methods make use
Minnesota - BUS - 265
Chapter 7Random Variablesand DiscreteprobabilityDistributions17.2 Random Variables andProbability DistributionsA random variable is a function or rulethat assigns a numerical value to eachsimple event in a sample space. A random variable reflec
Minnesota - BUS - 265
Chapter 8ContinuousContinuousProbabilityDistributionsDistributions18.2Continuous Probability8.2DistributionsDistributions A continuous random variable has anuncountably infinite number of valuesin the interval (a,b). The probability that a
Minnesota - BUS - 265
Chapter 9SamplingSamplingDistributionsDistributions19.1 Introduction In real life calculating parameters ofInpopulations is prohibitive becausepopulations are very large.populations Rather than investigating the wholeRatherpopulation, we tak
Minnesota - BUS - 265
Chapter 10Introduction toEstimation110.1 IntroductionStatistical inference is the process bywhich we acquire information aboutpopulations from samples.There are two types of inference:EstimationHypotheses testing210.2 Concepts of EstimationTh
Minnesota - BUS - 265
Chapter 11Introduction toHypothesisTesting111.1 Introduction The purpose of hypothesis testing is todetermine whether there is enoughstatistical evidence in favor of a certainbelief about a parameter. Examples Is there statistical evidence in a
Minnesota - BUS - 265
Chapter 12Inference About One Population112.1 Introduction In this chapter we utilize the approach developed before to describe a population. Identify the parameter to be estimated or tested. Specify the parameter's estimator and its sampling distrib
Minnesota - BUS - 265
Chapter 13Inference about Two Populations112.1 Introduction Variety of techniques are presented whose objective is to compare two populations. We are interested in: The difference between two means. The ratio of two variances. The difference between
Minnesota - BUS - 265
Chapter 14StatisticalInference:A Review ofChapters 12 and 13114.1 Introduction In this chapter we build a framework thathelps decide which technique (ortechniques) should be used in solving aproblem. Logical flow chart of techniques forChapter
Minnesota - BUS - 265
Chapter 15Analysis ofVariance15.1 Introduction Analysis of variance compares two ormore populations of interval data. Specifically, we are interested indetermining whether differences existbetween the population means. The procedure works by anal
Minnesota - BUS - 265
Chapter 15 - continuedAnalysis ofVariance15.5 Two-Factor Analysis ofVariance Example 15.3 Suppose in Example 15.1, two factors are tobe examined: The effects of the marketing strategy on sales. Emphasis on convenience Emphasis on quality Emphasi
Michigan State University - ADV - 826
Chapter 11. Advertising: to turn the mind of the prospective customer toward the brand. (P6) Promotion: to produce immediate purchase of the brand. (P7) 2. Direct mail, personal selling, social media ads, etc. (figure 1.1) 3. 4. Because although the use
Michigan State University - ADV - 850
New Tactics: New Media RelationsNews ReleaseBig ChangeWhy change?New technologyNOT MEWhat Changed ?New communication tool and channel New communication habit Audiences OrganizationsNew audiences PublicsWhat is the change?News Release&quot;The role o
Minnesota - BUS - 265
Chapter 16Chi Squared Tests16.1 Introduction Two statistical techniques arepresented, to analyze nominal data. A goodness-of-fit test for the multinomialexperiment. A contingency table test of independence. Both tests use the 2 as the samplingdis
Michigan State University - ADV - 860
1. Definition of PR 1) Definition Ivy Lee: proper adjustment of the interrelations of public and business Edward Bernays: the attempt, by information, persuasion and adjustment, to engineer public support for an activity, cause, movement or institution. H
Minnesota - BUS - 265
Chapter 17Simple LinearSimpleRegression117.1 Introduction In Chapters 17 to 19 we examine therelationship between interval variablesvia a mathematical equation. The motivation for using thetechnique: Forecast the value of a dependentvariable (
Michigan State University - ADV - 865
1. Why have media planning departments acquired increasingly more power and influence in advertising agencies than creative departments? According to Leonard's article, there several reasons that caused the power shift from the creative departments to the
Michigan State University - ADV - 865
Assigned Readings 1) Three readings on ANGEL 2) ACCR a) Introduction, Ch1, Ch2, Ch26, Ch27 b) Ch4 c) Ch21 d) Ch14, Ch15 3) SCA a) Introduction, Ch1 b) Ch2, Ch3, Ch10 c) Ch4, Ch9 d) Ch6 4) AAD a) Ch1, Ch5, Ch61. How critics and defenders of advertising vi
Minnesota - BUS - 265
Chapter 18MultipleRegression118.1 Introduction In this chapter we extend the simplelinear regression model, and allow forany number of independent variables. We expect to build a model that fitsthe data better than the simple linearregression mo
Michigan State University - ADV - 865
1. How critics and defenders of advertising view consumer culture (Pollay &amp; Holbrook articles) 1) UNESCO report Regarded as a form of communication, it (advertising) has been criticized for playing on emotions, simplifying real human situations into stere
Michigan State University - ADV - 865
Regulating the Censorship Placing the Blame In this part, the authors claimed that there is a tendency to blame much of the Economic Censorship on advertisers. It is stated that advertisers are in control and responsible for most consistent and the most p
Minnesota - BUS - 265
StatisticalHeroesStatisticalHeroesFlorenceNightingaleandW.S.GossettFlorenceNightingaleFlorenceNightingaleFlorenceNightingaleFlorenceNightingaleGossettGossett
Michigan State University - ADV - 865
1. Explain the difference between the agency approach and the stakeholder orientation in marketing ethics. 2. According to the U.S. Federal Trade Commission, advertisers can be legally punished (fined, censored, etc.) for causing &quot;consumer harm.&quot; Explain
Minnesota - BUS - 265
1. HistogramSelect Tools &gt; Data Analysis from the mainExcel menu bar.This will bring up the window below.Select Histogram from this window. Thehistogram window should now appear as shownbelow.The following needs to be filled in: The Input Range is
Michigan State University - CAS - 892
Group Cues and Ideological ConstraintIn BriefA) Two expectations a) Common political appeals help to structure candidate preferences around political ideology, and this is done through activating and reinforcing group concerns, especially racial ones. b
Minnesota - CE - 4211
Topic: Fundamentals of Signal Design and TimingHenry Liu CE 4211/5211 Traffic Engineering University of Minnesota email: henryliu@umn.eduFour Basic Mechanism1. 2. 3. 4.Discharge headways at a signalized intersection Critical lane and time budget conce
Michigan State University - CAS - 892
News Frames, Political Cynicism, and Media CynicismBackground InformationA) The problem is that cynicism saps the public's confidence in politics and government, and encourages the assumption that what we see is not what it seems. B) News polls in recen
Michigan State University - CAS - 892
Political Campaign DebateIn BriefA) The primary goal of this paper: to provide a thorough review of the research that has been conducted surrounding televised campaign debates. B) Basic function of debate a) It reaches large audience, more than any othe
Minnesota - CE - 4211
Traffic Simulation What is it?John HourdosTransportation InfrastructureCritical Components of Transportation Infrastructure System Drivers Vehicles Roads d highways R d and hi h Freeway system Rural highway system Arterial d A i l and street system
Michigan State University - CAS - 892
The Influence of Television and Radio Advertising on Candidate EvaluationsIn BriefA) Until 1980s, the persuasive effects of mass media had been downplayed by early students of political propaganda, the &quot;minimal effects&quot; thesis. Scholarship of the 1960s
Minnesota - CE - 4211
t ime_code FWY 4/12/2001 0:00 I-5 4/12/2001 0:05 I-5 4/12/2001 0:10 I-5 4/12/2001 0:15 I-5 4/12/2001 0:20 I-5 4/12/2001 0:25 I-5 4/12/2001 0:30 I-5 4/12/2001 0:35 I-5 4/12/2001 0:40 I-5 4/12/2001 0:45 I-5 4/12/2001 0:50 I-5 4/12/2001 0:55 I-5 4/12/2001 1:
Michigan State University - CAS - 892
The Press EffectBackground InformationA) There is a problem with the information source of press. Many reporters spend most of their time among politicians, and other journalists. When asked for the source, many reporters cite &quot;the people&quot; despite the f
Minnesota - CE - 4211
t ime_code FWY 4/12/2001 0:00 I-5 4/12/2001 0:05 I-5 4/12/2001 0:10 I-5 4/12/2001 0:15 I-5 4/12/2001 0:20 I-5 4/12/2001 0:25 I-5 4/12/2001 0:30 I-5 4/12/2001 0:35 I-5 4/12/2001 0:40 I-5 4/12/2001 0:45 I-5 4/12/2001 0:50 I-5 4/12/2001 0:55 I-5 4/12/2001 1:
Michigan State University - CAS - 892
Overview of the Reading Who Sets the Agenda? Agenda-Setting as a Two-Step Flow Hans-Bernd Brosius &amp; Gabriel Weimann COMMUNICATION RESEARCH, vol. 23 No. 5, October 1996 561-580Overview of the Reading (con't) Four models of a two-step flow of the agend
Minnesota - CE - 4211
t ime_code FWY 3/19/2001 0:00 CA-57 3/19/2001 0:05 CA-57 3/19/2001 0:10 CA-57 3/19/2001 0:15 CA-57 3/19/2001 0:20 CA-57 3/19/2001 0:25 CA-57 3/19/2001 0:30 CA-57 3/19/2001 0:35 CA-57 3/19/2001 0:40 CA-57 3/19/2001 0:45 CA-57 3/19/2001 0:50 CA-57 3/19/2001
Michigan State University - COM - 475
Definition of a CampaignWhat is a campaign? A campaign is intended to generate specific outcomes or effects in a relatively large number of individuals usually within a specified period of time and through and organized set of communication activities. D
Minnesota - CE - 4211
t ime_code FWY 3/22/2001 0:00 CA-91 3/22/2001 0:05 CA-91 3/22/2001 0:10 CA-91 3/22/2001 0:15 CA-91 3/22/2001 0:20 CA-91 3/22/2001 0:25 CA-91 3/22/2001 0:30 CA-91 3/22/2001 0:35 CA-91 3/22/2001 0:40 CA-91 3/22/2001 0:45 CA-91 3/22/2001 0:50 CA-91 3/22/2001
Michigan State University - COM - 475
Audience Analysis Allows you to understand your audience(s) Allows you to better predict behavior and thus, develop messages that appeal to your audience(s) Consists of gathering, interpretation, and application of demographic, behavioral, psychographic
Minnesota - CE - 4211
t ime_code FWY 4/2/2001 0:00 CA-55 4/2/2001 0:05 CA-55 4/2/2001 0:10 CA-55 4/2/2001 0:15 CA-55 4/2/2001 0:20 CA-55 4/2/2001 0:25 CA-55 4/2/2001 0:30 CA-55 4/2/2001 0:35 CA-55 4/2/2001 0:40 CA-55 4/2/2001 0:45 CA-55 4/2/2001 0:50 CA-55 4/2/2001 0:55 CA-55
Michigan State University - COM - 475
The Extended Parallel Process (EPPM) Learning models are too reactive individuals think in response to messages: Fear control process: Emotional processes=fear arousal and fear reduction Danger control process: Cognitive process=formulation of thoughts a
Minnesota - CE - 4211
t ime_code FWY 3/22/2001 0:00 I-5 3/22/2001 0:05 I-5 3/22/2001 0:10 I-5 3/22/2001 0:15 I-5 3/22/2001 0:20 I-5 3/22/2001 0:25 I-5 3/22/2001 0:30 I-5 3/22/2001 0:35 I-5 3/22/2001 0:40 I-5 3/22/2001 0:45 I-5 3/22/2001 0:50 I-5 3/22/2001 0:55 I-5 3/22/2001 1:
Michigan State University - COM - 475
Diffusion of Innovations What does this theory look at? Looks at how people make decisions to adopt new innovations How products, services, and ideas are diffused in populationssystem&quot; Examines &quot;how an innovation, is communicated, over time, among memb
Minnesota - CE - 4211
t ime_code FWY 4/2/2001 0:00 CA-22 4/2/2001 0:05 CA-22 4/2/2001 0:10 CA-22 4/2/2001 0:15 CA-22 4/2/2001 0:20 CA-22 4/2/2001 0:25 CA-22 4/2/2001 0:30 CA-22 4/2/2001 0:35 CA-22 4/2/2001 0:40 CA-22 4/2/2001 0:45 CA-22 4/2/2001 0:50 CA-22 4/2/2001 0:55 CA-22