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Utah Valley University - MRKT - MRKT 3600
McGrawHill/IrwinCopyright 2011byTheMcGrawHillCompanies,Inc.Allrightsreserved.LEARNING OBJECTIVES (LO)AFTER READING CHAPTER 19, YOU SHOULD BE ABLE TO:LO1Explain the differences between product advertising andinstitutional advertising and the variation
Utah Valley University - MRKT - MRKT 3600
McGrawHill/IrwinCopyright 2011byTheMcGrawHillCompanies,Inc.Allrightsreserved.LEARNING OBJECTIVES (LO)AFTER READING CHAPTER 20, YOU SHOULD BE ABLE TO:LO1Discuss the nature and scope of personal selling and salesmanagement in marketing.LO2Identify th
Utah Valley University - MRKT - MRKT 3600
McGrawHill/IrwinCopyright 2011byTheMcGrawHillCompanies,Inc.Allrightsreserved.LEARNING OBJECTIVES (LO)AFTER READING CHAPTER 21, YOU SHOULD BE ABLE TO:LO1LO2LO3Describe what interactive marketingis and how it creates customer value, customerrelation
Utah Valley University - MRKT - MRKT 3600
McGrawHill/IrwinCopyright 2011byTheMcGrawHillCompanies,Inc.Allrightsreserved.LEARNING OBJECTIVES (LO)AFTER READING CHAPTER 22, YOU SHOULD BE ABLE TO:LO1Explain how marketing managersallocate their limited resources.LO2Describe two marketing plannin
Utah Valley University - MATH - MATH 2040
2 Sample Tests FlowchartComparingSample descriptionAssumptions1 and 2 knownTest2 sample zIndependent samples1 and/or 2 not knownMeansx1 and x 22 sample tDependent samples(Repeated measurementsOn the same or very similarSubjects/units)Propo
Utah Valley University - MATH - MATH 2040
CHAPTER 9 CONFIDENCE INTERVALS9.1 Confidence Intervals For The Mean When is knownIf samples are taken from a normally distributed populationThen over the long run 95% of the values should be in the = populationinterval 1.96 ( = population meanstanda
Utah Valley University - MATH - MATH 2040
9.3,9.4 Confidence Intervals For Proportions andTesting Hypotheses About ProportionsResearchers often want to estimate the proportion of apopulation with a particular characteristic. For example, APhysical Anthropologist may want to know the fraction
Utah Valley University - MATH - MATH 2040
2040 Exam Two TopicsChp. 4 Regression Be able to:Calculate r (TI or Excel)Calculate r2 using SST = SSR + SSEEstimate r (w/in 0.2) from a scatter plotState what r and r2 measureFind the least square regression line y = b1x + b0 using:1) TI or Excel
Utah Valley University - MATH - MATH 2040
2040 Exam 3 ReviewChp. 7 & 8 The Normal Distributiona) Calculate probabilities for a continuous uniform distribution.b) Properties of the normal distribution: continuous and - < x < , total area = 1, symmetric aboutthe mean, probabilities of being in
Utah Valley University - MATH - MATH 2040
1.1 IntroductionStatisticsThe collection, organization, summarization, andanalysis of informationThe Two Branches of Statistics- 1) Descriptive Statistics__2) Inferential Statistics__Types of Data1) Qualitative ___2) Quantitative ___a) Con
Utah Valley University - MATH - MATH 2040
1.5 Sources of Errors in SamplingNon Sampling (Biased) Errors1) Bias in the selection of individuals to be included inthe sample population (e.g. incomplete frame)2) Bias in the selection of individuals from the samplepopulation. These are errors aft
Utah Valley University - MATH - MATH 2040
2.1 Categorical (Qualitative) Data DisplaysClass Level of Students Applying For a ScholarshipFr = FreshmanFrJrFrSoSrFrSrSoFrFrSoFrJrFrSrSoFrFrJrFrJrFrJrSoSo = SophomoreFrSoSoFrSoFrJrSoJr=JuniorFrFrFrSoFrSoSrSoF
Utah Valley University - MATH - MATH 2040
2.2 Quantitative DisplaysAcerage of 39 national parks under 800,000 acres(in 1,000s ofacres)413618352065050519676066338617746294701432332471327106751324677523630821752265285391696477567402220Frequency TablesAcres
Utah Valley University - MATH - MATH 2040
3.1 MEASURES OF CENTRAL LOCATION (TYPICAL)In a previous lecture it was noted that data can be labeled as categorical or quantatitive.We need to also note that the data we obtain may represent all the observations possible;we call this a population. If
Utah Valley University - MATH - MATH 2040
3.2 Measures of VariationSo far our in our study of statistics we have used descriptivemeasures of the central location (mean, median, mode) ofpopulations and samples. However we also would like to makeinferences about the populations from which sampl
Utah Valley University - MATH - MATH 2040
3.4 Measures of PositionChebyshev's TheormFor any set of data (including populations and samples) and anyconstant k > 1, the proportion of the data that lies within kstandard deviations of the mean is at least:1As an example of this theorem let's con
Utah Valley University - MATH - MATH 2040
CHAPTER 4 RELATIONSHIPS BETWEEN TWO VARIABLESGraphical DisplayNumerical summariesscatter plotr, r2, and y = ax + b4.1 Scatter Plots and Correlationscatter plotX1Y72339415511Y151296312345Correlationr - measures the strength of
Utah Valley University - MATH - MATH 2040
5.1 ProbabilitiesIn every day experience, terms such as almost certain or quiteunlikely are often used. In science however we must be more precisein the terms. We would also like to be able to quantify statements.The quantification of the degree of ce
Utah Valley University - MATH - MATH 2040
5.2 Properties of ProbabilityCompliments and The Addition RuleFrom the above definitions we can quickly develop a fewproperties of probability . For example, the probability ofan event (E) can be no less than 0(it never occurs) and nomore than 1 ( it
Utah Valley University - MATH - MATH 2040
5.5Counting Permutations and CombinationsIn calculating classical probabilities we will need to be able tocount both the number of possible outcomes of an experiment and thenumber of outcomes that will be considered success. For example, ifwe are tos
Utah Valley University - MATH - MATH 2040
5.6 CONDITIONAL PROBABILTYandBAYES THEOREMBayes theorem is used when we know the conditional of A given B but want to calculatethe conditionalof B given A. For example, assume we have data that show that 10% ofall deaths are due to lung cancer and 40
Utah Valley University - MATH - MATH 2040
6.1 Discrete Random VariablesAssume we perform the experiment of rolling two distinctDice and observe the number of dots on the top face. TheSample space for this experiment is:(1,1)(2,1)(3,1)(4,1)(5,1)(6,1)(1,2)(2,2)(3,2)(4,2)(5,2)(6,2)(1
Utah Valley University - MATH - MATH 2040
6.2 The Binomial DistributionA binomial distribution occurs when an experiment with twopossible outcomes (success or failure) is repeated severaltimes. Each repetition of the experiment is called a trial.Examples of binomial distributions are:a) The
Utah Valley University - MATH - MATH 2040
Chapter 7 Continuous distributions7.1 The Normal and Uniform DistributionsDiscrete sample spaces have a countable number of elements, i.e.experimental outcomes. Each possible outcome for a random variablevalue has a probability between 0 and 1. The su
Utah Valley University - MATH - MATH 2040
7.5Normal Approximation To The BinomialAs the value of n increases the binomial distribution (withparameters n and p) becomes more bell shaped and approachesthe normal distribution with = np and = npq . Thenormal distribution is a reasonable approxim
Utah Valley University - MATH - MATH 2040
8.1 Mean And Standard Error Of The Sample MeanAssume we have a population with the valuespopulationhas = 4and2 =2=2,3,4,5,6. ThisWe now take all possible non ordered samples of size two from thepopulation and calculate the mean of each pair.pair
Utah Valley University - MATH - MATH 2040
13 ANOVA (Comparison of Three or more means)Previously we looked at 2-sample Z and 2-sample T tests, which can be used to test if two samplescame from populations with the same mean. In this chapter, we will learn a test to check if three ormore sample
Utah Valley University - MATH - MATH 2040
Chapter 14 Simple Linear RegressionHypotheses tests and Confidence IntervalsIn simple linear regression we assume there is alinear relationship between the explanatoryvariable (x) and the response variable (y). Forexample, assume the growth rate (y)
Utah Valley University - MATH - MATH 2040
10.1TESTS OF HYPOTHESESAn important area of inferential statistics is the testing of claims.Examples:Is the average volume of bottles for a particular brand of milk at least128 ozs.?Is a newly develop drug more effective at curing a disease than cur
Utah Valley University - MATH - MATH 2040
10.4 Tests Involving Single ProportionsLarge samplesz=A researcher claims the population proportion = .30Test this claim given the sample data n = 600 and the numberin the sample with the characteristic of interest = 1571) H0: P = .30Ha: p .302)a
Utah Valley University - MATH - MATH 2040
11.1 Paired T tests, Comparison of Two Means FromDependent SamplesThere are instances when we want to use the t test to comparetwo means; however the test is complicated by confoundingvariables. For example, two types of baseball bat arecompared and
Utah Valley University - MATH - MATH 2040
11.2 Inferences About Two Means, IndependentSamples1) Two sample Z test (comparison of two means)When testing if two population means are equal (1 = 2) we usethe two sample Z statistic:( x1 x 2 ) ( 1 2 )Z=note: both 1 and 2 are known2 12 2+n1n
Utah Valley University - MATH - MATH 2040
11.3 Tests Comparing Two ProportionsOur next statistical test will compare two sample proportionsp1' = p2' =This test assumes large sample sizes, thus the standard normal zdistribution is used.z=p1-p2 is the standard deviation for the difference bet
Utah Valley University - MATH - MATH 2040
11.4 F TEST FOR THE COMPARISON OF TWO VARIANCESIn addition to tests comparing the means or proportions of two populations, there aretimes when we want to compare the variances of two populations. For example, inselecting between two manufacturing proce
Utah Valley University - MATH - MATH 2040
Math 2040 Exam 1 fall 2009Name_(Show your work to receive credit)1) Circle all the following charateristics that apply to the histograma) unimodalb) bimodalc) skewed to the rightd) skewed to theleft2) According to the textbook which of the follow
Utah Valley University - MATH - MATH 2040
Math 2040 Exam 2 summer 2010 Name _SHOW your work to receive credit1) A study was made to determine if the distance of a home from the nearest fire station increases thedamage that a fire will cause to the home and the following dat were obtained.Dist
Utah Valley University - MATH - MATH 2040
Math 2040 Exam 3 spring 2010Name_KEY_SHOW YOUR WORK TO RECEIVE CREDIT1) As given in the exam three review sheet, list two characteristicsof the normal distribution (other than the empirical rule)_see exam three review__2) Assume that infants after
Utah Valley University - MATH - MATH 2040
Math 2040 Exam 3 spring 2010Name_SHOW YOUR WORK TO RECEIVE CREDIT1) As given in the exam three review sheet, list two characteristicsof the normal distribution (other than the empirical rule)__2) Assume that infants after a typical gestation period
Utah Valley University - MATH - MATH 2040
Math 2040 exam 4 spring 2010 Name_SHOW YOUR WORK TO RECEIVE CREDIT1) A statistical test was performed as follows:Ho: = 42sample mean 40.4 = 4.8 n =16Ha: < 42Assuming the sample means are normally distributed:a) What is the p-value of the above test
Utah Valley University - MATH - MATH 2040
Math 2040 exam 4 spring 2010 Name_SHOW YOUR WORK TO RECEIVE CREDIT1) A statistical test was performed as follows:Ho: = 42sample mean 40.4 = 4.8 n =16Ha: < 42Assuming the sample means are normally distributed:a) What is the p-value of the above test
Utah Valley University - MATH - MATH 2040
Chp. 10 One Sample Hypothesis TestsA) Ho, Ha, Type I and Type II errors, , p-valueB) Perform and interpret single sample hypothesis tests concerning1) w/ use a z testTISTAT TESTS Z-TestPHStat 1 Sample TestsZ test for the mean knownw/ s use a t te
Utah Valley University - MATH - MATH 2040
2040 Homework fall 20101.29,10,11,19bcde1.39 for part b use seed 768, 11 for part b seed = 7291.4115,29,30,351.5 2,3,13,17,23,311.61.5,1.6,1.7,1.8,13,232.12.22.32.412,13,15,231,3,4,6,9,10,17,41,477,92,4a,5,69,113.13.23.33.413,16,18,2
Utah Valley University - MATH - MATH 2040
TENTATIVE SCHEDULEDateSectionsExam/HwDate8/268/271.1,1.21.2,1.38/308/319/29/31.41.5,1.62.1,2.22.3,2.410/25 9.1,9.210/26 9.210/28 9.2,9.3.10/29 9.59/69/79/99/10Labor Day3.13.1,3.23.211/111/211/411/5ReviewExam Chps. 7,8,91
Utah Valley University - MATH - MATH 2040
MATH 2040 COURSE SYLLABUSInstructor:Ray SieversOffice:LA022TOffice Hours: TR 11:00 -11:30 W 10:00-10:50 F 2:00-2:50 and byappointmentText:Statistics: Informed Decisions Using Data by Michael Sullivan(3rd ed)e-mail:sieverra@uvu.eduPrerequisite:
Utah Valley University - MATH - MATH 2040
Chapter 12 Chi-Square TestsUsed to analyze categorical variables and thus looks atfrequencies in different categories.Two types of tests:1) Goodness of fit(of a variable to a hypothesizeddistribution)2) Independence(of the distributions of several
Utah Valley University - MATH - MATH 2040
2040 Exam 1 TopicsChp1. Introduction, Types of Data, Sampling, Types of Studies1)The two branches of statistics (descriptive and inferential)Types of dataa)categorical vs. numericalb)continuous vs. discrete2)Sampling techniques and errors in sam
Utah Valley University - MATH - MATH 2040
Homework 6 Tech 1010 Understanding TechnologyOur PowerPoint posted in the course room in week 6,7, lists 5 shstems that are necessary invehicles:StructurePropulsionSuspensionGuidanceControlGive an example of each.
Utah Valley University - MATH - MATH 2040
Tech 1010 Quiz 6 Study GuideQuestions on the energy content of fuelsQuestion on the cause of knock in an engineQuestions on the Ford innovation and production rate
Utah Valley University - MGMT - MGMT 3000
MGMT 3000: Organizational BehaviorTEAM PROJECT ASSIGNMENTThe purpose of this group project is twofold:1. To provide hands-on experience in applying OB theories to managerialproblems.2. To provide experience in working in groups .Your job, as a group
Utah Valley University - MGMT - MGMT 3000
Feedback form for Team ProjectsManagement 3010Your Name_Please allocate points to each of your team members and yourself in relation to theiroverall contribution to the four projects you completed during the semester. You have100 points per member of
Utah Valley University - MGMT - MGMT 3000
MEMORANDUMTO:FROM:DATE:SUBJECT:The Pointe Dance AcademyKristin Bentley, Jake Bingham, and Jill Wilde23 November 2011Suggestions to improve communication and staff moraleWe are sending this memo in response to your request that we identify and res
Utah Valley University - MGMT - MGMT 3000
Group Paper Outline?Introduction:WHAT ARE GMOS?Genetically modified foods (GM foods or GMO foods) are foods derived from genetically modifiedorganisms (GMOs). Genetically modified organisms have had specific changes introduced into their DNA bygeneti
Utah Valley University - MGMT - MGMT 3000
Company Description:History:The company originally began a few years back with just the simple raising ofminks on farms. In these mink farms, minks were raised for the selling of fur to localbusiness and companies that were searching for this product
Utah Valley University - MGMT - MGMT 3000
International Law:Dispute Resolution:The United States and Japan have been strong allies throughout history, but havealso seen disputes throughout the year when it come to business and international trade.Throughout the years and more commonly the gui
Utah Valley University - MGMT - MGMT 3000
Motivation: The Key to a Successful WorkplaceMotivation: The Key to a Successful WorkplaceKrissy BentleyUtah Valley UniversityOrganizational Behavior13 November 2011Motivation: The Key to a Successful WorkplaceProfessor Taggart FrosAbstractClearl
Utah Valley University - MGMT - MGMT 2340
Accountants3626332831CFOs516548566534586529546523538523551552486558574CommissionsPlayerSalary ($000)3.9 Rodriguez, Alex$28,0005.7 Giambi, Jason$23,4297.3 Jeter, Derek$21,60010.6 Abreau, Bobby$16,00013 Pettite, Andy$16,000
Utah Valley University - MGMT - MGMT 2340
MGMT 2340Section W01Spring 2010Chapters 1 - 4For the quizzes & tests, some of the following formulas might be helpful:22 = 4; 23 = 8; 24 = 16; 25 = 32; 26 = 64; 27 = 128; 28 = 256i=X=H-LkX=1 ( x - X )2s=n -11 = 682 = 9522 ( wx)wXw =
Utah Valley University - MGMT - MGMT 2340
MGMT 2340 Section W01Business Statistics IInstructor: E. Mark Leanycontact via Blackboardonline.uen.orgalternately:professorleany@gmail.comStatistics YOU Might UseSports - Example: Babe Ruth'sCareer Batting Average is .342Whose opinion to listen
Utah Valley University - MGMT - MGMT 2340
MGMT 2340 Section W01Business Statistics IInstructor: E. Mark Leanycontact via Blackboardonline.uen.orgalternately:professorleany@gmail.comDescribing Data:Frequency Tables, FrequencyDistributions, and Graphic PresentationChapter 221GOALS - Cha
Utah Valley University - MGMT - MGMT 2340
MGMT 2340 Section W01Business Statistics IInstructor: E. Mark Leanycontact via Blackboardonline.uen.orgalternately:professorleany@gmail.comDescribing Data:Numerical MeasuresChapter 355McGraw-Hill/IrwinCopyright 2010 by The McGraw-Hill Companie
Utah Valley University - MGMT - MGMT 2340
MGMT 2340 Section W01Business Statistics IInstructor: E. Mark Leanycontact via Blackboardonline.uen.orgalternately:Professor.Leany@gmail.comDescribing Data:Displaying and Exploring DataChapter 499GOALS - Chapter 41.2.3.4.5.6.7.Develop a