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31 Pages

### 501_Lecture_08

Course: STAT 501, Spring 2012
School: Purdue University -...
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Word Count: 1289

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8.1 Inference Section for a Single Proportion Recall: Population Proportion Let p be the proportion of &quot;successes&quot; in a population. A random sample of size n is selected, and X is the count of successes in the sample. Suppose n is small relative to the population size, so that X can be regarded as a binomial random variable with X = np and X = np(1 - p) Recall: Population Proportion...

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8.1 Inference Section for a Single Proportion Recall: Population Proportion Let p be the proportion of "successes" in a population. A random sample of size n is selected, and X is the count of successes in the sample. Suppose n is small relative to the population size, so that X can be regarded as a binomial random variable with X = np and X = np(1 - p) Recall: Population Proportion We use the sample proportion p = ^ n the population proportion p. X as an estimator of ^ p is an unbiased estimator of p, with mean and SD: p = p ^ and p = ^ p (1 - p ) n ^ When n is large, p is approximately normal. Thus z= ^ p- p p (1 - p ) n is approximately standard normal. CI for a Population Proportion ^ Since p is normal. ^ The standard error of p is ^ SE ( p) = ^ ^ p (1 - p) n An approximate level C confidence interval for p: ^ ^ ^ p z SE ( p ) = p z * * ^ ^ p(1 - p) n where P(Z z*) = (1 C)/2. CI for a Population Proportion The margin of error is ^ m = z SE ( p ) = z * * ^ ^ p(1 - p ) n Use this interval when the successes and failures are both at least 15 Use Table A or last row of Table D to find z*. Example: A news program constructs a call-in poll about a proposed city ban on handguns. 2372 people call in to the show. Of these, 1921 oppose the ban. Construct a 95% confidence interval for the true proportion of people who oppose the ban. What are the possible problems with the study design? Solution: Note: Since p is a proportion, if you ever get an upper limit value of > 1 or lower <0 while calculating the CI, replace by 1 and 0 (respectively). Choosing a Sample Size If we want to estimate the proportion p within a specified margin of error m, the required sample size is (at least): n= ^ ^ ( p) ( 1- p) ( z ) m 2 * 2 Choosing a Sample Size ^ Since p is unknown before the data is collected, we use any prior information we have to get a rough known estimate, p*. 2 ( p ) ( 1- p ) ( z ) n= * * * m2 This is especially important if you believe p is close to 0 or 1. Where might we find previous information about p? If you have no information, we may replace p*, above, with 0.5 to obtain the most conservative * 2 * 2 sample size. ( 0.5) ( 1 - 0.5 ) ( z ) ( z ) n= m 2 = 4m 2 Example (handguns revisited): Assume that we plan to ask randomly chosen people from the phone book. We would like to have a margin of error of 0.03=3%. How big a sample size should we have now? Another example: Suppose that the results of a survey of 2,000 television viewers at 11:40p.m. on Monday September 28, 1998 were recorded, and it was determined that 226 viewers watched "The Tonight Show." Estimate with 95% confidence the number of TVs tuned to "The Tonight Show" if there are 100 million potential television sets. Testing for a single population proportion ^ When n is large, p is approximately normal, so z= ^ p - p0 p0 (1 - p0 ) n is approximately standard normal. We may test H0: p = p0 against one of these: Ha: p > p0 Ha: p < p0 Ha: p p0 Large-sample Significance Test for a Population Proportion The null hypothesis, H0: p = p0 The test statistic is z= ^ p - p0 p0 (1 - p0 ) n P-value P(Z z) P(Z z) 2P(Z | z |) Alternative Hypothesis Ha: p > p0 Ha: p < p0 Ha: p p0 Large-sample Significance Test for a Population Proportion How big does the sample size need to be? The general rule of thumb to use here, as before for approximation of binomial distribution by normal distribution, is np0 10, n(1 - p0 ) 10 Example: A claim is made that only 34% of all college students have part-time jobs. You are a little skeptical of this result and decide to conduct an experiment to show more students work. You get a sample of 100 college students and find that 47 of these students have parttime jobs. Conduct hypothesis a test with = 0.05 to determine whether more than 34% of college students have parttime jobs. SAS Programs proportion.doc But mostly hand computations. Section 8.2 Comparing Two Proportions Comparing Two Proportions Before we begin... Intuitively, how do you think we will be comparing two proportions? Think in terms of two means, what did we do there? Comparing Two Proportions Notation: Populatio Population Sample Count of n proportion size successes 1 2 p1 p2 n1 n2 X1 X2 Comparing Two Proportions SRS of size n1 from a large population having proportion p1 of successes and an independent SRS of size n2 from another large population having proportion p2 of successes. ^ p1 is an estimator of p1 ^ p2 is an estimator of p2: X ^ p1 = 1 n1 X2 ^ p2 = n2 Comparing Two Proportions: properties of estimators We have p1 = p1 ^ p2 = p2 ^ p1 = ^ p2 = ^ p1 (1 - p1 ) n1 p2 (1 - p2 ) n2 Comparing Two Proportions: approximate normality Compare p1 and p2 means to examine p1 p2. This can be done by studying the difference ^ ^ p1 - p2 We obtain an approximate standard normal variable ^ ^ ( p1 - p2 ) - ( p1 - p2 ) z= p1 (1 - p1 ) p2 (1 - p2 ) + n1 n2 Significance Test Comparing Two Population Proportions When p1 and p2 are unknown, we want to carry out hypothesis testing for H0: p1 = p2 (same as p1 p2=0) against one of the following alternatives: Ha: p1 > p2 Ha: p1 < p2 Ha: p1 p2 Comparing Two Population Proportions: Significance Test Under the null hypothesis H0: p1 = p2, we view all the data as coming from a single population with proportion p1=p2=p (p unknown). The z-statistic becomes ^ ^ ^ ^ ( p1 - p2 ) ( p1 - p2 ) z= = p (1 - p ) p (1 - p ) 1 1 + p(1 - p ) + n n n1 n2 2 1 Since p is unknown, we use the pooled sample ^ proportion p to estimate it: p = X 1 + X 2 ^ n1 + n2 Comparing Two Population Proportions- Significance Test Null hypothesis: H0: p1 = p2 ^ ^ ( p1 - p2 ) The test statistic: z = 1 1 ^ ^ p (1 - p ) + n1 n2 Alternative Hypothesis Ha: p1 > p2 Ha: p1 < p2 Ha: p1 p2 P-value P(Z z) P(Z z) 2*P(Z | z |) Example: In a highly-publicized study, doctors confirmed earlier observations that aspirin seems to help prevent heart attacks. The research project employed 21,996 male American physicians. Half of these took an aspirin tablet every other day, while the other half took a placebo on the same schedule. After 3 years, researchers determined that 139 of those who took aspirin and 239 of those who took placebo had had heart attacks. Determine whether these results indicate that aspirin is effective in reducing the incidence of heart attacks at significance level 0.05. Confidence Interval: We are interested in constructing a confidence interval for p1 p2. The estimate for this is ^ ^ p1 - p2 ^ ^ The standard error of p1 - p2 is defined as SE p1 - p2 = ^ ^ ^ ^ ^ ^ p1 (1 - p1 ) p2 (1 - p2 ) + n1 n2 Confidence Interval - Formula: An approximate level C confidence interval for p1 p2 is given by * ^ ^ ^ ^ ( p1 - p2 ) z SE ( p1 - p2 ) * ^ ^ = ( p1 - p2 ) z ^ ^ ^ ^ p1 (1 - p1 ) p2 (1 - p2 ) + n1 n2 where P(Z z*) = (1 C)/2 Confidence Interval-margin of error: The margin of error is given by ^ ^ m = z SE ( p1 - p2 ) = z * * ^ ^ ^ ^ p1 (1 - p1 ) p2 (1 - p2 ) + n1 n2 Use this interval when the number of successes and the number of failures in each sample are at least 10. Example (Aspirin and Heart Attacks): Estimate with 95% confidence the difference in proportion of men risking a heart attack (in the next 3 years) among aspirin takers and non-takers. Relative risk: ^ p1 RR = ^ p2 Example: Calculate the relative risk for the aspirin example: Software gives confidence intervals (based on data) for population relative risk: p1 / p2 This is another way of comparing the two population proportions.
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Purdue University - Main Campus - STAT - 501
Chapter 9Two categorical variables. Data Analysis and Inference for Two-Way TablesTopicsImportant change: We switch from quantitative variables to categorical variables describing relations in two-way tables marginal distributions conditional distribut
Purdue University - Main Campus - STAT - 501
Section 10.1Simple Linear RegressionA continuation of Chapter 2Statistical model for linear regression Data for simple linear regression Estimation of the parameters Confidence intervals and significance tests Confidence intervals for mean response vs.
Purdue University - Main Campus - STAT - 501
Section 11.1Multiple Linear Regression (MLR)Topics-MLRExtension of SLR Statistical model Estimation of the parameters and interpretation R-square with MLR Anova Table F-Test and t-testsA continuation of Chapter 10Most things are conceptually similar
Purdue University - Main Campus - STAT - 501
Section 12.1One-Way Analysis of Variance (ANOVA)Inference for One-Way ANOVAComparing means for several groups Format of data An analogy: two sample t-statistic ANOVA hypotheses and model Understanding two types of variation Estimates of population para
Purdue University - Main Campus - STAT - 501
STAT 501Experimental Statistics IPurpose: To explain the essential ideas and concepts of applied statistics, including numericalsummaries, graphing, hypothesis testing, confidence intervals, two-way tables and the Chi-square,regression and ANOVA. Also
Purdue University - Main Campus - STAT - 501
If you find a mistake, please email me ASAP: colvertn@stat.purdue.edu1.a. 89.248b. 0.2215c. (61.3, 88.7)2.a. 0.0479b. No.c. 0.02563.a. 0.9951b. 0.80304.a.b.c.d.N( = 0.7, = 0.0458)0.1379(0.6084, 0.7916)457 guarantees less than 0.01.5.
Purdue University - Main Campus - STAT - 501
Correlation Exampleoptions ls=72;title1 'Gesell Correlation Example';data gesell;infile 'C:\gesell.txt';input name \$ age score;yrs = age/12; /* creating new variable &quot;yrs&quot; whichconverts age in months to age in years */run;symbol value = circle;p
Purdue University - Main Campus - STAT - 501
Exam 1 Review-Summary of topicsChapter 1 Individuals Categorical and Quantitative variables Graphical tools for categorical variables Bar Chart Pie Chart Graphical tools for quantitative variables Stem and leaf plot Histogram Distributions Describe: Shap
Purdue University - Main Campus - STAT - 501
Test of Mean(s):Hypotheses:One SampleH 0:Ha:Two Sample = 0 &gt; 0 &lt; 0 0H 0:Ha:IF you DO know :X 0nTest of Significance:z=Confidence Interval:X z*n(XX 0snt=1IF you do NOT know :Test of Significance:t=Confidence Interval:X t *sn
Purdue University - Main Campus - STAT - 501
Experiments on learning in animals sometimesmeasure how long it takes a mouse to find itsway through a maze. The mean time is 20seconds for one particular maze. A researcherthinks that playing rap music will cause the miceto complete the maze faster
Purdue University - Main Campus - STAT - 501
Getting Started in SASSome general instructions regarding homeworks:Do not use an alternate program(s) to make any of your graphs, for now just make themby hand, in homework 2 we will start using SAS!Occasionally I will give special instructions for s
Purdue University - Main Campus - STAT - 501
1 Sample t-test in SASoptions nodate pageno=1;goptions colors=(none);title1 'One sample t-test in SAS';data one;infile 'C:\gesell.txt';input name \$ age score;run;proc print data=one;run;One sample t-test in SASObs12345678910111213
Purdue University - Main Campus - STAT - 501
Matched Pairs t-test in SASoptions nodate pageno=1;goptions colors=(none);title 'Analysis of Aggressive behaviors - Example 7.7';data moon;input patient aggmoon aggother;aggdiff = aggmoon - aggother;datalines;1 3.330.272 3.670.593 2.670.324
Purdue University - Main Campus - STAT - 501
2 Sample t-test in SASoptions nodate pageno=1;goptions colors=(none);title1 'DRP data - Example 7.14';data drp;input group score @;datalines;1 24 1 43 1 58 1 71 1 43 1 49 1 61 1 441 67 1 49 1 53 1 56 1 59 1 52 1 62 1 541 57 1 33 1 46 1 43 1 572
Purdue University - Main Campus - STAT - 501
Two-Way Tables in SASoptions nodate pageno=1;goptions colors=(none);title 'Age versus College Program - class example';data one;input age \$ program \$ count;datalines;18below2full3618below2part9818below4full7518below4part3718to212full1
Purdue University - Main Campus - STAT - 502
Stat 502 Topic #2 CLG HandoutActivity #1Data concerning statewide average SAT scores was obtained from www.amstat.org. A complete description ofthe data set may be found at http:/www.amstat.org/publications/jse/datasets/sat.txt . Variables in this data
Purdue University - Main Campus - STAT - 502
Topic 8 Handout: One Way Analysis of VarianceLearning Goals for this Activity: (1) Learn the relationships in the ANOVA table and understand theassociated F test; (2) Understand how to get estimates for cell means model (using the output from SAS); (3)
Purdue University - Main Campus - STAT - 502
Topic 10 Handout: ANCOVA &amp; RCBD DesignsLearning Goals for this Activity: (1) Experience in the use of ANCOVA and RCBD Models; (2) Anunderstanding of why such models are worthwhile.10.1Output for Example II (from notes) is given below on pages 1-2. Com
Purdue University - Main Campus - STAT - 502
Topic 11 Handout: Two Way Analysis of VarianceLearning Goals: (1) Learn how to analyze two factors in ANOVA; (2) Understandand be able to properly interpret interactions.11.1Pages 2 and 3 show six possible interaction plots for an analysis of drugeff
Purdue University - Main Campus - STAT - 502
Topic 12 Handout: CARS exampleLearning Goals: (1) Explore the difference between balanced and unbalanced ANOVAdesigns; (2) Further understanding of interactions; (3) Learn about confounding of effectsin ANOVA.The questions are based on the following s
Purdue University - Main Campus - STAT - 502
Topic 13 Handout: Random EffectsLearning Goals: (1) Understand the differences between fixed effects and random effectsmodels; (2) Be able to identify effects as either fixed or random; (3) Be able to utilize EMS(expected mean squares) to determine (a)
Purdue University - Main Campus - STAT - 502
STAT 502 Assignment #1Coverage: KKMN Chapters 1-3Name: _SCORE: _ of 30Instructions: Although I do encourage you to work together both in and outside of class, remember thatcollaboration on homework problems should be minimal and everyone should creat
Purdue University - Main Campus - STAT - 502
STAT 502 Assignment #1Coverage: KKMN Chapters 1-3Name: _SCORE: _ of 30Instructions: Although I do encourage you to work together both in and outside of class, remember thatcollaboration on homework problems should be minimal and everyone should creat
Purdue University - Main Campus - STAT - 502
STAT 502 Assignment #2Coverage: KKMN Chapters 4-7Name: _SCORE: _ of 40Homework ProblemsThese should be done individually!#1.(3 pts) Briefly explain the difference between the following two equations. Do either of theseconstitute a complete statemen
Purdue University - Main Campus - STAT - 502
STAT 502 Assignment #3Coverage: KKMN Chapter 8-9Name: _SCORE: _ of 40Homework ProblemsThese should be done individually!Please note that HW problems generally do not require any SAS coding.#1.(6 points) KKMN Problem 8.03 as stated. Please note that
Purdue University - Main Campus - STAT - 502
STAT 502 Assignment #4Coverage: KKMN Chapter 9Name: _SCORE: _ of 40Homework ProblemsThese should be done individually!Please note that HW problems generally do not require SAS coding.#1.(6 points) KKMN Problem 9.02.#2.(4 points) KKMN Problem 9.03
Purdue University - Main Campus - STAT - 502
STAT 502 Assignment #5Coverage: KKMN Chapters 10 &amp; 12Name: _SCORE: _ of 40Homework ProblemsThese should be done individually!Please note that HW problems generally do not require SAS coding.#1.(7 points) KKMN Problem 10.07. Ignore the questions in
Purdue University - Main Campus - STAT - 502
Topic 1 Basic StatisticsKKMN Chapters 1-31Topic OverviewCourse Syllabus &amp; ScheduleReview: Basic StatisticsTerminology: Being able to CommunicateDistributions: Normal, T, FHypothesis Testing &amp; Confidence IntervalsSignificance Level &amp; Power2Textb
Purdue University - Main Campus - STAT - 502
Topic 2 Simple Linear RegressionKKMN Chapters 4 71OverviewRegression Models; Scatter PlotsEstimation and Inference in SLRSAS GPLOT ProcedureSAS REG ProcedureANOVA Table &amp; Coefficient ofDetermination (R2)2Simple Linear Regression ModelWe take n
Purdue University - Main Campus - STAT - 502
CLG Activity #1Please discuss questions 3.1-3.6from the handout.CLG Activity #1Q1: Research Questions?Is there an effect of smoking on SBP?Is body size associated to SBP?Can any combination of the three variablesbe used to predict SBP?CLG Activit
Purdue University - Main Campus - STAT - 502
Topic 3 Multiple Regression AnalysisRegression on Several PredictorVariables(Chapter 8)1Topic OverviewSystolic Blood Pressure ExampleMultiple Regression ModelsSAS Output for RegressionMulticollinearity2Systolic Blood Pressure DataIn this topic
Purdue University - Main Campus - STAT - 502
Topic 4 CLG Addendum1CLG Activity #1In your groups, please attempt thefirst set of questions. These willinvolve computing various extrasums of squares.2Question 4.1 &amp; 4.2Univariate and bivariate issues that mayaffect modeling include:For HSM, H
Purdue University - Main Campus - STAT - 502
Topic 4 Extra Sums of Squaresand the General Linear TestUsing Partial F Tests in MultipleRegression Analysis(Chapter 9)1Recall: Types of Tests1.ANOVA F Test: Does the group of predictorvariables explain a significant percentage of thevariation i
Purdue University - Main Campus - STAT - 502
CLG ActivityIn CLGs, please attempt Activity#1 from the handout for Topic 5.Note that this activity builds on theone we used for Topic #4.15.1 (a) &amp; (b)2GPA, HSM | HSS , HSE , SATM , SATVr2GPA , HSE | HSMr6.835== 0.061105.65 + 6.8352.48=
Purdue University - Main Campus - STAT - 502
Topic 5 Partial Correlations; Diagnostics &amp;Remedial MeasuresChapters 10 &amp; 141OverviewReview: MLR Tests &amp; Extra SSPartial Correlations Think Extra SS beingused to compute Extra R2Of the variation left to explain.How much isexplained by adding anot
Purdue University - Main Campus - STAT - 502
Topic 6 Model SelectionSelecting the BestRegression Model(Chapters 9 &amp; 16)1OverviewWe already have many of the pieces inplace. Well use those to developalgorithmic procedures.There are some additional statistics that canbe used for comparison of
Purdue University - Main Campus - STAT - 502
Topic 7 Other Regression IssuesReading: Some parts ofChapters 11 and 15OverviewConfounding (Chapter 11)Interaction (Chapter 11)Using Polynomial Terms (Chapter 15)Regression: Primary GoalsWe usually are focused on one of thefollowing goals:Predic
Purdue University - Main Campus - STAT - 502
Topic 8 One-Way ANOVASingle Factor Analysis of VarianceReading: 17.1, 17.2, &amp; 17.5Skim: 12.3, 17.3, 17.41OverviewNote: Entire topic constitutes some reviewas this would be the last thing you coveredin 501. We will cover it perhaps insomewhat more
Purdue University - Main Campus - STAT - 502
Topic 8 One-Way ANOVASingle Factor Analysis of VarianceReading: 17.1, 17.2, &amp; 17.5Skim: 12.3, 17.3, 17.41OverviewNote: Entire topic constitutes some reviewas this would be the last thing you coveredin 501. We will cover it perhaps insomewhat more
Purdue University - Main Campus - STAT - 502
Topic 9 Multiple ComparisonsMultiple Comparisonsof Treatment MeansReading: 17.7-17.81OverviewBrief Review of One-Way ANOVAPairwise Comparisons of Treatment MeansMultiplicity of TestingLinear Combinations &amp; Contrasts ofTreatment Means2Review: O
Purdue University - Main Campus - STAT - 502
Topic 10 ANCOVA &amp; RCBDAnalysis of Covariance (Ch. 13)Randomized Complete BlockDesigns (Ch. 18)1ReviewRecall the idea of confounding. Suppose I want todraw inference about a certain predictor.If meaningfully different interpretations would bemade
Purdue University - Main Campus - STAT - 502
Topic 11 ANOVA IIBalanced Two-Way ANOVA(Chapter 19)1Two Way ANOVAWe are now interested the combined effectsof two factors, A and B, on a response (note:text refers to these as R = rows andC = columns well call them A, B, and laterC for a 3-way AN
Purdue University - Main Campus - STAT - 502
Topic 12 Further Topics in ANOVAUnequal Cell Sizes(Chapter 20)1OverviewWell start with the Learning Activity.More practice in interpreting ANOVA results; anda baby-step into 3-way ANOVA.An illustration of the problems that anunbalanced design wil
Purdue University - Main Campus - STAT - 502
Topic 13 Random EffectsBackground Reading:Parts of Chapters 17 and 191Reading SummarySection 17.1 (particularly page 422)Section 17.6 (pages 438-440)Section 19.7 (pages 538-541)2Random EffectsSo far we have really only dealt with fixedeffects w
Purdue University - Main Campus - STAT - 502
Topic 14 Experimental DesignCrossoverNested FactorsRepeated Measures1OverviewWe will conclude the course by consideringsome different topics that can arise in amulti-way ANOVA, as well as some othermiscellaneous topics.Some of these are discusse
Purdue University - Main Campus - STAT - 506
STAT 506Homework 1You should first go the the website and download the Data folder for the Programming 1course, it is available in a zip file called prg1.zip. I suggest you use your H: drive for storagebut its really up to you. You will need to unzip i
Purdue University - Main Campus - STAT - 506
STAT 506Homework 2For most problems you will still need to access the data in the PRG1 folder. Use the libname statement we learned toload this each time you work on your assignments. You may now call it orion to be consistent with the SASmaterials. Th
Purdue University - Main Campus - STAT - 506
STAT 506Homework 3For most problems you will still need to access the data in the PRG1 folder. Use the libname statement we learned toload this each time you work on your assignments calling the library orion. I tried to bold the parts where I expectyo
Purdue University - Main Campus - STAT - 506
STAT 506Homework 4For most problems you will still need to access the data in the PRG1 folder. Use the libname statement we learned toload this each time you work on your assignments calling the library orion. I tried to bold the parts where I expectyo
Purdue University - Main Campus - STAT - 506
STAT 506Homework 5For most problems you will still need to access the data in the PRG1 folder. Use the libname statement we learned toload this each time you work on your assignments calling the library orion. I tried to bold the parts where I expectyo
Purdue University - Main Campus - STAT - 506
STAT 506Homework 6For most problems you will need to access the data in the PRG2 folder. Use the libname statement we learned to loadthis each time you work on your assignments calling the library orion. I tried to bold the parts where I expect you toa
Purdue University - Main Campus - STAT - 506
STAT 506Homework 7For most problems you will need to access the data in the PRG2 folder. Use the libname statement we learned to loadthis each time you work on your assignments calling the library orion. I tried to bold the parts where I expect you toa
Purdue University - Main Campus - STAT - 506
Chapter 1: Introduction1.1 Course Logistics1.2 Purpose of the Macro Facility1.3 Program Flow1Chapter 1: Introduction1.1 Course Logistics1.2 Purpose of the Macro Facility1.3 Program Flow2Objectives3Explain the naming convention that is used for
Purdue University - Main Campus - STAT - 506
Chapter 2: Macro Variables2.1 Introduction to Macro Variables2.2 Automatic Macro Variables2.3 Macro Variable References2.4 User-Defined Macro Variables2.5 Delimiting Macro Variable References2.6 Macro Functions1Chapter 2: Macro Variables2.1 Intro
Purdue University - Main Campus - STAT - 506
Chapter 3: Macro Definitions3.1 Defining and Calling a Macro3.2 Macro Parameters3.3 Macro Storage (Self-Study)1Chapter 3: Macro Definitions3.1 Defining and Calling a Macro3.2 Macro Parameters3.3 Macro Storage (Self-Study)2Objectives3Define and
Purdue University - Main Campus - STAT - 506
Chapter 4: DATA Step and SQL Interfaces4.1 Creating Macro Variables in the DATA Step4.2 Indirect References to Macro Variables4.3 Retrieving Macro Variables in the DATA Step(Self-Study)4.4 Creating Macro Variables in SQL1Chapter 4: DATA Step and SQ
Purdue University - Main Campus - STAT - 506
Chapter 5: Macro Programs5.1 Conditional Processing5.2 Parameter Validation5.3 Iterative Processing5.4 Global and Local Symbol Tables1Chapter 5: Macro Programs5.1 Conditional Processing5.2 Parameter Validation5.3 Iterative Processing5.4 Global a
Purdue University - Main Campus - STAT - 506
Chapter 6: Learning More6.1: SAS Resources6.2: Beyond This Course1Chapter 6: Learning More6.1: SAS Resources6.2: Beyond This Course2Objectives3Identify areas of support that SAS offers.EducationComprehensive training to deliver greater valuet
Purdue University - Main Campus - STAT - 506
Chapter 1: Introduction1.1 Course Logistics1.2 An Overview of Foundation SAS1Chapter 1: Introduction1.1 Course Logistics1.2 An Overview of Foundation SAS2Objectives3Explain the naming convention that is used for thecourse files.Compare the thr
Purdue University - Main Campus - STAT - 506
Chapter 2: Getting Started with SAS2.1 Introduction to SAS Programs2.2 Submitting a SAS Program1Chapter 2: Getting Started with SAS2.1 Introduction to SAS Programs2.2 Submitting a SAS Program2Objectives3List the components of a SAS program.Stat
Purdue University - Main Campus - STAT - 506
Chapter 3: Working with SAS Syntax3.1 Mastering Fundamental Concepts3.2 Diagnosing and Correcting Syntax Errors1Chapter 3: Working with SAS Syntax3.1 Mastering Fundamental Concepts3.2 Diagnosing and Correcting Syntax Errors2Objectives3Identify t