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48 Pages

### 501_Lecture_07

Course: STAT 501, Spring 2012
School: Purdue University -...
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Word Count: 1845

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7.1 Inference Section for the mean of a population Change: Population standard deviation () is now unknown The t distribution One-sample t confidence interval One-sample t test Matched pairs t procedures Robustness of t procedures The t distribution: The goal is to estimate or test for an unknown in the situation when is also unknown. Solution: estimate by s and use it intelligently in the formulas....

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7.1 Inference Section for the mean of a population Change: Population standard deviation () is now unknown The t distribution One-sample t confidence interval One-sample t test Matched pairs t procedures Robustness of t procedures The t distribution: The goal is to estimate or test for an unknown in the situation when is also unknown. Solution: estimate by s and use it intelligently in the formulas. Challenge: the distribution of the test statistic will change and will no longer be the standard normal distribution. Sampling Normal Population, Unknown Standard Deviation Suppose an SRS X1, ..., Xn is selected from a normally distributed population with mean and standard deviation . Assume that and are both unknown. We know that X ~ N , n When is unknown, we estimate its value with the sample standard deviation s. ( ) Sampling Normal Population, Unknown Standard Deviation The standard deviation of X can be estimated by s SE X = n This quantity is called the standard error of the sample mean. The test statistic (appropriately standardized sample mean) will no longer be normally distributed when we use the standard error. The test statistic will have a new distribution, called the t (or Student's t) distribution. The t-test Statistic and Distribution Suppose that an SRS of size n is drawn from an N(, ) population. Then the one-sample t-statistic t= x - 0 s n has the t distribution with n 1 degrees of freedom. There is a different t distribution for each sample size. The degrees of freedom for the t-statistic "come" from the sample standard deviation s. The density curve of a t distribution with k degrees of freedom is symmetric about 0 and bell-shaped. The t-test Statistic and Distribution The higher the degrees of freedom (df) are, the narrower the spread of the t distribution n1 < n2 df = n2 df = n1 0 As the df increase, the t density curve approaches the N(0, 1) curve more closely Generally it is more spread than the normal, especially if the df are small One-sample t Confidence Interval Suppose a SRS of size n is drawn from a population having unknown mean . A level C confidence interval for is x t * s , n or * s * s x , x +t -t n n Here t* is the value for the t density curve with the df = n-1. The area between t* and t* is C The interval is exact for the normal population and approximately correct for large n in other cases Note the standard error in the formula Example 1 A mutual fund is trying to estimate the return on investment in companies that won quality awards last year. A random sample of 20 such companies is selected, and the return on investment is calculated. The mean of the sample is 14.75 and the standard deviation of the sample is 8.18. Construct a 95% confidence interval for the mean return on investment. Example 2 From a running production of corn soy blend we take a sample to measure content of vitamin C. The results are: 26 31 23 22 11 22 14 31 Find a 95% confidence interval for the content of vitamin C in this production. Give the margin of error. One-Sample t Test Suppose that an SRS of size n is drawn from a population having unknown mean . To test the hypothesis H0: = 0 based on an SRS of size n, compute the one-sample t statistic x - 0 t= s n Note the standard error in the denominator. One-Sample t Test In terms of a random variable T with t (n1) distribution, the P-value for a test of H0: = 0 against... Ha: > 0 is P(T t) Ha: < 0 is P(T t) Ha: 0 is 2P(T | t |) P-values These P-values are exact if the population distribution is normal and are approximately correct for large n in other cases. Use Table D to know the range of the Pvalues, software gives exact p-values Example 2 (vitamin C continued): Test whether the vitamin C content from the sample conforms to specifications of 40. Example (vitamin C continued): Test whether the vitamin C content from the sample is lower than the specifications of 40. Matched Pairs t Procedures Inference about a parameter of a single distribution is less common than comparative inference. In certain circumstances a comparative study makes use of single-sample t procedures. In a matched pairs study, subjects are matched in pairs; the outcomes are compared within each matched pair. One typical situation here is "before" and "after" (quantitative) observations of the same subject. Matched Pairs t Test A matched pairs analysis is appropriate when there are two measurements or observations per each individual and we examine the change from the first to the second. Typically, the observations are "before" and "after" measures in some sense. For each individual, subtract the "before" measure from the "after" measure. Analyze the difference using the one-sample confidence interval or significance-testing t procedures (with H0: diff = 0). Example 20 French teachers attend a course to improve their skills. The teachers take a Modern Language Association's listening test at the beginning of the course and at it's end. The maximum possible score on the test is 36. The differences in each participant's "before" and "after" scores have sample mean 2.5 and sample standard deviation 2.893. Is the improvement significant? Construct a 95% confidence interval for the mean improvement (in the entire population). Robustness of t Procedures A statistical inference is called robust if it's outcome is not sensitive to the violation of the assumptions made. Real populations are never exactly normal. Usefulness of the t procedures in practice depends on how strongly they are affected by non-normality. If they are not strongly affected, we say that they are robust. The t procedures are robust against non-normality of the population except in the case of outliers or strong skewness. Robustness of t Procedures Practical guidelines for inference on a single mean: Sample size < 15: Use t procedures if the are data close to normal; otherwise, don't. Sample size 15: Use t procedures except in the presence of outliers or strong skewness. Large sample size ( 40): Use t procedures even for clearly skewed distributions. Use normal quantile plot, histogram, stemplot, and/or boxplot to investigate these properties of data. Assumption that data are SRS always important. Using SAS One sample t Proc ttest--gives confidence interval and significance test For matched pairs Still use similar code as one sample t Or use the paired command Section 7.2 Comparing Two Means Topics The two-sample z statistics The two-sample t procedures: significance test confidence interval Robustness of the two-sample procedures Small samples Two-Sample Problems Two-sample problems typically arise from a randomized comparative experiment with two treatment groups. (Experimental study) Comparing random samples separately selected from two populations is also a two-sample problem. (Observational study) Unlike matched pairs design, there is no matching of the units in the two sample, and the two samples may be of different sizes. Notation for Two-Sample Settings Population Population mean 1 2 Population standard deviation 1 2 1 2 Notation for Two-Sample Settings Suppose an SRS of size n1 is selected from the 1st population, and another SRS of size n2 is selected from the 2nd population. Population Sample size 1 n1 2 n2 Sample mean x1 x2 Sample standard deviation s1 s2 The Two-Sample z Statistic A natural estimator of the difference 1 2 is the difference between the sample means: x1 - x2 From the rules of adding means and variances: (population) mean of differences: 1 - 2 2 12 2 (population) variance of differences of sample standard deviations: + n1 n2 This expresses the mean and variance of the distribution of differences (of sample means) in terms of the parameters of the two original populations. If the two population distributions are both normal, then the distribution of the difference D is also normal. Distribution of Two-Sample z Statistic Suppose that x1 is the mean of an SRS of size n1 drawn from an N(1, 1) population and that x2 is the mean of an independent SRS of size n2 drawn from an N(2, 2) population. Then the two-sample z statistic is ( x - x ) - ( - ) z= 1 2 1 2 + n1 n2 2 1 2 2 and it has the standard normal distribution, N(0, 1), as Two Populations, Known Population Standard Deviations If 1 and 2 are unknown, then a level C confidence interval for 1 2 is x1 - x2 z * + n1 n2 2 1 2 2 where P(-z* Z z*) = C. Two Populations, Known Population Standard Deviations We want to test H0: 1 = 2 against one of the following alternative hypotheses: Ha: 1 > 2 Ha: 1 < 2 Ha: 1 2 The z test statistic when known population SDs: ( x1 - x2 ) - ( 1 - 2 ) z= 2 12 2 + n1 n2 Inference Two Populations, Known Population Standard Deviations Alternative Hypothesis Ha: 1 > 2 Ha: 1 < 2 Ha: 1 2 P-value P(Z > z) P(Z < z) 2*P(Z > | z |) Two Populations, Unknown Population Standard Deviations Suppose 1, 2, 1, and 2 are unknown Two-sample t statistic for difference in means: t= ( x1 - x2 ) - ( 1 - 2 ) 2 s12 s2 + n1 n2 is approximately t distributed with the df either approximated by software or taken as: min(n1 1, n2 1). Two Populations, Unknown Population Standard Deviations If 1, 2, 1, and 2 are unknown, then a level C confidence interval for 1 2 is ( x1 - x2 ) t * P (tdf * df s s + n1 n2 * tdf ) = C 2 1 2 2 where t (from Table D). So, this is t. Degrees of freedom as before: min(n 1, n 1). Example (metabolism rates for men and women): Obs Gender Mass Rate Obs 1 2 3 4 5 6 7 8 9 10 11 12 M M F F F F M F F M F F 62 62.9 36.1 54.6 48.5 42 47.4 50.6 42 48.7 40.3 33.1 1792 1666 995 1425 1396 1418 1362 1502 1256 1614 1189 913 1 2 3 4 5 6 7 8 9 10 11 F F F F F F F F F F F 36.1 54.6 48.5 42 50.6 42 40.3 33.1 42.4 34.5 51.1 995 1425 1396 1418 1502 1256 1189 913 1124 1052 1347 Gender Mass Rate Example (cont) A back-to-back stem plot or boxplot is always a good idea: 1 8 0 0 1 6 0 0 R a t 1 4 0 0 e 1 2 0 0 1 0 0 0 F M G e n d e r Example (Metabolism rate: women vs. men) Find a 95% CI for the difference in mean metabolism rates between men and women. n1 = 12 n2 = 7 x1 = 1235.1 x2 = 1600 s1 = 188.3 s 2 = 189.2 Two Populations, Unknown Population Standard Deviations Goal: test H0: 1 = 2 against one of the following alternative hypotheses when 1, 2 are unknown: Ha: 1 > 2 Ha: 1 < 2 x1 - x2 - 1 - 2 Ha: 1 2 t= 2 2 The t test statistic is ( ) ( ) s1 s2 + n1 n2 Degrees of freedom as before: min(n1 1, n2 1) or from software. What does the "1 2" in the formula equal? Two Populations, Unknown Population Standard Deviations 1, 2 Alternative Hypothesis Ha: 1 > 2 Ha: 1 < 2 Ha: 1 2 P-value P(T t) P(T t) 2*P(T | t |) Example: Do women have a different metabolism rate than men? Robustness of Two-Sample t Test Even more robust than one-sample t methods robust against non-normal population distributions, in particular if the population distributions are symmetric and the two sample sizes are equal. Outliers are always a problem may need to be eliminated. Skewness is less important for not-toosmall sample sizes. t procedures are rather conservative, so your calculated P-values may be even larger than the "real" P-values. This is good (safe). df for two-sample t procedures df as before: min(n1 1, n2 1) or from software The choice of min(n1 1, n2 1) is conservative. Software will usually give smaller P-values. More correct. In our example with metabolism rates, software will give df = 12.6 (12 in Table D) Here no difference in final conclusion... The pooled two-sample t-test If the variances of the two groups could be assumed to be equal, then a pooled twosample t test can be used (pages 461465). Using SAS Two sample t Still use similar code as one sample t Add class command to designate two groups or populations
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Purdue University - Main Campus - STAT - 501
Section 8.1Inference for a Single ProportionRecall: Population ProportionLet 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 po
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