proc import out=work.test
datafile='C:\sastest\excelfile.xlsx'
dbms=xlsx replace;
sheet='Sheet1';
run;
proc export data=sashelp.class
outfile='C:\sastest\excelfile2.xlsx'
dbms=xlsx replace;
sheet='Sheet2';
run;


proc import out=work.test datafile='C:\sastest\excelfile.xlsx' dbms=xlsx replace; sheet='Sheet1'; run; proc export data=sashelp.class outfile='C:\sastest\excelfile2.xlsx' dbms=xlsx replace; sheet='Sheet2'; run; I have to say I spent about an hour trying to figure out how to use it off a PC. It worked immediately for my Samsung phone, but it refused to produce sound off my PC. After internetsearching for solutions, I made it work. Here is how: a) Go to http://support.logitech.com/ DPLYR is some kind of module that allows efficient data management. library(dplyr)
This keeps rows that has a specified value: filter(dataname,School=="Ad High School") x < filter(dataname,School=="Ad High School")
Algorithm: SD=Standard error * sqrt(N);
Reference: http://handbook.cochrane.org/chapter_7/7_7_3_2_obtaining_standard_deviations_from_standard_errors_and.htm
QC: I checked the algorithm using SAS. The result was consistent with the algorithm (i.e., SD=standard error*sqrt(N)). proc means data=sashelp.class mean std stderr n; ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ SD 5.1270752 Stadard Error 1.1762317 N 19 data YesNo; proc print noobs; proc freq order=data; The goal of this exercise is to find out what happens if I intentionally use a level2 variable at level 1 in HLM. I found that the coefficients and standard errors remain about the same. The parameter that differed was just the degree of freedom, which was consistent with my expectation. Using my old NELS dataset, I ran two different HLM models using Bryk and Raudenbush’s software (See model 1 and model 2 equations in the table below).
The outcome variable is the achievement composite (POSTTEST), students are level 1 and schools are level 2. When expressed as mixed models, the two models are identical, which is why I expected most parameters to come out the same. POSTTEST_{ij} = γ_{00} The first model (MODEL 1; see below) included URBAN (students are in urban school) as a level 1 predictor. Of course this is a wrong specification because urban is a school characteristic. In the second model (MODEL 2), I used it at the expected level, which is at level 2 (school level). These models look different, but AGAIN when expressed as mixed models, they are identical. As the third model (MODEL 3), I replicated the same HLM model using SAS PROC GLIMMIX. SAS requires that the equation be expressed as a mixed model. Results showed that coefficients and standard errors are more or less the same across three models. The only one thing that was different was degree of freedom. Conclusion: As long as variables enter the model as fixed effects as done here, there is nothing magical about the HLM model. HLM software or SAS PROC GLIMMIX (option ddfm=kr) adjust degree of freedom values, accounting for the fact that URBAN is a schoollevel variable and thus should not be awarded a value that is too large. Notice that under the correct specification (MODEL 2 and MODEL 3), the degree of freedom for URBAN is close to the number of schools, not to the number of students. Thanks for any comments you may have.
Datasets: www.nippondream.com/file/datafiles_HLM.zip
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<html> rsort($array); ?>
http://www.w3schools.com/php/php_echo_print.asp <!DOCTYPE html> ?>
FUNCTIONS $length=strlen("kazuaki");
<html> ?> <?php
<html> <p> <p> <?php
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<html> Programming –Python 3.5 https://www.python.org/downloads/
–PHP & MySQL (from WampServer)
Statistical Analysis Program –R https://cran.cnr.berkeley.edu/ –GPower http://www.gpower.hhu.de/en.html –Optimal Design https://sites.google.com/site/optimaldesignsoftware/home
Editing Programs — NotePad++ https://notepadplusplus.org/download/v7.1.html –Sigil https://github.com/SigilEbook/Sigil/releases/tag/0.9.6 (There is a link to the exe file at the bottom of the page.
proc glimmix data=kaz2.Year2AnalysisSample; 

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