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Profiling numerical variables is an integral part of data analytics, which generally consists of obtaining standard descriptive statistics such as quantiles, first central moments as well as missing ratio. There are two methods available to handle this scenario, both uses the one-pass method of Jain R. Of couse, due to the way the output statistics are organized from PROC STDIZE OUTSTAT=, it can be parallelized very easily, and by examining the actual CPU time, PROC STDIZE is more efficient than PROC MEANS.

We also examine the difference on quantile estimates using the Ordered Statistics (OS) method and P2 method in PROC MEANS. Profiling numerical variables is an integral part of data analytics, which generally consists of obtaining standard descriptive statistics such as quantiles, first central moments as well as missing ratio.It is easily obtainable by using PROC MEANS (or PROC SUMMARY). The book "Bayesian Computation with R" by Jim Albert is an easy to read entry level book on applied Bayesian Statistics. PROC FREQ directly corresponds to table() function in R, but provides much richer functionality.

PROC MEANS somehow corresponds to summary() function to obtain summary statistics for quantitative variables. Histogram can be obtained in SAS via either PROC UNIVARIATE or newly introduced PROC SGPLOT. Boxplot to visualize distribution of quantitative variables by some classification variable.

Now we can apply jitter function to the two variables studied in the book and check them out. In order to overlay a fitted regression line, we have multiple ways to do it in SAS, and the Statistical Plot procedures are particularly handy to generate this type of figures. Now, to study the robustness of T-Statistics which relies on several assumptions, such as normality, homoskedasticity, etc. Just like the "source(file)" command in R, in SAS, we have a counterpart called %include(or in abv. The following code pieces conduct the four simulate scenarios illustrated in the book and draw the last figure. Obtain summary statistics over a rolling window for a given data, usually on a time dimension, is not quit easy in SAS, especially the rolling window may contain different number of records and the maximum number is unknown without pass the data once.

This rolling window problem can be efficiently solved by using MultiLabel Format, a feature tend to be ignored. When the data is getting large, there will be some computing difficulty and insufficient resource. The multilabel approach has an overhead that is smaller overall to the other methods and can be easily changed to accommandate other sizes of rolling windows.

They're worth examining since they are chosen almost exclusively based on how attractive they are to the average male consumer, not how elegant or confident they appear. But when we face very large data with many varibles, we will hit memory wall and very long processing time using default options in PROC MEANS.

Or we can use PROC STDIZE, specifying PCTLMTD=ONEPASS to invoke P2 method to calculate quantiles. Besides, higher central moments can be calculated from obtained first 2 central moments and this is done in the small output data set. While the book was written for R users, it is not difficult to translate the languages between R and SAS and I believe it is a good way to show Bayesian capability of SAS.

For graphics, we use axis statement to define how we want the two axes to appear in print out, and use symbol statement to define the appearance of symbols for data. Compare the code and corresponding figures (in the same order of LINEPRINTER, ODS GRAPHICS, Manually Drawn, SGPLOT). For example, given a transaction data over several days, a business analyst wants to summarize the data for each 24 hour period.

Typically people use an array that is large enough to handle a reasonable guess of maximum number of records within the given interval.

To solve this problem, simply sort and divide the original data into smaller pieces with an overlap equals to the size of rolling window. The website of the modeling agency that hosts the more elite stars -- LA Direct Models (NSFW) -- has height data for all but a couple of their members. The most time consuming and memory intensive descriptive statistics is quantile calculation. This method is able to obtain fairly accurate estimate of quantiles between P25 and P75, but for quantiles outside this range, the estimates will be more rough but given very large sample, maybe acceptable.

The most memory efficient is PROC STDIZE with PCTLMTD=ONEPASS, only 0.68MB memory was used.

The LABEL= statement in SAS is a very handy tool for annotation purpose for PRINT or GRAPH.

For manually drawn figures, since we ask for connected dots in order to obtain a line overlay on circles, we have to sort the data by X-axis variable to make the line as straight as possible. When combine the summarized pieces, you should discard the overlap part from the rest pieces. The default method uses ordered statistics, which is the most accurate but also the most memory intensive and time consuming one. Another problem is that this estimator is sensitive to the distribution of underlying data [2]. On the other hand, SGPLOT is very convenient if you want to overlay density curve, either a normal fit or kernel one. The posts here will only spend minimum effects to explain the statistics underlying while most resource will still be denoted to SAS coding. Because the adult film star sample could easily by biased by a half-inch, and because the means are close enough anyway, I won't bother running a t-test.

If you really want to, feel free to post it in the comments, but it's clear that the adult film stars are not taller or shorter than the population at large.Because the females are chosen only based on how physically attractive they are, this result goes against the hypothesis that long legs are in general physically attractive to men (although some men may find them sexy).

Both of these methods are not easy to code and error prone for a beginner.This rolling window problem can be efficiently solved by using MultiLabel Format, a feature tend to be ignored.

There are newer methods available that are insensitive to the distribution, such as [3].In PROC MEANS, we can specify QMETHOD=P2 or QMETHOD=HIST to call this one pass method. You can sepecify MAXDEC= to keep desired number of decimals in print out.Histogram can be obtained in SAS via either PROC UNIVARIATE or newly introduced PROC SGPLOT. Smith College of Engineering and Computer Science - Former Departments, Centers, Institutes and Projects.

We also examine the difference on quantile estimates using the Ordered Statistics (OS) method and P2 method in PROC MEANS. Profiling numerical variables is an integral part of data analytics, which generally consists of obtaining standard descriptive statistics such as quantiles, first central moments as well as missing ratio.It is easily obtainable by using PROC MEANS (or PROC SUMMARY). The book "Bayesian Computation with R" by Jim Albert is an easy to read entry level book on applied Bayesian Statistics. PROC FREQ directly corresponds to table() function in R, but provides much richer functionality.

PROC MEANS somehow corresponds to summary() function to obtain summary statistics for quantitative variables. Histogram can be obtained in SAS via either PROC UNIVARIATE or newly introduced PROC SGPLOT. Boxplot to visualize distribution of quantitative variables by some classification variable.

Now we can apply jitter function to the two variables studied in the book and check them out. In order to overlay a fitted regression line, we have multiple ways to do it in SAS, and the Statistical Plot procedures are particularly handy to generate this type of figures. Now, to study the robustness of T-Statistics which relies on several assumptions, such as normality, homoskedasticity, etc. Just like the "source(file)" command in R, in SAS, we have a counterpart called %include(or in abv. The following code pieces conduct the four simulate scenarios illustrated in the book and draw the last figure. Obtain summary statistics over a rolling window for a given data, usually on a time dimension, is not quit easy in SAS, especially the rolling window may contain different number of records and the maximum number is unknown without pass the data once.

This rolling window problem can be efficiently solved by using MultiLabel Format, a feature tend to be ignored. When the data is getting large, there will be some computing difficulty and insufficient resource. The multilabel approach has an overhead that is smaller overall to the other methods and can be easily changed to accommandate other sizes of rolling windows.

They're worth examining since they are chosen almost exclusively based on how attractive they are to the average male consumer, not how elegant or confident they appear. But when we face very large data with many varibles, we will hit memory wall and very long processing time using default options in PROC MEANS.

Or we can use PROC STDIZE, specifying PCTLMTD=ONEPASS to invoke P2 method to calculate quantiles. Besides, higher central moments can be calculated from obtained first 2 central moments and this is done in the small output data set. While the book was written for R users, it is not difficult to translate the languages between R and SAS and I believe it is a good way to show Bayesian capability of SAS.

For graphics, we use axis statement to define how we want the two axes to appear in print out, and use symbol statement to define the appearance of symbols for data. Compare the code and corresponding figures (in the same order of LINEPRINTER, ODS GRAPHICS, Manually Drawn, SGPLOT). For example, given a transaction data over several days, a business analyst wants to summarize the data for each 24 hour period.

Typically people use an array that is large enough to handle a reasonable guess of maximum number of records within the given interval.

To solve this problem, simply sort and divide the original data into smaller pieces with an overlap equals to the size of rolling window. The website of the modeling agency that hosts the more elite stars -- LA Direct Models (NSFW) -- has height data for all but a couple of their members. The most time consuming and memory intensive descriptive statistics is quantile calculation. This method is able to obtain fairly accurate estimate of quantiles between P25 and P75, but for quantiles outside this range, the estimates will be more rough but given very large sample, maybe acceptable.

The most memory efficient is PROC STDIZE with PCTLMTD=ONEPASS, only 0.68MB memory was used.

The LABEL= statement in SAS is a very handy tool for annotation purpose for PRINT or GRAPH.

For manually drawn figures, since we ask for connected dots in order to obtain a line overlay on circles, we have to sort the data by X-axis variable to make the line as straight as possible. When combine the summarized pieces, you should discard the overlap part from the rest pieces. The default method uses ordered statistics, which is the most accurate but also the most memory intensive and time consuming one. Another problem is that this estimator is sensitive to the distribution of underlying data [2]. On the other hand, SGPLOT is very convenient if you want to overlay density curve, either a normal fit or kernel one. The posts here will only spend minimum effects to explain the statistics underlying while most resource will still be denoted to SAS coding. Because the adult film star sample could easily by biased by a half-inch, and because the means are close enough anyway, I won't bother running a t-test.

If you really want to, feel free to post it in the comments, but it's clear that the adult film stars are not taller or shorter than the population at large.Because the females are chosen only based on how physically attractive they are, this result goes against the hypothesis that long legs are in general physically attractive to men (although some men may find them sexy).

Both of these methods are not easy to code and error prone for a beginner.This rolling window problem can be efficiently solved by using MultiLabel Format, a feature tend to be ignored.

There are newer methods available that are insensitive to the distribution, such as [3].In PROC MEANS, we can specify QMETHOD=P2 or QMETHOD=HIST to call this one pass method. You can sepecify MAXDEC= to keep desired number of decimals in print out.Histogram can be obtained in SAS via either PROC UNIVARIATE or newly introduced PROC SGPLOT. Smith College of Engineering and Computer Science - Former Departments, Centers, Institutes and Projects.

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