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05 results in a 95 confidence limit. 05 Range between 0 and 1 Interaction To compute confidence limits specify the statistic-keyword CLM, LCLM, or UCLM. See also Confidence Limits Featured in Computing a Confidence Limit for the Mean CHARTYPE. The length of the variable equals the number of class variables. specifies that the _TYPE_ variable in the output data set is a character representation of the binary value of _TYPE_.
Interaction When you specify more than 32 class variables, _TYPE_ automatically becomes a character variable. Main discussion Output Data Set Featured in Computing Output Statistics with Missing Class Variable Values CLASSDATA SAS-data-set. specifies a data set that contains the combinations of values of the class variables that must be present in the output.
Any combinations of values of the class variables that occur in the CLASSDATA data set but not in the input data set appear in the output and have a frequency of zero. Restriction The CLASSDATA data set must contain all class variables. Interaction If you use the EXCLUSIVE option, then PROC MEANS excludes any observation in the input data set whose combination of class variables is not in the CLASSDATA data set.
Their data type and format must match the corresponding class variables in the input data set. Tip Use the CLASSDATA data set to filter or to supplement the input data set. Featured in Using a CLASSDATA Data Set with Class Variables COMPLETETYPES. creates all possible combinations of class variables even if the combination does not occur in the input data set. Interaction The PRELOADFMT option in the CLASS statement ensures that PROC MEANS writes all user-defined format ranges or values for the combinations of class variables to the output, even when a frequency is zero.
Tip Using COMPLETETYPES does not increase the memory requirements. Main discussion Input Data Sets DESCENDTYPES. identifies the input SAS data set. orders observations in the output data set by descending _TYPE_ value. Featured in Using Preloaded Formats with Class Variables DATA SAS-data-set. Alias DESCENDING DESCEND Interaction Descending has no effect if you specify NWAY. Tip Use DESCENDTYPES to make the overall total _TYPE_ 0 the last observation in each BY group.
See also Output Data Set Featured in Computing Different Output Statistics for Several Variables EXCLNPWGT. excludes observations with nonpositive weight values zero or negative from the analysis. By default, PROC MEANS treats observations with negative weights like observations with zero weights and counts them in the total number of observations. Alias EXCLNPWGTS See also WEIGHT and WEIGHT Statement EXCLUSIVE.
excludes from the analysis all combinations of the class variables that are not found in the CLASSDATA data set. Requirement If a CLASSDATA data set is not specified, then this option is ignored. Featured in Using a CLASSDATA Data Set with Class Variables FW field-width. specifies the field width to display the statistics in printed or displayed output.
Default 12 Tip If PROC MEANS truncates column labels iq option boleto rapido the output, then increase the field width. FW has no effect on statistics that are saved in an output data set. specifies that the output data set contain the minimum value of the ID variables. Interaction Specify PRINTIDVARS to display the value of the ID variables in the output. See also ID Statement MAXDEC number.
specifies the maximum number of decimal places to display the statistics in the printed or displayed output. Default BEST. width for columnar format, typically about 7. Range 0-8 Featured in. MAXDEC has no effect on statistics that are saved in an output data set. considers missing values as valid values to create the combinations of class variables. Special missing values that represent numeric values the letters A through Z and the underscore _ character are each considered as a separate value.
Default If you omit MISSING, then PROC MEANS excludes the observations with a missing class variable value from the analysis. suppresses the column that displays the total number of observations for each unique combination of the values of the class variables. See also SAS Language Reference Concepts for a discussion of missing values that have special meaning. Featured in Using Preloaded Formats with Class Variables NONOBS.
See also The N Obs Statistic Featured in. disables floating point exception FPE recovery during data processing. By default, PROC MEANS traps these errors and sets the statistic to missing. In operating environments where the overhead of FPE recovery is significant, NOTRAP can improve performance. Note that normal SAS FPE handling is still in effect so that PROC MEANS terminates in the case of math exceptions.
specifies that the output data set contain only statistics for the observations with the highest _TYPE_ and _WAY_ values. When you specify class variables, NWAY corresponds to the combination of all class variables. Interaction If you specify a TYPES statement or a WAYS statement, then PROC MEANS ignores this option. See also Output Data Set Featured in Computing Output Statistics with Missing Class Variable Values ORDER DATA FORMATTED FREQ UNFORMATTED.
specifies the sort order to create the unique combinations for the values of the class variables in the output, where. orders values according to their order in the input data set. Interaction If you use PRELOADFMT in the CLASS statement, then the order for the values of each class variable matches the order that PROC FORMAT uses to store the values of the associated user-defined format. If you use the CLASSDATA option, then PROC MEANS uses the order of the unique values of each class variable in the CLASSDATA data set to order the output levels.
If you use both options, then PROC MEANS first uses the user-defined formats to order the output. This column corresponds to the _FREQ_ variable iq option boleto rapido the output data set. If you omit EXCLUSIVE, then PROC MEANS appends after the user-defined format and the CLASSDATA values the unique values of the class variables in the input data set based on the order in which they are encountered. Tip By default, PROC FORMAT stores a format definition in sorted order.
orders values by their ascending formatted values. This order depends on your operating environment. Use the NOTSORTED option to store the values or ranges of a user defined format in the order that you define them. Alias FMT EXTERNAL FREQ. orders values by descending frequency count so that levels with the most observations are listed first.
Interaction For multiway combinations of the class variables, PROC MEANS determines the order of a class variable combination from the individual class variable frequencies. Interaction Use the ASCENDING option in the CLASS statement to order values by ascending frequency count. orders values by their unformatted values, which yields the same order as PROC SORT.
Alias UNFMT INTERNAL Default UNFORMATTED See also Ordering the Class Values PCTLDEF. PCTLDEF is an alias for QNTLDEF. See also QNTLDEF PRINT NOPRINT. specifies whether PROC MEANS displays the statistical analysis. NOPRINT suppresses all the output. Default PRINT Tip Use NOPRINT when you want to create only an OUT output data set.
Featured in For an example of NOPRINT, see. displays all requested combinations of class variables all _TYPE_ values in the printed or displayed output. Normally, PROC MEANS shows only the NWAY type. Alias PRINTALL Interaction If you use the NWAY option, the TYPES statement, or the WAYS statement, then PROC MEANS ignores this option. Featured in Using a CLASSDATA Data Set with Class Variables PRINTIDVARS. displays the values of the ID variables in printed or displayed output.
Alias PRINTIDS Interaction Specify IDMIN to display the minimum value of the ID variables. See also ID Statement QMARKERS number. The number of markers controls the size of fixed memory space. Default The default value depends on which quantiles you request. specifies the default number of markers to use for the PВІ quantile estimation method. For the median P50number is 7. For the quantiles P25 and P50number is 25. For the quantiles P1, P5, P10, P75 P90, P95, or P99, number is 105. If you request several quantiles, then PROC MEANS uses the largest value of number.
Range an odd integer greater than 3 Tip Increase the number of markers above the defaults settings to improve the accuracy of the estimate; reduce the number of markers to conserve memory and computing time. Main Discussion Quantiles QMETHOD OS P2 HIST. specifies the method that PROC MEANS uses to process the input data when it computes quantiles. If the number of observations is less than or equal to the QMARKERS value and QNTLDEF 5, then both methods produce the same results.
uses order statistics. This method is the same method that PROC UNIVARIATE uses. Note В В В This technique can be very memory-intensive. uses the PВІ method to approximate the quantile. Default OS Restriction When QMETHOD P2, PROC MEANS will not compute the following. Tip When QMETHOD P2, reliable estimations of some quantiles P1,P5,P95,P99 might not be possible for some data sets. Main Discussion Quantiles QNTLDEF 1 2 3 4 5.
specifies the mathematical definition that PROC MEANS uses to calculate quantiles when QMETHOD OS. To use QMETHOD P2, you must use QNTLDEF 5. Alias PCTLDEF Default 5 Main discussion Quantile and Related Statistics statistic-keyword s. specifies which statistics to compute and the order to display them in the output. Descriptive statistic keywords CLM NMISS CSS RANGE CV SKEWNESS SKEW KURTOSIS KURT STDDEV STD LCLM STDERR MAX SUM MEAN SUMWGT MIN UCLM MODE USS N VAR Quantile statistic keywords MEDIAN P50 Q3 P75 P1 P90 P5 P95 P10 P99 Q1 P25 QRANGE Hypothesis testing keywords PROBT PRT T.
Default N, MEAN, STD, MIN, and MAX Requirement To compute standard error, confidence limits for the mean, and the Student s t -test, you must use the default value of the VARDEF option, which is DF. Tip Use CLM or both LCLM and UCLM to compute a two-sided confidence limit for the mean. To compute skewness or kurtosis, you must use VARDEF N or VARDEF DF. Main discussion The definitions of the keywords and the formulas for the associated statistics are listed in Keywords and Formulas.
Use only LCLM or UCLM, to compute a one-sided confidence limit. The available keywords in the PROC statement are. SUMSIZE value. value might be one of the following. specifies the amount of memory that is available for data summarization when you use class variables. specifies the amount of memory available in bytes, kilobytes, megabytes, or gigabytes, respectively.
If n is 0, then PROC MEANS use the value of the SAS system option SUMSIZE. specifies the maximum amount of memory that is available. Default The value of the SUMSIZE system option. Tip For best results, do not make SUMSIZE larger than the amount of physical memory that is available for the PROC step. Main discussion Computational Resources. If additional space is needed, then PROC MEANS uses utility files. Note В В В Specifying SUMSIZE 0 enables proc MEANS to use the preferred global REALMEMSIZE option.
THREADS NOTHREADS. See also The SAS system option SUMSIZE in SAS Language Reference Dictionary. enables or disables parallel processing of the input data set. This option overrides the SAS system option THREADS NOTHREADS unless the system option is restricted see Restriction. See SAS Language Reference Concepts for more information about parallel processing.
Default value of SAS system option THREADS NOTHREADS. Restriction Your site administrator can create a restricted options table. A restricted options table specifies SAS system option values that are established at startup and cannot be overridden. If the THREADS NOTHREADS system option is listed in the restricted options table, any attempt to set these system options is ignored and a warning message is written to the SAS log.
You can use THREADS in the PROC MEANS statement to force PROC MEANS to use parallel processing in these situations. In this case, PROC MEANS stops processing and writes a message to the SAS log. Interaction PROC MEANS honors the SAS system option THREADS except when a BY statement is specified or the value of the SAS system option CPUCOUNT is less than 2. Note В В В If THREADS is specified either as a SAS system option or on the PROC MEANS statement and another program has the input data set open for reading, writing, or updating, then PROC MEANS might fail to open the input data set.
specifies the divisor to use in the calculation of the variance and standard deviation. The following table shows the possible values for divisor and associated divisors. The procedure computes the variance aswhere is the corrected sums of squares and equals. Possible Values for VARDEF Value Divisor Formula for Divisor DF degrees of freedom n - 1 N number of observations n WDF sum of weights minus one i w i - 1 WEIGHT WGT sum of weights i w i.
When you weight the analysis variables, equalswhere is the weighted mean. Enjoy our quizzes listed below. Lots of Quizzes - Hundreds of Quizzes the further you go down this page. All our quizzes are well researched. We try to add more quizzes quite regularly for your use and inspiration. All the questions and answers on this quiz website are free to use, print out, enjoy for your own quizzing fun, or your pub quiz - we simply request that you do not re-use them on other websites or internet mediums.
Some quizzes, such as in our football section, have multiple pages of questions and answers. And don t forget to still visit our Main Rounds page for your general knowledge, sport, science and geography quizzes. Descriptive Statistics Using. use this series of class-tested tutorials to learn about SAS. These SAS statistics tutorials briefly explain the use and interpretation of standard statistical analysis techniques for Medical, Pharmaceutical, Clinical Trials, Marketing or Scientific Research.
com SASDATA for files mentioned in this tutorial TexaSoft, 2007-13. The examples include how-to instructions for SAS Software. Preliminary information about PROC MEANS. PROC MEANS produces descriptive statistics means, standard deviation, minimum. maximum, etc. for numeric variables in a set of data. PROC MEANS can be used for.
Describing continuous data where the average has meaning Describing the means across groups Searching for possible outliers or incorrectly coded values Performing a single sample t-test. The syntax of the PROC MEANS statement is. Statistical options that may be requested are default statistics are underlined. N - Number of observations NMISS - Number of missing observations MEAN - Arithmetic average STD - Standard Deviation MIN - Minimum smallest MAX - Maximum largest RANGE - Range SUM - Sum of observations VAR - Variance USS Uncorr.
WINKS SDA Statistical Iq option boleto rapido Affordable. sum of squares CSS - Corr. sum of squares STDERR - Standard Error T - Student s t value for testing Ho md 0 PRT - P-value associated with t-test above SUMWGT - Sum of the WEIGHT variable values. New to version 8. Other commonly used options available in PROC MEANS include. MEDIAN 50th percentile P1 1st percentile P5 - 5th percentile P10 10th percentile P90 - 90th percentile P95 95th percentile P99 - 99th percentile Q1 - 1st quartile Q3 - 3rd quartile QRANGE Quartile range.
A few quick examples of PROC MEANS. Simplest invocation on all numeric variables. Specified statistics and variables. PROC MEANS N MEAN STD; VAR SODIUM CARBO. Subgroup descriptive statistics using by statement. PROC SORT; BY SEX. PROC MEANS; BY SEX. VAR FAT PROTEIN SODIUM. PROC MEANS; CLASS SEX. Example 1 A simple use of PROC MEANS. This example calculates the means of several specified variables, limiting the output to two decimal places.
Output for Example 1. Example 1a - PROC MEANS, simplest use. WEIGHT HEIGHT AGE. Example 1b - PROC MEANS, limit decimals, specify variables. Example 1c PROC MEANS, specify statistics to report. PROCMEANS1. BY variable list -- Statistics are reported for groups in separate tables CLASS variable list Statistics reported by groups in a single table VAR variable list specifies which numeric variables to use OUTPUT OUT datasetname statistics will be output to a SAS data file FREQ variable - specifies a variable that represents a count of observations.
Commonly used statements with PROC MEANS include. DATA Specify data set to use NOPRINT Do not print output MAXDEC n Use n decimal places to print output. Subgroup descriptive statistics using class statement. Example 2 Using PROC MEANS using By Group and Class statements. This example uses PROC MEANS to calculate means for an entire data set or by grouping variables.
PROCMEANS2. Output for this SAS code is. Analysis Variable WEIGHTGAIN. In this first version of the output the BY statement along with the PROC SORT creates two tables, one for each value of the BY variable. Summary Statistics by Group. In this next example, the CLASS statement produces a single table broken down by group FEEDTYPE. Summary statistics USING CLASS. Hands on Exercise. Modify the above program to output the following statistics. EXAMPLE 3 Using PROC MEANS to find OUTLIERS.
PROC MEANS is a quick way to find large or small values in your data set that may be considered outliers see PROC UNIVARIATE also. Use MAXDEC 2 to limit number of decimals in output. This example shows the results ofusing PROC means where the MINIMUM and MAXIMUM identify unusual values inthe data iq option boleto rapido. Notice that in this output, PROC means indicates that there is a small value of -99 could be a missing value code and a large value of 78 could be a miscoded number.
This is a quick way to find outliers in your data set. Analysis Variable LOSS. To compare two paired groups such as in a before-after situation where both observations are taken from the same or matched subjects, you can perform a paired t-test using PROC MEANS. To do this convert the paired data into a difference variable and perform a single sample t-test. EXAMPLE 4 Using PROC MEANS to perform a single sample t-test or Paired t-test. Also see PROC Univariate for detecting outliers.
To perform a paired t-test using PROC MEANS, follow these steps. Read in your data. The hypotheses for this test are. Calculate the difference between the two observations WLOSS is the amount of weight lostand Report the mean loss, t-statistic and p-value using PROC MEANS. Ho μLoss 0 The average weight loss was 0. Ha μLoss 0 The weight loss was different than 0. For iq option boleto rapido, the following code performs a paired t-test for weight loss data PROCMEANS4.
Notice that the actual test is performed on the new variable called WLOSS, and that is why it is the only variable requested in the PROC MEANS statement. This is essentially a one-sample t-test. The statistics of interest are the mean of WLOSS, the t-statistic associated with the null hypothesis for WLOSS and the p-value. The SAS output is as follows. Paired t-test example using PROC MEANS. Analysis Variable WLOSS.
The mean of the variable WLOSS is 22. The t-statistic associated with the null hypothesis is 2. 79, and the p-value for this paired t-test is p 0. 027, which provides evidence to reject the null hypothesis. End of this tutorialpart 1, Click to continue. For SAS Users SAS Essentials A Guide to Mastering SAS 2nd Edition - provides an introduction to SAS statistical software, the premiere statistical data analysis tool for scientific research.
Through its straightforward approach, the text presents SAS with step-by-step examples. SAS Essentials introduces a step-by-step approach to mastering SAS software for statistical data analysis. Designed for those new to SAS and filled with illustrative examples, the book shows how to read, write and import data; prepare data for analysis; use SAS procedures; evaluate quantitative data; analyze counts and crosstabulation tables; and compare means using the t-test. It s also a valuable reference tool for any researcher currently using SAS.
The book also provides instruction and examples on analysis of variance, correlation and regression, nonparametric analysis, logistic regression, creating graphs, controlling outputs using ODS, as well as advanced topics in SAS programming. From John Wiley Sons. ISBN 111904216X. Order From Amazon. For example, suppose your data contained the variables WBEFORE and WAFTER, before and after weight on a dietfor 8 subjects.
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