1. November 4, 2009 Introduction to SAS LISA Short Course Series Mark Seiss, Dept. of Statistics

2. Reference Material The Little SAS Book – Delwiche and Slaughter SAS Programming I: Essentials SAS Programming II: Manipulating Data with the DATA Step Presentation and Data http://www.lisa.stat.vt.edu/?q=node/167

3. Presentation Outline 1.Introduction to the SAS Environment 2.Working With SAS Data Sets 3.Summary Procedures 4.Basic Statistical Analysis Procedures

4. Presentation Outline Questions/Comments

5. Introduction to the SAS Environment 1.SAS Programs 2.SAS Data Sets and Data Libraries 2.Creating SAS Data Sets

6. SAS Programs File extension -.sas Editor window has four uses: Access and edit existing SAS programs Write new SAS programs Submitting SAS programs for execution Saving SAS programs SAS program – sequence of steps that the user submits for execution Submitting SAS programs Entire program Selection of the program

7. SAS Programs Syntax Rules for SAS statements Free-format – can use upper or lower case Usually begin with an identifying keyword Can span multiple lines Always end with a semicolon Multiple statements can be on the same line Errors Misspelled key words Missing or invalid punctuation (missing semi-colon common) Invalid options Indicated in the Log window

8. SAS Programs 2 Basic steps in SAS programs: Data Steps Typically used to create SAS datasets and manipulate data, Begins with DATA statement Proc Steps Typically used to process SAS data sets Begins with PROC statement The end of the data or proc steps are indicated by: RUN statement – most steps QUIT statement – some steps Beginning of another step (DATA or PROC statement)

9. SAS Programs Output generated from SAS program – 2 Windows SAS log Information about the processing of the SAS program Includes any warnings or error messages Accumulated in the order the data and procedure steps are submitted SAS output Reports generated by the SAS procedures Accumulates output in the order it is generated

10. SAS Data Sets and Data Libraries SAS Data Set Specifically structured file that contains data values. File extension -.sas7bdat Rows and Columns format – similar to Excel Columns – variables in the table corresponding to fields of data Rows – single record or observation Two types of variables Character – contain any value (letters, numbers, symbols, etc.) Numeric – floating point numbers Located in SAS Data Libraries

11. SAS Data Sets and Data Libraries SAS Data Libraries Contain SAS data sets Identified by assigning a library reference name – libref Temporary Work library SAS data files are deleted when session ends Library reference name not necessary Permanent SAS data sets are saved after session ends SASUSER library You can create and access your own libraries

12. SAS Data Sets and Data Libraries SAS Data Libraries cont. Assigning library references Syntax LIBNAME libref ‘SAS-data-library’; Rules for Library References 8 characters or less Must begin with letter or underscore Other characters are letters, numbers, or under scores

13. SAS Data Sets and Data Libraries SAS Data Libraries cont. Identifying SAS data sets within SAS Data Libraries libref.filename Accessing SAS data sets within SAS Data Libraries Example:DATA new_data_set; set libref.filename; run; Creating SAS data sets within SAS Data Libraries Example:DATA libref.filename; set old_data_set; run;

14. Creating SAS Data Sets Creating a SAS data sets from raw data 4 methods 1.Importing existing raw data in SAS program 2.Manually entering raw data in SAS program 3.Importing existing data sets using Import menu option 4.Manually entering raw data using Table Editor

15. Creating SAS Data Sets Importing existing raw data in SAS program 1.Start Data step and name the SAS data set to be created (include SAS Data library to be stored in) DATA libref.SAS-data-set; 2.Identify the file that contains the raw data file (.dat file) INFILE ‘raw-data-filename’; 3.Provide instruction on how to read data from raw data file INPUT input-specifications;

16. Creating SAS Data Sets Input Specifications Specifies the names of the SAS variables in the new data set Specifies whether the SAS variables are character or numeric Identifies the locations of the variables in the raw data file List Input Column Input Formatted Input Mixed Input

17. Creating SAS Data Sets List Input Used when raw data is separated by spaces All data in a row must be read in All missing data must be indicated by period Simple character data – no embedded spaces, no lengths greater than 8 INPUT statement Simply list variables after the INPUT keyword in the order they appear on file. If variables are character format, place a $ after the variable name Example) INPUT Name $ City $ Age Height Weight Sex $;

18. Creating SAS Data Sets Column Input Used when raw data file does not have delimiters between values (large data sets) Each variable’s values are found in the same columns in each row Numeric data must be standard – numbers, decimals, signs, and scientific notation only Advantages No spaces required Missing values left blank Character data can have embedded spaces Ability to skip unwanted variables

19. Creating SAS Data Sets Column Input cont. INPUT Statement Numeric variables – list variable name then list column or range of columns where the variable is found on the raw data file Character variables – list variable name, dollar sign, and then column or range of columns Example) INPUT Name $ 1-10 Age 26-28 Sex $ 35;

20. Creating SAS Data Sets Formatted Input Appropriate for reading: Data in fixed columns Standard and nonstandard character and numeric data Calendar values to be converted to SAS date value Read data in using SAS informats Instruction that SAS uses to read in data values General forms –Character - $informatw. –Numeric – informatw.d –Date – informatw.

21. Creating SAS Data Sets Formatted Input cont. Character Informats $w. – character string with a width of w, trims leading blanks $charw. – character string with a width of w, does not trim leading or trailing blanks Numeric Informats w.d – standard numeric data with width w and d numbers after the decimal –Raw Data Value = 1234567  informat = 8.2  SAS Data Value = 12345.67 COMMAw.d – numeric data with embedded commas –Raw Data Value =1,000,001  informat=COMMA10.  SAS Data Value=1000001

22. Creating SAS Data Sets Formatted Input cont. SAS date values Stored as special numeric number data Number of days between January 1, 1960 and the specified data Informats are used to read and convert the dates Raw Data ValueInformat 11/04/2009MMDDYY10. 11/04/09MMDDYY8. 04NOV2009Date9. 04/11/2009DDMMYY10.

23. Creating SAS Data Sets Formatted Input cont. Columns read are determined by the starting point and width of the informat Example: INPUT Name $10. Age 3. Height 5.1 BirthDate MMDDYY10.; -Name – Character of length 10, columns 1-10 - Age – Numeric with length 3, columns 11-13 - Height – Numeric with length 5 (including decimal) and one decimal place (120.9 for instance), columns 14-18 - Birthdate – Date format MMDDYY (11-04-2009 for instance), columns 19 - 28

24. Creating SAS Data Sets Formatted Input cont. Pointer controls +n moves pointer n positions @n moves pointer to column n Example: INPUT Flight 3. +4 Date mmddyy8. @20 Destination $3.; -Flight - Number of length 3, columns 1 through 3 -Date – Date format mmddyy (11/04/09) of length 8, columns 8 through 15 -Destination – Character of length 3, columns 20 through 22

25. Creating SAS Data Sets Mixed Formatted Input Styles Mix and match the previous 3 input styles Example: Raw Data: Great Smoky Mountains NC/TN 1926 520,269 INPUT ParkName $ 1-22 State $ Year @40 Acreage COMMA9.; - Parkname - Character of length 22, columns 1 through 22 - State -Character, separated by spaces - Year - Numeric, separated by spaces - Acreage - Numeric with informat COMMA9., starts column 40

26. Creating SAS Data Sets Manually Entering Raw Data Files in SAS program 1.Start Data step and name the SAS data set to be created DATA library.SAS-data-set; 2.Provide instructions on how to read data from raw data file INPUT input-specifications; 3.Manually enter raw data DATALINES;

27. Creating SAS Data Sets Manually Entering Raw Data Files in SAS program Example: Data uspresidents; INPUT President $ Party $ Number; DATALINES; AdamsF 2 LincolnR16 GrantR18 KennedyD35 ; Run;

28. Creating SAS Data Sets Using the import data menu option 1.File  Import Data 2.Standard data source  select the file format 3.Specify file location or Browse to select file 4.Create name for the new SAS data set and specify location

29. Creating SAS Data Sets Compatible file formats Microsoft Excel Spreadsheets Microsoft Access Databases Comma Separate Files (.csv) Tab Delimited Files (.txt) dBASE Files (.dbf) JMP data sets SPSS Files Lotus Spreadsheets Stata Files Paradox Files

30. Creating SAS Data Sets Enter raw data directly into a SAS data set 1.Tools  Table Editor 2.Enter data manually into table - Observations in each row - Variables in each column 3.Left Click Column  Column Attributes - Variable Name, Variable Label, Type – Character/Numeric, Format, Informat Note: Informats determine how raw data is read. Formats determine how variable is displayed. 4.Close window  Save Changes – Yes  Specify File name and directory

31. Introduction to the SAS Environment Questions/Comments

32. Working With SAS Data Sets 1.Data Set Manipulation 2.Data Set Processing 3.Combining Data Sets A.Concatenating/Appending B.Merging

33. Data Set Manipulation Create a new SAS data set using an existing SAS data set as input Specify name of the new SAS data set after the DATA statement Use SET statement to identify SAS data set being read Syntax: DATA output_data_set; SET input_data_set; ; RUN; By default the SET statement reads all observations and variables from the input data set into the output data set.

34. Data Set Manipulation Assignment Statements Evaluate an expression Assign resulting value to a variable General Form:variable = expression; Example:miles_per_hour = distance/time; SAS Functions Perform arithmetic functions, compute simple statistics, manipulate dates, etc. General Form:variable=function_name(argument1, argument2,…); Example: Time_worked = sum(Day1,Day2, Day3, Day4, Day5);

35. Data Set Manipulation Selecting Variables Use DROP and KEEP to determine which variables are written to new SAS data set. 2 Ways DROP and KEEP as statements –Form:DROP = Variable1 Variable2; KEEP = Variable3 Variable4 Variable5; DROP and KEEP options in SET statement –Form:SET input_data_set (KEEP=Var1);

36. Data Set Manipulation Conditional Processing Uses IF-THEN-ELSE logic General Form:IF THEN ; ELSE IF THEN ; ELSE ; is a true/false statement, such as: Day1=Day2, Day1 > Day2, Day1 < Day2 Day1+Day2=10 Sum(day1,day2)=10 Day1=5 and Day2=5

37. Data Set Manipulation Conditional Processing SymbolicMnemonicExample =EQIF region=‘Spain’; ~= or ^=NEIF region ne ‘Spain’; >GTIF rainfall > 20; <LTIF rainfall lt 20; >=GEIF rainfall ge 20; <=LEIF rainfall <= 20; &ANDIF rainfall ge 20 & temp < 90; | or !ORIF rainfall ge 20 OR temp < 90; IS NOT MISSING IF region IS NOT MISSING; BETWEEN ANDIF region BETWEEN ‘Plain’ AND ‘Spain’; CONTAINSIF region CONTAINS ‘ain’; INIF region IN (‘Rain’, ‘Spain’, ‘Plain’);

38. Data Set Manipulation Conditional Processing cont. If is true, is processed ELSE IF and ELSE are only processed if is false Only one statement specified using this form Use DO and END statements to execute group of statements General Form:IF THEN DO; ; END; ELSE DO; ; END;

39. Data Set Manipulation Subsetting Rows (Observations) We will look at two ways Using IF statement Using WHERE option in SET statement IF statement Only writes observations to the new data set in which an expression is true; General Form: IF ; Example: IF career = ‘Teacher’; IF sex ne ‘M’; In the second example, only observations where sex is not equal to ‘M’ will be written to the output data set

40. Data Set Manipulation Subsetting Rows (Observations) cont. Where Option in SET statement Use option to only read rows from the input data set in which the expression is true General Form:SET input_data_set (where=( )); Example:SET vacation (where=(destination=‘Bermuda’)); Only observations where the destination equals ‘Bermuda’ will be read from the input data set Comparison Resulting output data set is equivalent IF statement – all rows read from the input data set Where option – only rows where expression is true are read from input data set Difference in processing time when working with big data sets

41. Data Set Manipulation PROC SORT sorts data according to specified variables General Form:PROC SORT DATA=input_data_set ; BY Variable1 Variable2; RUN; Sorts data according to Variable1 and then Variable2; By default, SAS sorts data in ascending order Number low to high A to Z Use DESCENDING statement for numbers high to low and letters Z to A BY City DESCENDING Population; SAS sorts data first by city A to Z and then Population high to low

42. Data Set Manipulation Some Options NODUPKEY Eliminates observations that have the same values for the BY variables OUT=output_data_set By default, PROC SORT replaces the input data set with the sorted data set Using this option, PROC SORT creates a newly sorted data set and the input data set remains unchanged

43. Data Set Processing DATA steps read in data from existing data sets or raw data files one row at a time, like a loop DATA step reads data from the input data set in the following way: 1. Read in current row from input data set to Program Data Vector (PDV) 2.Process SAS statements 3.PDV to output data set 4.Set current row to the next row in the input data set 5.Iterate to Step 1 One row at a time is processed Thus we cannot simply add the value of a variable in one row to the value in another row

44. Data Set Processing Data Set Processing – Example Let the following be the input data set dfwlax: FlightDateDestFirstClassEconomy 43914955LAX20137 92114955DFW15131 11414956LAX1585 98214956DFW5196 43914957LAX14116 98214957DFW20166

45. Data Set Processing Data Set Processing – Example Consider the following submitted code: DATA onboard; SET dfwlax; Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; RUN;

46. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; Current  SET dfwlax; Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX20137.. FlightDateDestFirstClassEconomyTotalFirstClassFull

47. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; SET dfwlax; Current  Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX20137157. FlightDateDestFirstClassEconomyTotalFirstClassFull

48. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; SET dfwlax; Total=FirstClass+Economy; Current  IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571 FlightDateDestFirstClassEconomyTotalFirstClassFull

49. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; SET dfwlax; Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; Current  RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571 FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571

50. Data Set Processing Data Set Processing – Example Execution of the Data Step Current  DATA onboard; SET dfwlax; Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX20137.. FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571

51. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; Current  SET dfwlax; Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 92114955DFW15131.. FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571

52. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; SET dfwlax; Current  Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 92114955DFW15131146. FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571

53. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; SET dfwlax; Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; Current  ELSE FirstClassFull=0; RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 92114955DFW151311460 FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571

54. Data Set Processing Data Set Processing – Example Execution of the Data Step DATA onboard; SET dfwlax; Total=FirstClass+Economy; IF FirstClass=20 then FirstClassFull=1; ELSE FirstClassFull=0; Current  RUN; PDV Onboard FlightDateDestFirstClassEconomyTotalFirstClassFull 92114955DFW151311460 FlightDateDestFirstClassEconomyTotalFirstClassFull 43914955LAX201371571 92114955DFW151311460

55. Combining Data Sets Concatenating (or Appending) Stacks each data set upon the other If one data set does not have a variable that the other datasets do, the variable in the new data set is set to missing for the observations from that data set. General Form:DATA output_data_set; SET data1 data2; run; PROC APPEND may also be used

56. Combining Data Sets Merging Data Sets One-to-One Match Merge A single record in a data set corresponds to a single record in all other data sets Example: Patient and Billing Information One-to-Many Match Merge Matching one observation from one data set to multiple observations in other data sets Example: County and State Information Note:Data must be sorted before merging can be done (PROC SORT)

57. Combining Data Sets One-to-One Match Merge Usually need at least one common variable between data sets – matching purposes For the example, a patient ID would be needed Do not need common variable if all data sets are in exactly the same order General Form:DATA output_data_set; MERGE input_data_set1 input_data_set2; By variable1 variable2; RUN;

58. Combining Data Sets One-to-One Match Merge Example: PerformanceGoals Code: DATA compare; MERGE performance goals; BY month; difference=sales-goal; RUN; MonthSales 18223 26034 34220 MonthGoal 19000 26000 35000

59. Combining Data Sets One-to-One Match Merge Example cont.: Compare MonthSalesGoalDifference 182239000-777 26034600034 342205000-780

60. Combining Data Sets One-to-Many Match Merge Requires at least one common variable in the data sets for matching purposes For the example, State information is in both the state and county files If two data sets have variables with the same name, the variables in the second data set will overwrite the variable in the first. General Form:DATA output_data_set; MERGE Data1 Data2 Data3; BY Variable1 Variable2; RUN:

61. Combining Data Sets One-to-Many Match Merge Example: VideosAdjustment Code: DATA prices; MERGE videos adjustment BY category; NewPrice=(1-adjustment)*sales; RUN; CategorySales Aerobics12.99 Aerobics13.99 Aerobics13.99 Step12.99 Step12.99 Weights15.99 CategoryAdjustment Aerobics.20 Step.30 Weights.25

62. Combining Data Sets One-to-One Many Merge Example cont.: Videos CategorySalesAdjustmentNewPrice Aerobics12.99.2010.39 Aerobics13.99.2011.19 Aerobics13.99.2011.19 Step12.99.309.09 Step12.99.309.09 Weights15.99.2511.99

63. Working With SAS Data Sets Questions/Comments

64. Summary Procedures 1.Print Procedure 2.Plot Procedure 3.Univariate Procedure 4.Means Procedure 5.Freq Procedure

65. Print Procedure PROC PRINT is used to print data to the output window By default, prints all observations and variables in the SAS data set General Form:PROC PRINT DATA=input_data_set ; RUN; Some Options input_data_set (obs=n) -Specifies the number of observations to be printed in the output NOOBS - Suppresses printing observation number LABEL - Prints the labels instead of variable names

66. Print Procedure Optional SAS statements BY variable1 variable2 variable3; Starts a new section of output for every new value of the BY variables ID variable1 variable2 variable3; Prints ID variables on the left hand side of the page and suppresses the printing of the observation numbers SUM variable1 variable2 variable3; Prints sum of listed variables at the bottom of the output VAR variable1 variable2 variable3; Prints only listed variables in the output

67. Plot Procedure Used to create basic scatter plots of the data Use PROC GPLOT or PROC SGPLOT for more sophisticated plots General Form: PROC PLOT DATA=input_data_set; PLOT vertical_variable * horizontal_variable/ ; RUN; By default, SAS uses letters to mark points on plots A for a single observation, B for two observations at the same point, etc. To specify a different character to represent a point PLOT vertical_variable * horizontal variable = ‘*’;

68. Plot Procedure To specify a third variable to use to mark points PLOT vertical_variable * horizontal_variable = third_variable; To plot more than one variable on the vertical axis PLOT vertical_variable1 * horizontal_variable=‘2’ vertical_variable2 * horizontal_variable=‘1’/OVERLAY ;

69. Univariate Procedure PROC UNIVARIATE is used to examine the distribution of data Produces summary statistics for a single variable Includes mean, median, mode, standard deviation, skewness, kurtosis, quantiles, etc. General Form: PROC UNIVARIATE DATA=input_data_set ; VAR variable1 variable2 variable3; RUN ; If the variable statement is not used, summary statistics will be produced for all numeric variables in the input data set.

70. Univariate Procedure Options include: PLOT – produces Stem-and-leaf plot, Box plot, and Normal probability plot; NORMAL – produces tests of Normality

71. Means Procedure Similar to the Univariate procedure General Form:PROC MEANS DATA=input_data_set options; ; RUN; With no options or optional SAS statements, the Means procedure will print out the number of non-missing values, mean, standard deviation, minimum, and maximum for all numeric variables in the input data set

72. Means Procedure Options Statistics Available Note: The default alpha level for confidence limits is 95%. Use ALPHA= option to specify different alpha level. CLMTwo-Sided Confidence LimitsRANGERange CSSCorrected Sum of SquaresSKEWNESSSkewness CVCoefficient of VariationSTDDEVStandard Deviation KURTOSISKurtosisSTDERRStandard Error of Mean LCLMLower Confidence LimitSUMSum MAXMaximum ValueSUMWGTSum of Weight Variables MEANMeanUCLMUpper Confidence Limit MINMinimum ValueUSSUncorrected Sum of Squares NNumber Non-missing ValuesVARVariance NMISSNumber Missing ValuesPROBTProbability for Student’s t MEDIAN (or P50)MedianTStudent’s t Q1 (P25)25% QuantileQ3 (P75)75% Quantile P11% QuantileP55% Quantile P1010% QuantileP9090% Quantile P9595% QuantileP9999% Quantile

73. Means Procedure Optional SAS Statements VAR Variable1 Variable2; Specifies which numeric variables statistics will be produced for BY Variable1 Variable2; Calculates statistics for each combination of the BY variables Output out=output_data_set; Creates data set with the default statistics

74. FREQ Procedure PROC FREQ is used to generate frequency tables Most common usage is create table showing the distribution of categorical variables General Form:PROC FREQ DATA=input_data_set; TABLE variable1*variable2*variable3/ ; RUN; Options LIST – prints cross tabulations in list format rather than grid MISSING – specifies that missing values should be included in the tabulations OUT=output_data_set – creates a data set containing frequencies, list format NOPRINT – suppress printing in the output window Use BY statement to get percentages within each category of a variable

75. Summary Procedures Questions/Comments

76. Statistical Analysis Procedures 1.Correlation – PROC CORR 2.Regression – PROC REG 3.Analysis of Variance – PROC ANOVA 4.Chi-square Test of Association – PROC FREQ 5.General Linear Models – PROC GENMOD

77. CORR Procedure PROC CORR is used to calculate the correlations between variables Correlation coefficient measures the linear relationship between two variables Values Range from -1 to 1 Negative correlation - as one variable increases the other decreases Positive correlation – as one variable increases the other increases 0 – no linear relationship between the two variables 1 – perfect positive linear relationship -1 – perfect negative linear relationship General Form:PROC CORR DATA=input_data_set VAR Variable1 Variable2; With Variable3; RUN;

78. CORR Procedure If the VAR and WITH statements are not used, correlation is computed for all pairs of numeric variables Options include SPEARMAN – computes Spearman’s rank correlations KENDALL – computes Kendall’s Tau coefficients HOEFFDING – computes Hoeffding’s D statistic

79. REG Procedure PROC REG is used to fit linear regression models by least squares estimation One of many SAS procedures that can perform regression analysis Only continuous independent variables (Use GENMOD for categorical variables) General Form: PROC REG DATA=input_data_set MODEL dependent=independent1 independent2/ ; ; RUN; PROC REG statement options include PCOMIT=m - performs principle component estimation with m principle components CORR – displays correlation matrix for independent variables in the model

80. REG Procedure MODEL statement options include SELECTION= Specifies a model selection procedure be conducted – FORWARD, BACKWARD, and STEPWISE ADJRSQ - Computes the Adjusted R-Square MSE – Computes the Mean Square Error COLLIN – performs collinearity analysis CLB – computes confidence limits for parameter estimates ALPHA= Sets significance value for confidence and prediction intervals and tests

81. REG Procedure Optional statements include PLOT Dependent*Independent1 – generates plot of data

82. ANOVA Procedure PROC ANOVA performs analysis of variance Designed for balanced data (PROC GLM used for unbalance data) Can handle nested and crossed effects and repeated measures General Form: PROC ANOVA DATA=input_data_set ; CLASS independent1 independent2; MODEL dependent=independent1 independent2; ; Run; Class statement must come before model statement, used to define classification variables

83. ANOVA Procedure Useful PROC ANOVA statement option – OUTSTAT=output_data_set Generates output data set that contains sums of squares, degrees of freedom, statistics, and p-values for each effect in the model Useful optional statement – MEANS independent1/ Used to perform multiple comparisons analysis Set to: TUKEY – Tukey’s studentized range test BON – Bonferroni t test T – pairwise t tests Duncan – Duncan’s multiple-range test Scheffe – Scheffe’s multiple comparison procedure

84. FREQ Procedure PROC FREQ can also be used to perform analysis with categorical data General Form:PROC FREQ DATA=input_data_set; TABLE variable1 variable2/ ; RUN; TABLE statement options include: AGREE – Tests and measures of classification agreement including McNemar’s test, Bowker’s test, Cochran’s Q test, and Kappa statistics CHISQ - Chi-square test of homogeneity and measures of association MEASURE - Measures of association include Pearson and Spearman correlation, gamma, Kendall’s Tau, Stuart’s tau, Somer’s D, lambda, odds ratios, risk ratios, and confidence intervals

85. GENMOD Procedure PROC GENMOD is used to estimate linear models in which the response is not necessarily normal Logistic and Poisson regression are examples of generalized linear models General Form: PROC GENMOD DATA=input_data_set; CLASS independent1; MODEL dependent = independent1 independent2/ dist= link= ; run;

86. GENMOD Procedure DIST = - specifies the distribution of the response variable LINK= - specifies the link function from the linear predictor to the mean of the response Example – Logistic Regression DIST = binomial LINK = logit Example – Poisson Regression DIST = poisson LINK = log

87. Statistical Analysis Procedures Questions/Comments