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Lecture 4

Lecture 4. Ways to get data into SAS Some practice programming Review of statistical concepts. Getting data into SAS. DATALINES statement Data is contained within a data step INFILE statement Data contained in separate file PROC IMPORT Data contained in separate file.

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Lecture 4

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  1. Lecture 4 • Ways to get data into SAS • Some practice programming • Review of statistical concepts

  2. Getting data into SAS • DATALINES statement • Data is contained within a data step • INFILE statement • Data contained in separate file • PROC IMPORT • Data contained in separate file

  3. * List Directed Input: Reading data values separated by spaces.; DATA bp; INFILE DATALINES; INPUT clinic $ dbp6 sbp6 dbpbl sbpbl; DATALINES; C 84 138 93 143 D 89 150 91 140 A 78 116 100 162 A . . 86 155 C 81 145 86 140 ; RUN ; TITLE'Data Separated by Spaces'; PROCPRINTDATA=bp; RUN; Obs clinic dbp6 sbp6 dbpbl sbpbl 1 C 84 138 93 143 2 D 89 150 91 140 3 A 78 116 100 162 4 A . . 86 155 5 C 81 145 86 140

  4. * List Directed Input: Reading data values separated by commas; DATA bp; INFILE DATALINES DLM = ',' ; INPUT clinic $ dbp6 sbp6 dbpbl sbpbl; DATALINES; C,84,138,93,143 D,89,150,91,140 A,78,116,100,162 A,.,.,86,155 C,81,145,86,140 ; RUN ; TITLE'Data separated by a comma'; PROCPRINTDATA=bp; RUN;

  5. * List Directed Input: Reading data values from a .csv type file; DATA bp; INFILE DATALINES DLM = ','DSD ; INPUT clinic $ dbp6 sbp6 dbpbl sbpbl; DATALINES; C,84,138,93,143 D,89,150,91,140 A,78,116,100,162 A,,,86,155 C,81,145,86,140 ; TITLE 'Reading in Data using the DSD Option'; PROCPRINTDATA=bp; RUN;

  6. * List Directed Input: Reading data values separated by tabs (.txt files); DATA bp; INFILE DATALINES DLM = '09'xDSD; INPUT clinic $ dbp6 sbp6 dbpbl sbpbl; DATALINES; C 84 138 93 143 D 89 150 91 140 A 78 116 100 162 A 86 155 C 81 145 86 140 ; TITLE'Reading in Data separated by a tab'; PROCPRINTDATA=bp; RUN;

  7. * Reading data from an external file DATA bp; INFILE'/home/ph5415/data/bp.csv'DSD FIRSTOBS = 2; INPUT clinic $ dbp6 sbp6 dbpbl sbpbl ; TITLE'Reading in Data from an External File'; PROCPRINTDATA=bp; clinic,dbp6,sbp6,dbpbl,sbpbl C,84,138,93,143 D,89,150,91,140 A,78,116,100,162 A,,,86,155 C,81,145,86,140 Content of bp.csv

  8. *Using PROC IMPORT to read in data ; PROCIMPORTDATAFILE='/home/ph5415/data/bp.csv' OUT = bp DBMS = csv REPLACE ; GETNAMES = yes; TITLE'Reading in Data Using PROC IMPORT'; PROCPRINTDATA=bp; PROCCONTENTS DATA=bp;

  9. The CONTENTS Procedure Data Set Name: WORK.BP Observations: 5 Member Type: DATA Variables: 5 Engine: V8 Indexes: 0 Created: 18:15 Tuesday, January 25, 2005 Observation Length: 40 Last Modified: 18:15 Tuesday, January 25, 2005 Deleted Observations: 0 Protection: Compressed: NO Data Set Type: Sorted: NO Label: -----Alphabetic List of Variables and Attributes----- # Variable Type Len Pos ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1 clinic Char 8 32 2 dbp6 Num 8 0 4 dbpbl Num 8 16 3 sbp6 Num 8 8 5 sbpbl Num 8 24

  10. Some Definitions • Statistics: The art and science of collecting, analyzing, presenting, and interpreting numerical data. • Data: facts and figures that are analyzed • Dataset: All the data collected for a study • Elements: Units in which data is collected • People, companies, schools, households • Variables: Characteristics measured on elements • People (height, weight) • Company (number of employees) • Schools (percentage of students who graduate in 5 years) • Households (number of computers owned)

  11. Informal Definition • Statistics: In a scientific way gain information about something you do not know

  12. Start With Research Question • What is the proportion of persons without health insurance in Minnesota? • Do newer BP medications prevent heart disease compared to older medications? • What is the relationship between grade point average and SAT scores • Do persons who eat more F&V have lower risk of developing colon cancer. • Does the program DARE reduce the risk of young persons trying drugs?

  13. Statistics Start With Question Design Study And Collect Data Compute Summary Data to Assess Question. Make Conclusions (Inference)

  14. Statistical Inference • Estimation (Chapter 4) • Hypothesis Testing (Chapter 5) • Comparing population proportions (Chap 6) • Comparing population means (Chap 7)

  15. Common Parameters to Estimate

  16. The value of is used to make inferences about the value of m. The sample data provide a value for the sample mean . Statistical Inference Population with mean m = ? A simple random sample of n elements is selected from the population.

  17. Sampling • Sample: a subset of target population (usually a simple random sample - each sample has equal probability of occurring) • Different samples yield different estimates • Trying to understand the population parameter (the “true value”) • It’s usually not possible to measure the population value

  18. Point Estimate

  19. Interval Estimation In general, confidence intervals are of the form: Estimate = mean, proportion, regression coefficient, odds ratio... SE = standard error of your estimate 1.96 = for 95% CI based on normal distribution

  20. Estimation “What is the average total cholesterol level for MN residents?” Random sample of cholesterol levels sample mean = sum of values / number of observations Estimates the population mean:

  21. Estimation “What is the average total cholesterol level for MN residents?” sample standard deviation: estimates the population standard deviation:

  22. Confidence Interval Example Suppose sample of 100 mean = 215 mg/dL, standard deviation = 20 95% CI = = standard error of mean = (215 - 1.96*20/10, 215 + 1.96*20/10) approximately = (211, 219)

  23. Properties of Confidence Intervals • As sample size increases, CI gets smaller • If you could sample the whole population; • Can use different levels of confidence • 90, 95, 99% common • More confidence means larger interval; so a 90% CI is smaller than a 99% CI • Changes with population standard deviation • More variable population means larger interval

  24. Caution with Confidence Intervals • Data should be from random sample • More complicated sampling requires different methods • Example - multistage or stratified sampling • Outliers can cause problems • Non-normal data can change confidence level • Skewed data a big problem • Bias not accounted for • Non-responders • Target and sampled population different

  25. 95% Confidence Intervals with SAS 1) Construct from output estimate +/- 1.96*SE 2) Provided automatically by some procedures PROC MEANS DATA = STUDENTS LCLM UCLM; VAR AGE;

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