SAS软件统计数据分析：中级统计方法

数据分析

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"Statistical Data Analysis Using SAS software" 是一本由Mervyn G. Marasinghe和Kenneth J. Koehler合著的统计学教材，属于SpringerTexts in Statistics系列。该书分为基础篇和提高篇，旨在教授读者如何利用SAS软件进行统计数据分析。内容涵盖数据处理、描述统计、假设检验、图形绘制、回归分析、多元统计、方差分析、协方差分析以及时间序列分析。提高篇进一步介绍了SAS编程和SAS/IML模块，后者是一种交互式矩阵运算语言，适合进行复杂和特殊的统计分析。在本书中，作者首先介绍了SAS的基本操作，如数据的输入和输出，这是所有统计分析的前提。数据整理是数据分析的重要步骤，它包括数据清洗、缺失值处理和变量转换等，以确保数据质量。接着，书中详述了描述性统计，如均值、中位数、标准差等统计量的计算，用于概括数据的基本特征。在假设检验部分，作者讲解了t检验、ANOVA（方差分析）、卡方检验等常用统计方法，这些方法用于判断样本间的差异是否具有统计显著性。图形的制作是数据分析的可视化部分，SAS提供了丰富的图表类型，如直方图、散点图、箱线图等，有助于直观理解数据分布和关系。回归分析章节则涵盖了线性回归、逻辑回归等，帮助读者理解变量间的关系。多元统计分析涉及主成分分析、因子分析等，用于处理多变量问题。方差分析和协方差分析则用于比较多个组间的差异和变量间的协变情况。时间序列分析章节则针对具有时间顺序的数据，包括趋势分析、季节性分析和自相关性分析等，适用于经济、金融、气象等领域。提高篇的SAS/IML模块和矩阵运算语言部分，使读者能够编写复杂脚本，执行高级统计计算，适合研究人员和需要进行特殊分析方法的用户。 "Statistical Data Analysis Using SAS software" 是一本全面介绍SAS在统计分析应用的教材，既适合初学者学习基本统计概念和SAS操作，也适合有经验的分析师提升SAS编程和高级统计分析技巧。书中的实例和程序代码有助于读者更好地理解和应用所学知识。

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2 1 Introduction to the SAS Language

formatted by the user to produce a desired appearance (called an ODS style).

Thus ODS allows the user the ﬂexibility in presenting the output from SAS

procedures in a style of user’s own choice. Beginning with SAS Version 9.3,

instead of routing the output to a listing destination in the output window,

SAS windowing system is set up by default to use an html destination and for

the resulting html ﬁle to be automatically displayed using an internal browser.

The user may modify these default settings by selecting Tools ➡ Options

➡ Preferences from the main menu system on the SAS window. Figure 1.1

shows the default settings under the Results tab of the Preferences window.

Fig. 1.1. Screenshot of the results tab on the preferences dialog box

Note the check boxes that are selected on this dialog. Thus the creation of

html output is enabled by default, while the creation of the listing output

is not. Also note that the style selected (from a drop-down list) is Htmlblue,

the default style associated with the html destination. An ODS style is a

description of the appearance and structure of tables and graphs in the ODS

output and how these are integrated in the output and is speciﬁed using a

style template.TheHtmlblue style is an all-color style that is designed to

integrate tables and statistical graphics and present these as a single entity.

Note that the Use ODS Graphics box is checked meaning that the creation of

ODS Graphics, the functionality of automatically creating statistical graphics,

is also enabled. This is equivalent to including a ODS Graphics On statement

within the SAS program, whenever ODS Graphics are to be produced by

default or as a result of a user request initiated from a procedure that supports

ODS Graphics. The following example illustrates the default ODS output

produced by SAS.

1.1 Introduction 3

data biology;

input Id Sex Age Year Height Weight;

BMI=703*Weight/Height**2;

datalines;

7389 M 24 4 69.2 132.5

3945 F 19 2 58.5 112.0

4721 F 20 2 65.3 98.6

1835 F 24 4 62.8 102.5

9541 M 21 3 72.5 152.3

2957 M 22 3 67.3 145.8

2158 F 21 2 59.8 104.5

4296 F 25 3 62.5 132.5

4824 M 23 4 74.5 184.4

5736 M 22 3 69.1 149.5

8765 F 19 1 67.3 130.5

5734 F 18 1 64.3 110.2

4529 F 19 2 68.3 127.4

8341 F 20 3 66.5 132.6

4672 M 21 3 72.2 150.7

4823 M 22 4 68.8 128.5

5639 M 21 3 67.6 133.6

6547 M 24 2 69.5 155.4

8472 M 21 2 76.5 205.1

6327 M 20 1 70.2 135.4

8472 F 20 4 66.8 142.6

4875 M 20 1 74.2 160.4

;

proc means data=biology mean std min max maxdec=3;

class Sex;

var BMI;

title "Biology class: BMI Statistics by Gender";

run;

Fig. 1.2. Illustrating ODS output

An Introductory SAS Program

The SAS code displayed in Fig. 1.2 is used here to give the reader a quick

introduction to a complete SAS program. The raw data consists of values for

several variables measured on students enrolled in an elementary biology class

at a college during a particular semester. In this program an input statement

reads raw data from data lines embedded in the program (called instream

data) and creates a SAS data set named biology.

The list input style used in this program scans the data lines to ac-

cess values for each of the variables named in the input statement. No-

tice that the data values are aligned in columns but also are separated by

(at least) one blank. The “$” symbol used in the input statement indicates

that the variable named Sex contains character values. The SAS expression

703*Weight/Height**2 calculates a new value using the values of the two

variables Weight and Height obtained from the current data line being pro-

cessed and assigns it to a (newly created) variable named BMI representing

the body mass index of the individual (the conversion factor 703 is required

as the two variables Weight and Height were not recorded in metric units

as needed by the deﬁnition of body mass index). Once the SAS data set is

created and saved in a temporary folder, the SAS procedure named MEANS

4 1 Introduction to the SAS Language

is used to produce an analysis containing some statistics for the new variable

BMI separately for the females and males in the class. Figure 1.3 displays a

reproduction of the default html output displayed by the Results Viewer in

SAS and illustrates the Htmlblue style.

The MEANS Procedure

Biology class: BMI Statistics by Gender

Analysis Variable : BMI

Sex

Obs

Mean Std Dev Minimum Maximum

F 10 20.366 2.341 16.256 23.846

M 12 21.236 1.775 19.085 24.638

Fig. 1.3. ODS output

In most of the SAS examples used in this book, the pdf-formatted ODS

version of the resulting output will be used to display the output. An ODS

statement (not shown in all SAS programs) will be used to direct the output

produced to a pdf destination. Note carefully that since the destination is

diﬀerent from html, the output produced is in a diﬀerent style than Htmlblue;

that is, the output is formatted for printing rather than for being displayed

in a browser window.

An alternative way of running SAS programs for producing ODS-formatted

output is to use the SAS Enterprise Guide (SAS/EG). SAS/EG is a point-

and-click interface for managing data, performing a statistical analysis, and

generating reports. Behind the scenes, SAS/EG generates SAS programs that

are submitted to SAS, and the results returned back to SAS/EG. Since the

focus of this book is SAS programming, general instructions on how to use

SAS/EG is not discussed here. However, SAS/EG includes a full programming

interface that uses a color-coded, syntax-checking SAS language editor that

can be used to write, edit, and submit SAS programs and is available to SAS

programmers as an alternative to using the SAS windowing environment.

Further, the output in SAS/EG is automatically produced in ODS format,

and the user can select options for the output to be directed to a destination

such as a pdf or an html ﬁle.

Most statistical analysis does not require knowledge of the considerable

number of features available in the SAS system. However, even a simple anal-

ysis will involve the use of some of the extensive capabilities of the language.

Thus, to be able to write SAS programs eﬀectively, it is necessary to learn at

least a few SAS statement structures and how they work. The following SAS

program contains features that are common to many SAS programs.

6 1 Introduction to the SAS Language

statement 2 , and a group of statements used for analyzing a SAS data set

(called a SAS proc step) can be recognized because it begins with a proc

statement

3 . There may be several of each kind of these steps in a SAS pro-

gram that logically deﬁnes a data analysis task.

SAS interprets and executes these steps in their order of appearance in a

program. Therefore, the user must make sure that there is a logical progression

in the operations carried out. Thus, a proc step must follow the data step

that creates the SAS data set to be analyzed by that proc step. Although

statements in a data step are executed sequentially, in order that computations

are carried out on the data values as expected, statements within the step

must also satisfy this requirement, in general, except for certain declarative

or nonexecutable statements. For example, an input statement that deﬁnes

variables must precede executable SAS statements, such as SAS programming

statements, that references those variable names.

One very important characteristic of the execution of a SAS data step is

that the statements in a data step are executed and an observation written

to the output SAS data set, repeatedly for every line of data input in cyclic

fashion, until every data line is processed. A detailed discussion of data step

processing is given in Sect. 1.6.

The ﬁrst statement following the data statement

2 in the data step usually

(but not always) is an input statement, especially when raw data are being

accessed. The input statement used here is a moderately complex example

of aformattedinputstatement, described in detail in Sect. 1.4.Thesymbols

and informats used to read the data values for the variables Income, Tax,

Age, and State from the data lines in SAS Example A1 and their eﬀects are

itemized as follows:

• @4 causes SAS to begin reading each data line at column 4.

• 2*5.2 reads data values for Income and Tax from columns 4–8 and 9–13,

respectively, using the informat 5.2 twice, that is, two decimal places are

assumed for each value.

• 2. reads the data value for Age from columns 14 and 15 as a whole number

(i.e., a number without a fraction portion) using the informat 2.

• $2. reads the data value for State from columns 16 and 17 as a character

string of length 2, using the informat $2.

A semicolon symbol “;” appearing by itself in the ﬁrst column in a data line

signals the end of the lines of raw data supplied instream in the current data

step. On its encounter, SAS proceeds to complete the creation of the SAS data

set named first by closing the ﬁle. The proc print;

3 that follows the data

step signals the beginning of a proc step. The SAS data set processed in this

proc step is, by default, the data set created immediately preceding it (in this

program the SAS data set first was the only one created). Again, by default,

all variables and observations in the SAS data set will be processed in this

proc step.

The output from execution of the SAS program consists of two parts: the

SAS Log (see Fig. 1.5), which is a running commentary on the results of ex-

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