北航数理统计大作业基于SPSS的建筑业企业房屋竣工面积聚类与判别分析

本科毕业设计（论文）

数理统计

基于SPSS的建筑业企业房屋竣工面积

聚类与判别分析

Cluster and Discriminant Analysis of Completed Area of Houses in Construction Enterprises Based on SPSS

学院：仪器科学与光电工程学院

专业：仪器科学与技术

作者：池浩湉

学号：SY1617228

指导教师：孙海燕

北京航空航天大学

2017年11月

北京航空航天大学--数理统计A13 中文摘要

中文摘要

摘要：改革开放以来，特别是20世纪90年代以来，城镇化的快速发展、城镇住房制度的改革及其深化，使房地产在推动我国国民经济发展，提高人民生活水平方面发挥了重要作用。因此，建筑业对国民经济及其他产业的推动作用不可忽视，因此，研究建筑业企业房屋竣工面积具有重要的意义。

为了研究全国不同地区建筑业企业房屋竣工面积情况，本文收集了2016年第三季度全国31个省、直辖市、自治区的建筑业企业房屋竣工面积的数据，包括住宅房屋、商业及服务用房屋、办公用房屋科研、教育和医疗用房屋、文化体育娱乐用房屋、厂房及建筑物、仓库这7个类别的房屋竣工面积的数据，利用SPSS软件进行聚类分析，并建立判别函数模型，将不同地区进行分类，分别得到分为3类和4类的两个模型，且这个两个模型中，浙江省和江苏省均各占一类。之后利用建立的分类模型，对新的变量2016年第二季度河北省、江苏省、浙江省和安徽省这四个省份进行判别分类，得到了较好的结果，最后，对结果进行了分析。

关键词：Building completion area; SPSS; cluster analysis; discriminant analysis

北京航空航天大学--数理统计A13 English abstract

ABSTRACT

ABSTRACT:Since the reform and opening up, especially since 1990s, the rapid development of urbanization, the reform of the urban housing system and the deepening of the real estate in promoting the development of our national economy, play an important role in improving the level of people's life. Therefore, the construction industry on the national economy and other industries to promote the role can not be ignored, therefore, the study of construction enterprises housing completion area is of great significance.

In order to study the enterprises in different regions of the country building housing area, this paper collected the housing area of the third quarter of 2016, the country's 31 provinces, municipalities and autonomous regions, the construction enterprise data, including commercial and residential housing services, buildings, office buildings, education and medical research buildings, cultural and sports entertainment housing, plant and warehouse buildings, these 7 categories of housing area data, clustering analysis by SPSS software, and establish the discriminant function model, different area classification, are divided into two types and 4 types of model 3, and the two model, Zhejiang province and Jiangsu province each class. After using the classification model, classify the new variable of the second quarter of 2016 in Hebei Province, Jiangsu Province, Zhejiang province and Anhui Province, the four provinces, and better results are obtained, finally, the results are analyzed.

KEYWORDS：Production Price Index ofGrain Grain; SPSS; Stepwise Regression；Multiple Linear Regression

目录

中文摘要..................................................................................................................................... I ABSTRACT .............................................................................................................................. II 目录.......................................................................................................................................... III 1引言 (1)

2数据采集 (2)

3聚类分析 (3)

3.1聚类分析过程 (3)

3.2聚类分析结果 (3)

4判别分析 (6)

4.1判别函数建立 (6)

4.2模型评价 (8)

4.3新变量判别 (9)

5结论 (11)

参考文献 (12)