Background: Globally tuberculosis (TB) is one of the leading causes of mortality. There is scientific evidence of sociodemographic, behavioral and health risk factors associated with TB infection and TB disease. In the United States (US), there is a low endemicity of TB and a goal of TB elimination. Objective: The primary objective of the study was to examine the correlation of TB risk factors at the state level in the US to obtain insights specific to the state of TB in the US. The risk factors examined were diabetes rates, smoking rates, alcohol abuse rates, AIDS rates, foreign-born vs. US-born, poverty as expressed by GINI and per capita income and race/ethnicity. Methods: Secondary data from the Centers for Disease Control and Prevention (CDC) and US Census Bureau on line databases were used. Simple linear regression, bivariate correlation and multiple linear regression were carried out. Results: Significant correlations were found at the state level between TB disease rates and being non-Hispanic White (r=-0.856, p<0.001), foreign-born (r=0.649, p<0.001), GINI (r=0.588, p<0.001) and AIDS diagnosis rates (r=0.579, p<0.001). No significant associations were found between TB disease rates and diabetes rates, smoking rates and alcohol abuse rates. Conclusion: The focus of the fight against TB in the US should be on minority communities, those populated by the foreign-born and those with high rates of AIDS particularly where a large degree of income inequality is present.

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Despite an improved understanding of the disease, the prevalence of asthma and asthma-related morbidity continue to rise, particularly among minority and inner-city populations. Despite the growing epidemic of asthma, the surveillance of disease at the state or even local levels is very limited. Such information is very important to identify high-risk population groups and to design more effective community-based preventive interventions or risk management programs that may modify these trends. The study provided important information about spatial differences by the geographical area of residence and changes in asthma hospital admissions over time in the selected area. Environmental exposure to ambient air pollution by ambient particles, sulfur dioxide and ozone was a significant factor to explain the increase in asthma hospitalizations in simple regression analysis, but was not significant after the adjustment to area socioeconomic status characteristics. Sulfur dioxide was the only significant independent variable in a multiple adjusted regression model of hospitalizations for childhood asthma, however, more detailed environmental exposure assessment by calendar quarter suggested that ambient air pollution by sulfur dioxide is not significant variable in the multiple regression model. Future asthma prevention interventions and risk management programs should address population groups described by such socioeconomic status characteristics as poverty, unskilled workers, single parent families with children, families having no vehicle available, people living in less crowded households or socially excluded conditions without adequate family members or relatives support, and also people residing in houses heated by fuel. Developed complex area socioeconomic deprivation index was shown to be a significant predictor of hospital admissions for childhood and adult asthma by zip code area of residence. Predictive loglinear regression model for asthma hospitalizations was further validated by using standard statistical model validation techniques to estimate the accuracy of prediction with new independent dataset outside of our study area. Increase in complex area socioeconomic deprivation index by 1 extra unit could explain the increase by 7.9% in childhood and 7.5% in adult asthma hospitalization in 1997, 8.3% in childhood and 7.2% in adult asthma hospitalizations in 1998, and 7.7% in childhood and 6.7% in adult asthma hospitalizations in 1999 respectively. Predictive log-linear regression model could be successfully applied to develop more effective asthma prevention interventions and risk management programs and to address more sensitive population groups within specific high risk geographical areas.

Orientador: Mara de Andrade Marinho Weill / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-05T13:28:12Z (GMT). No. of bitstreams: 1 Lourenco_LeonardoSenede_M.pdf: 607870 bytes, checksum: fe4db0d68393c2258c9d6605c0faa233 (MD5) Previous issue date: 2005 / Resumo: O desenvolvimento de sensores orbitais de alta resolução espacial e espectral e a perspectiva de maior periodicidade na obtenção de imagens tem incentivado a aplicação crescente de técnicas de sensoriamento remoto no estudo de características espectrais das culturas relacionadas com seu potencial de produção. Os chamados índices de vegetação têm sido utilizados como critérios para estimar a resposta espectral da cultura e, indiretamente, sua produtividade. A resposta espectral é uma medida do vigor vegetativo da cultura, sendo afetada por fatores ambientais, do manejo e da planta. O presente estudo teve por objetivo central estudar a influência de atributos do solo e da planta na variação da resposta espectral da cultura da cana-de-açúcar, medida por meio do índice de vegetação da diferença normalizada (NDVI), aplicando métodos estatísticos uni e multivariados. O estudo foi realizado em um talhão de produção comercial de cana-de-açúcar de cerca de 26 ha, no município de Araras (SP), entre as coordenadas 47º19¿02¿ e 47º19¿26¿ W e 22º21¿53¿ e 22º22¿12¿ S. A lavoura foi implantada em setembro de 1997 com a variedade SP80-1842, precoce, de hábito decumbente e acamamento regular. O delineamento amostral constou de uma grade regular, composta por 67 pontos amostrais georreferenciados e espaçados de 50 m nas direções X e Y, de onde foram extraídas amostras de solo (camadas 0- 30 cm e 30-60 cm) e foliares. As amostras de solo foram extraídas em setembro de 2000 e de 2001, logo após a colheita. As amostras foliares foram extraídas durante a fase de desenvolvimento vegetativo da cultura, no mês de janeiro de 2001 e de 2002. Os anos agrícolas estudados correspondem ao 4º (2000/01) e 5º (2001/02) cortes. Foram avaliados atributos granulométricos e de fertilidade do solo e os teores foliares de macro e micronutrientes (variáveis preditoras). O índice de vegetação NDVI (variável predita) foi calculado a partir de imagens LANDSAT 7, sensor ETM+, obtidas em duas épocas durante a fase de desenvolvimento vegetativo da cultura. Para análise da influência da variação dos atributos edáficos e foliares na variação observada no NDVI foram empregados os métodos estatísticos referidos por análise exploratória, análise de agrupamentos, análise de componentes principais e análise de regressão linear múltipla, adotando-se o método stepwise para seleção de variáveis e ajuste dos modelos. Foram ajustados dois modelos de regressão linear múltipla. O modelo de regressão ajustado aos dados de 2000/01 explicou 30,8% da variação observada da resposta espectral em função da matéria orgânica (M.O., 0-30 cm) e dos teores foliares de fósforo (P, Planta) e de ferro (Fe, Planta). A inclusão desses atributos no modelo pode ser interpretada no caso da matéria orgânica pela similaridade com o NDVI, conforme resultado da análise de agrupamento; no caso do ferro por sua representatividade como integrante do 1º componente principal, e no caso do fósforo por sua baixa correlação com os demais atributos analisados, conforme indicado pela análise exploratória. O modelo de regressão ajustado aos dados de 2001/02 explicou 29,8% da variação observada da resposta espectral em função dos teores de cobre (Cu) e ferro (Fe) na camada 0-30 cm e do teor de enxofre (S-SO4) na camada 30-60 cm. Interpreta-se a inclusão desses atributos no modelo no caso do enxofre por sua representatividade como integrante do 1º componente principal e do ferro como integrante do 3º componente. No caso do cobre, sua inclusão deve estar baseada na média correlação com a resposta espectral (NDVI) e baixa correlação com os demais atributos do modelo, de acordo com os dados da análise exploratória. Os resultados obtidos permitiram comprovar a hipótese do trabalho. Parte da variação observada da resposta espectral na área de estudo pôde ser explicada pela variação de atributos do solo (fator de produção) e da planta. No entanto, entende-se que a capacidade de explicação dos modelos poderia ter sido maior caso tivessem sido incluídas na análise outras variáveis, sobretudo climáticas, bem como, variáveis edáficas que permitissem avaliar o efeito de fatores como compactação e resistência à penetração, tendo em vista se tratar de solos argilosos e muito argilosos / Abstract: The development of multispectral sensors with high spatial and spectral resolutions and the perspective of greater regularity in the attainment of the images have stimulated the increasing application of the remote sensing techniques in the study of spectral response patterns of the crops relating with their potential of production. The spectral response pattern is a measure of the vegetative vigor of a crop, being affected by genotype, management and environmental factors. The main objective of the present research was to study the influence of selected soil and plant attributes in the observed variation of the spectral response pattern of the sugarcane crop, measured by means of the normalized difference vegetation index (NDVI), applying multivariate statistics methods. The study was developed in a commercial area (26 ha) of sugarcane production in Araras (SP), between the coordinates 47º19'02 "and 47º19'26" W and 22º21'53"and 22º22'12" S. The crop was installed in September/1997 with the variety SP80-1842. The experimental delineation was a regular grid, composed by 67 points of sampling, georeferenced, and spaced of 50 m in the X and Y directions, from where had been extracted the plant (leaves) and the soil samples (0-30 cm and 30-60 cm). The soil samples were extracted in September (2000 and 2001), after the harvest. The plants were sampled during the phase of vegetative development of the crop, in January (2001 and 2002). The attributes evaluated were grain sized and fertility attributes (soils) and nutrient contents in leaves (plant). The vegetation index NDVI was calculated from LANDSAT 7, sensor ETM+ images. The statistics methods of analysis have included exploratory analysis, cluster analysis, principal component analysis (PCA), and multiple regression analysis, using the stepwise criterion for selection of the variables and model adjustment. Two linear multiple regression models have been adjusted. The first model (2001/02) could explain 30,8% of the observed variation of NDVI as a function of the soil organic matter (M.O., 0-30 cm) and of the phosphorus (P, Plant) and of the iron (Fe, Plant) contents in leaves. The inclusion of these attributes in the model can be interpreted with basis on the case of the soil organic matter for its similarity with the NDVI, as indicated by cluster analysis. In the case of the iron content its inclusion could be interpreted for its significance as integrant of the first component in PCA, and in the case of the P content with basis on its low correlation with the all others attributes, as indicated for the exploratory analysis. The second regression model (2001/02) could explain 29,8% of the observed variation of NDVI as a function of soil contents of copper (Cu) and iron (Fe) in the first layer (0-30 cm) and the sulphur content (S-SO4) in the second layer (30-60 cm). The inclusion of these attributes in the second model can be interpreted in the case of the S-SO4 content according its significance as integrant of first component in PCA, and the iron content according its significance as integrant of third component in PCA. In the case of copper, its inclusion must be explained with basis in its average correlation with the NDVI and small correlation with the all other attributes of the model, as indicated for the exploratory analysis. The results have permitted to accept the hypothesis of the work. Part of the observed variation of the spectral response pattern in the study area could be explained by the local variation of some soil (production factor) and plant attributes. However, that the capacity of explanation of the two adjusted models could have been better if another variables, in particular the ones related with climate and soil hardness, have been included in the analysis / Mestrado / Planejamento e Desenvolvimento Rural Sustentável / Mestre em Engenharia Agrícola

La reconnaissance d’activités humaines est un sujet de recherche d’actualité comme en témoignent les nombreux travaux de recherche sur le sujet. Dans ce cadre, la reconnaissance des activités physiques humaines est un domaine émergent avec de nombreuses retombées attendues dans la gestion de l’état de santé des personnes et de certaines maladies, les systèmes de rééducation, etc.Cette thèse vise la proposition d’une approche pour la reconnaissance automatique et non-intrusive d’activités physiques quotidiennes, à travers des capteurs inertiels de type accéléromètres, placés au niveau de certains points clés du corps humain. Les approches de reconnaissance d’activités physiques étudiées dans cette thèse, sont catégorisées en deux parties : la première traite des approches supervisées et la seconde étudie les approches non-supervisées. L’accent est mis plus particulièrement sur les approches non-supervisées ne nécessitant aucune labellisation des données. Ainsi, nous proposons une approche probabiliste pour la modélisation des séries temporelles associées aux données accélérométriques, basée sur un modèle de régression dynamique régi par une chaine de Markov cachée. En considérant les séquences d’accélérations issues de plusieurs capteurs comme des séries temporelles multidimensionnelles, la reconnaissance d’activités humaines se ramène à un problème de segmentation jointe de séries temporelles multidimensionnelles où chaque segment est associé à une activité. L’approche proposée prend en compte l’aspect séquentiel et l’évolution temporelle des données. Les résultats obtenus montrent clairement la supériorité de l’approche proposée par rapport aux autres approches en termes de précision de classification aussi bien des activités statiques et dynamiques, que des transitions entre activités. / Human activity recognition is currently a challengeable research topic as it can be witnessed by the extensive research works that has been conducted recently on this subject. In this context, recognition of physical human activities is an emerging domain with expected impacts in the monitoring of some pathologies and people health status, rehabilitation procedures, etc. In this thesis, we propose a new approach for the automatic recognition of human activity from raw acceleration data measured using inertial wearable sensors placed at key points of the human body. Approaches studied in this thesis are categorized into two parts : the first one deals with supervised-based approaches while the second one treats the unsupervised-based ones. The proposed unsupervised approach is based upon joint segmentation of multidimensional time series using a Hidden Markov Model (HMM) in a multiple regression context where each segment is associated with an activity. The model is learned in an unsupervised framework where no activity labels are needed. The proposed approach takes into account the sequential appearance and temporal evolution of data. The results clearly show the satisfactory results of the proposed approach with respect to other approaches in terms of classification accuracy for static, dynamic and transitional human activities

Syftet med denna studie är att undersöka hur nivån på humankapital i ett land kommer att påverka effekten av utländska direktinvesteringar på ekonomisk tillväxt. Studien omfattar 31 OECD-länder under tidsperioden 1989-2020. FDIs inverkan på ekonomisk tillväxt undersöks i en multipel regressionsmodell med paneldata där fokus ligger på samspelet mellan utländska direktinvesteringar och utbildning (en proxy för humankapital). Det teoretiska ramverket är huvudsakligen fokuserat på Romers endogena tillväxtteori och institutionell teori. Resultatet av den multipla regressionsmodellen visar att utländska direktinvesteringar som en enskild variabel har en positiv effekt på ekonomisk tillväxt. Samspelet mellan utländska direktinvesteringar och humankapital visade sig ha en negativ effekt på den ekonomiska tillväxten. Resultatet av humankapitalet som en enskild faktor visade sig dock vara positivt i förhållande till ekonomisk tillväxt. Resultatet av studien indikerar att utländska direktinvesteringar som en oberoende faktor har en positiv inverkan på den ekonomiska tillväxten samt humankapital som en oberoende faktor. Resultatet tyder på att en hög nivå på humankapitalet i ett land attraherar inte tillräckligt mycket utländska direktinvesteringar för att ha en positiv effekt på den ekonomiska tillväxten. / The purpose of this study is to examine how the level of human capital in a country will influence the effect FDI has on economic growth. The study covers 31 OECD-countries during the time period 1989-2020. The impact of FDI on economic growth is examined in a multiple regression model with panel data where the focus is on the interaction between FDI and education (a proxy for human capital). The theoretical framework is mainly focused on Romer’s endogenous growth theory and institutional theory. The result of the multiple regression model shows that FDI as a single variable has a positive effect on economic growth. The interaction between FDI and human capital was shown to have a negative effect on economic growth. However, the result of human capital as a single factor was proved to be positive in relation to economic growth. The results indicate that a high level of human capital in a country does not attract enough foreign direct investment to have a positive effect on economic growth.

本文旨在檢定台電公司現行季節電價月份劃分之合理性，並探討影響用戶別售電量與電費收入之經濟因素。為達成此目的，本文先就負載觀點與成本觀點進行群集分析，以檢定季節電價是否具統計意義之正當性；其次建立經濟計量模型，分別採用戶別之總售電量與總電費收入做為被解釋變數，運用民國88年1月至民國91年12月之月資料進行實證分析。本文建立之經濟模型有二，分別為時間序列以及複迴歸方程式模型。經檢定分析後，本文就各實證參數之經濟意涵加以闡示，最後並提出結論以及未來研究之方向。 本文透過月資料之群集分析，顯示夏月相對於非夏月之群集差異與台電公司現行季節電價夏月與非夏月之月份相一致，證實台電公司季節電價月份劃分之合理性。其次，透過ARIMA時間序列建立之短期電力需求預測模型，經實證結果顯示：電燈與電力用戶別之售電量均逐年增加，預測民國93年1月至民國99年12月，電燈用戶之年售電量平均成長率為3.33%、電力用戶為3.23%。再者，利用複迴歸模型進行實證分析之結果發現：(一)影響售電量之主要變數為溫度。惟因電燈用戶每隔兩月抄表一次，與電力用戶按月抄表之作業方式不同，故電燈用戶每月售電量係受前期(月)溫度影響，而電力用戶則受當期(月)溫度影響。(二)各用戶別之總電費收入與售電量有明顯相關，且經估算出各月售電量之電費收入彈性顯示：電燈用戶約為0.5，電力用戶約為1。由於總電費收入為總售電量與平均電價之乘積，故電燈用戶之電費收入增加1% 時，其售電量僅增加0.5%，顯示總電費的收入增加係有部分來自於平均電價的提高；換言之，就電燈用戶別而言，其電費收入增減變化之百分比除了會受到售電量增減幅度之影響外，亦反映了平均電價變化的情形。同理，對電力用戶來說，其各月售電量之電費收入彈性接近於1，表示電費收入變化1% 時，售電量亦增加1%，即電費收入之增減變化比例主要受到售電量之同向等幅變化所影響。 至於各用戶別之電費收入方面，電燈與電力兩類用戶自民國88年初至91年底四年期間均有逐年增加之趨勢，惟電力用戶之年增加幅度有隨時間遞減之現象，且歷年大抵以7-10月份較高，2月份最低。此外，影響用戶別電費收入之解釋變數中，各類用戶之售電量最為顯著，其參數值係隱示每增加一度售電量對其電費收入之影響。其中，電燈用戶之估計參數值為2.69，而電力用戶則為1.35。再者，由其電費收入之售電量彈性係數可以發現：電燈用戶約為1.2，電力用戶約為0.7，顯示電燈用戶總售電量增加1%時，總電費收入增加的幅度大於1%，而電力用戶則相反。推估電力用戶此一彈性係數較電燈用戶低之原因在於：電力用戶與電燈用戶之電價結構不同，前者係採需量電費與能量電費之兩部電價制，而後者僅包含流動電費之一部電價。最後，實證結果亦顯示電力系統之尖峰負載與負載率會影響電費收入，惟其影響幅度不大。 / A Study on Customer-by-Category Energy Sales and Power Sales Revenue Model: The Case of Taiwan Power Company Abstract The main purposes of this study are to examine the rationality of the seasonal pricing scheme defined by summer and non-summer months and to identify economic factors influencing customer-by-category energy sales and power sales revenue, utilizing the data of Taiwan Power Company (Taipower) as an empirical case. In order to achieve this objective, the cluster analysis from the perspective of load pattern and cost pattern are examined respectively to see if the seasonal pricing scheme has statistical meaning in its pattern differences in terms of summer vs. non-summer season. Second, two economic models including time-series analysis and multiple regression equations are formulated for the empirical case study. The subtotal energy sales and the subtotal power sales revenue by different type of customer categories, i.e. lighting and industrial customers, are set to be the explained variables. Data from January 1999 to December 2002 are collected for modeling simulation tests. The economic meanings and policy implications of the modeling results are elaborated on. And conclusions with directions for further research are presented. Through the cluster analysis utilizing monthly data within the time frame mentioned above, empirical research results on the grouping cluster of summer vs. non-summer months shows a consistent trend with those defined by Taipower’s present seasonal pricing scheme. Second, the empirical results of ARIMA time-series model show that the forecasted energy sales of both lighting and industrial customers will be gradually increasing through January 2004 to December 2010, and the average annual growth rate of energy sales for the lighting customer is 3.33%, and for the industrial customer is 3.23%. On the other hand, the empirical research results through the multiple regression equations show that the main factor affecting the energy sales is temperature. Due to the different time schedules for reading electricity meters between the lighting customer and the industrial customer, i.e. the time interval for reading the meter of lighting customers is every two months and for industrial customers is every month, the monthly energy sales of the lighting customer are directly related to the temperature of the previous month, while the monthly sales of the industrial customer are directly related to the temperature of the present month. In addition, for each type of customers, there is an obvious correlation between the total power sales revenue and the total energy sales. Furthermore, the estimated elasticity of the total power sales revenue versus total energy sales is about 0.5 for the lighting customer, and about 1 for the industrial customer. Since the total power sales revenue is the product of total energy sales times the average electricity price, when the total power sales revenue increases 1% with the total energy sales only increases 0.5%, it implies that the increase of total power sales revenue not just only comes from the increase of energy sales, but also partially affected by the increase of average electricity price. Similarly, for the industrial customer, when the elasticity of their monthly total power sales revenue versus total energy sales is close to 1, it implies that when the total power sales revenue increases 1%, the total energy sales also increase about 1%. In other words, the change of percentage of the total power sales revenue is mostly attributed to the variation of total energy sales, not because of the average electricity price. As for the simulation results of the total power sales revenue, those of the lighting and industrial customers are both gradually increasing between the years 1999 to 2002. However, the increasing pace of the industrial customer tended to slow down. Moreover, both types of the customers possess a similar trend that their total power sales are higher in statistical meaning for the months from July to October, and lower for February, for those above three years. Besides, among the variables affecting each type of customer’s power sales revenue, the energy sales is the most significant one, its parameter implies that whenever the total energy sales increases one unit, i.e. one kwh, it would affect the total power sales revenue by that amount equivalent to the figure of the parameter. According to the empirical results, the estimated parameter mentioned-above of the lighting customer is 2.69, and 1.35 of the industrial customer respectively. That implies one kwh unit price for the lighting customer is 2.69 N.T. dollars, and 1.35 N.T. dollars for the industrial customer. Moreover, from the elasticity of the total energy sales versus the total power sales revenue, it shows that the elasticity of the lighting customer is around 1.2, and the elasticity of the industrial customer is around 0.7. The underlining reason of the difference between the two figures could be that the electricity pricing structure of the lighting and industrial customers are quite different. The industrial customer is charged by two-part tariff including a demand charge for the capacity use and an energy charge for the kwh use. While the lighting customer is charged simply by a single rate, i.e. the energy use. Finally, the empirical results also show that the magnitude of the peak load and the load factor of the whole electricity system also affect the total power sales revenue of each type of the customer, though with much less effect.

本研究的目的為研究台灣省在1990年之15-17歲的在學率，在找出可能影響因素並蒐集好相關的資料後，我們將蒐集到的資料分成兩個部份，一個部份用來建造模型，而另一個部份則用來測試所建立出來的模型。主要的過程是：先利用簡單迴歸模型了解各個可能的因素對於15-17歲的在學率的影響程度，經過許多分析及了解後再對這些變數採取可能的變數轉換(variable transformations)，而後再利用三種常用的統計迴歸方法﹝包含有逐步迴歸(stepwise regression)方法、前進選擇(forward selection)方法以及後退消除(backward elimination)方法﹞去發展出一個適當的複迴歸模型(multiple regression model)。對於這個模型，以實際的台灣在學情況來看，我們看不出它有任何的不合理；同時也利用圖形及檢定去驗證模型的假設，其次還做有關迴歸參數的推論(inferences about regression parameters)。再其次，我們運用變異數分析的結果(analysis of variance results)以及新觀察值的預測情形(predictions of new observations)來評估模型的預測能力。最後並利用所得到的最適當的模型，對如何提昇15-17歲青少年的在學率給予適當的建議。 / The objective of this research is to study what factors may affect the schooling rates of 15-17 years old in Taiwan province in 1990. After finding out some possible factors and collecting those data regarding those factors, we separate the data (by stratified random sampling) into two sets. One set is used to construct the model, and the other set shall be used to test the model. The main process to build a regression model is as follows. First, we shall use simple linear regression models to help us to see if each factor may have relation with the schooling rates. With the analysis of residuals and so on, we then make appropriate transformations on each of these factors. Finally, we use three common statistical regression techniques (including stepwise regression, forward selection, and backward elimination methods) to develop a suitable multiple regression model. It seems that, by our understanding of schooling rates in Taiwan, this model is not unreasonable. In addition, we verify the assumptions of the model by graphical methods and statistical tests. We also do the inferences about regression parameters. Furthermore, ye use the results of the analysis of variance and predictions of new observations to evaluate the prediction ability of the model. Finally, we use the most appropriate multiple regression model to give some suggestions to improve (or keep) the schooling rates of 15-17 years old.

目前，中國大陸最熱門的兩大經濟區域為長江三角洲與珠江三角洲，此兩地吸引眾多外商至當地投資。究竟此兩區域具有何區位優勢能吸引眾多外資，以及此兩地區的優勢有何差異，成為本論文重心所在。本論文主要探討三個主題： 一﹑這兩區域自改革開放以來，區位優勢的消長如何，才造成今日長江三角洲吸引外商投資金額超越珠江三角洲。 二﹑這兩區域有何區位優勢，才能吸引眾多外商至當地投資。 三﹑這兩區域與中國大陸整體平均水準的區位優勢作比較，究竟這兩區域相較於中國大陸整體平均水準具有何優勢，使外商特別關注此兩大地區。 以上問題運用中國統計年鑑的數據資料，以複回歸模型分析各地區的外商投資數據，以得出各項主題的結果。 / Yangtze River Delta and Pearl River Delta are both the most famous economic areas in Mainland China nowadays, attracting many foreign capitals to invest. The purpose of this thesis is to investigate what are the advantages in these two areas that attract foreign direct investment, and what are the differences between them. Three subjects are discussed in the present study. First, how did the location advantages rise and fall between Yangtze River Delta and Pearl River Delta, thus now there are more foreign investment enterprises located in Yangtze River Delta than in Pearl River Delta since the beginning of China’s ‘Open Door’ Policy. Second, what are the location advantages in these two areas that attract so many foreign investment enterprises to locate in these two areas. Third, what are the unique location advantages in these two areas compared with the average level of Mainland China, so that many foreign investment enterprises pay especially high attention to this two areas. Data of Statistical Yearbook of China are used in the present study, and multiple regression model is adopted to analyze the data in order to obtain the results.

自行車一詞儼然成為綠色環保的代名詞之一，台灣自行車業過去在國際間，被認定為品質粗糙的產品，在經過多年努力的情況下，台灣自行車業不斷的備受肯定，隨著近年全球暖化議題、全球性健康概念、油價飆漲、金融海嘯爆發等，諸多原因造成自行車從不被看好的代步工具，演變到現在成為休閒運動工具的轉變，其中；台灣自行車2008年的金融海嘯中，相較於其他傳統產業，不論是出口產值或是股價不降反漲，大舉逆勢成長，其中巨大(Giant)、美利達(Merida)、愛地雅(Ideal)，成車製造商，近年來分別占出口前三大。 所以本研究要探討，金融海嘯爆發的前後，對台灣自行車業帶來的影響，研究資料選定為2000年1月至2013年12月間的巨大股價(9921)、美利達股價(9914)、愛地雅股價(8933)、台灣股價加權指數(TWII)、原油價格、工業生產指數的月資料，共168筆。透過單根檢定檢測資料是否為定態，利用共整合檢定確定是否含有至少一組解，搭配向量誤差修正模型檢測變數間的長短其關係，在利用複迴歸模型檢測。 研究結果顯示，巨大、美利達、愛地雅和台灣加權股價指數具有顯著關係，由於台灣自行車屬於出口導向以及中高價位產品，故全球景氣對台灣自行車業深具影響。其中，巨大和美利達除了ODM外，亦有自有品牌在全球銷售，愛地雅定位專業ODM專業代工廠，前者發展不同市場。 / The word "bicycle" has become one of the pronouns of environmental protection. In the past, Taiwan bicycling industry was treated as low-quality products internationally. With long-time effort, Taiwan bicycling industry was highly appreciated. Recently, global warming issue, cosmopolitan health sense, dramatically increased oil price, the eruption of financial crisis, and many reasons lead the bicycles have not positively evaluated as means of transportation. Now, it becomes the outdoor recreation mean. Comparing Taiwan bicycling industry with other traditional industry, it doesn't fall down but highly increase no matter export value or stock price. The manufacturer of Giant, Merida, and Ideal are the top 3 of export recently. So this study want to explore the things happened before and after the outbreak of the financial crisis that affects bicycle industry in Taiwan, research data for selected between January 2000 and December 2013, relationship between the Giant(9921) shares, Merida (9914) shares, Ideal(8933) shares, TWII, the price of crude oil, industrial production index. Through the Unit Root Test to test whether the data is the steady state or not. By using cointegration test to make sure whether contains at least one group of solutions and vector error correction model to detect the length of the relationship between variables, and using the multiple regression model to test. Results of the research shows that Giant, Merida, Ideal has significant relationship with TWII, because Taiwan bicycle are export-oriented and high price products, so the global boom has profound influence to Taiwan bicycle industry, among them, the Giant and Merida except the ODM, have their own brands in global sales, Ideal professional locate, ODM professional contract, the former develops different markets. / 摘要 I Abstract II 謝辭 III 目錄 IV 圖目錄 VI 表目錄 VII 第一章　緒論 1 第一節　研究動機 1 第二節　研究目的 3 第三節　巨大機械工業股份有限公司簡介 4 第四節　美利達工業股份有限公司簡介 5 第五節　愛地雅工業股份有限公司簡介 6 第六節　研究架構 7 第二章　文獻回顧 9 第一節　國內相關文獻 9 第二節　國外相關文獻 11 第三節　國內外文獻一覽表 12 第三章　研究方法 20 第一節　單根檢定 20 第二節　共整合檢定 22 第三節　向量誤差修正模型(VECM) 24 第四節　迴歸分析 24 第四章　實證分析 26 第一節　資料來源與處理 26 第二節　敘述統計 31 第三節　單根檢定 32 第四節　共整合檢定 33 第五節　向量誤差修正模型(VECM) 33 第六節　複迴歸模型 35 第五章　結果分析與建議 38 第一節　結果分析 38 第二節　建議 39 參考文獻 40 附錄一　巨大工業股份有限公司沿革 43 附錄二　美利達股份有限公司沿革 47 附錄三　愛地雅股份有限公司沿革 57 圖目錄 圖1-6 研究架構 8 圖4-1-1 台灣自行車業總出口產值(百萬元，美金) 27 圖4-1-2 台灣股價大盤指數(TWII，當日收盤價) 27 圖4-1-3 巨大股價(9921，當日收盤價) 28 圖4-1-4 美利達股價(9914，當日收盤價) 28 圖4-1-5 愛地雅股價(8933，當日收盤價) 29 圖4-1-6 國際原油價格(西德州，美元) 29 圖4-1-7 台灣工業生產指數 30 表目錄 表1-3 巨大公司基本資料 4 表1-4 美利達公司基本資料 5 表1-5 愛地雅公司基本資料 6 表2-3 國內外相關文獻整理 12 表4-1 資料來源一覽表 26 表4-3-1 ADF 單根檢定 32 表4-3-2 單根檢定-一階差分 32 表4-4-1 共整合檢定 33 表4-5-1 Giant & Merida　向量誤差修正模型 34 表4-5-2 Giant & Ideal　向量誤差修正模型 34 表4-5-3 Merida & Ideal　向量誤差修正模型 34 表4-6-1 自行車產業與景氣循環對巨大股價之影響 37 表4-6-2 自行車產業與景氣循環對美利達股價之影響 37 表4-6-3 自行車產業與景氣循環對愛地雅股價之影響 37