Regression Residual Analysis Research Articles

Spatio-temporal Variation in NEP in Ecological Zoning on the Loess Plateau and Its Driving Factors from 2000 to 2021

Net ecosystem productivity (NEP) is an important index for the quantitative evaluation of carbon sources and sinks in terrestrial ecosystems. Based on MOD17A3 and meteorological data, the vegetation NEP was estimated from 2000 to 2021 in the Loess Plateau (LP) and its six ecological subregions of the LP (loess sorghum gully subregions:A1, A2; loess hilly and gully subregions:B1, B2; sandy land and agricultural irrigation subregion:C; and earth-rock mountain and river valley plain subregion:D). Combined with the terrain, remote sensing, and human activity data, Theil-Sen Median trend analysis, correlation analysis, multiple regression residual analysis, and geographic detector were used, respectively, to explore the spatio-temporal characteristics of NEP and its response mechanism to climate, terrain, and human activity. The results showed that:① On the temporal scale, from 2000 to 2021 the annual mean NEP of the LP region (in terms of C) was 104.62 g·(m2·a)-1. The annual mean NEP for both the whole LP and each of the ecological subregions showed a significant increase trend, and the NEP of the LP increased by 6.10 g·(m2·a)-1 during the study period. The highest growth rate of the NEP was 9.04 g·(m2·a)-1, occurring in the A2 subregion of the loess sorghum gully subregions. The subregion C had the lowest growth rate of 2.74 g·(m2·a)-1. Except for the C subregion, all other ecological subregions (A1, A2, B1, B2, and D) were carbon sinks. ② On the spatial scale, the spatial distribution of annual NEP on the LP was significantly different, with the higher NEP distribution in the southeast of the LP and the lower in the northwest of the LP. The high carbon sink area was mainly distributed in the southern part of the loess sorghum gully subregions, and the carbon source area was mainly distributed in the northern part of the loess sorghum gully subregions and most of the C subregion. The high growth rate was mainly distributed in the central and the southern part of the A2 subregion and the southwest part of the B2 subregion. ③ Human activities had the greatest influence on the temporal variation in NEP in the LP and all the ecological subregions, with the correlation coefficient between human activity data and NEP being above 0.80, and the relative contribution rates of human factors was greater than 50%. The spatial distribution was greatly affected by meteorological factors, among which the precipitation and solar radiation were the main factors affecting the spatial changes in the NEP of the LP. The temporal and spatial variations in the NEP in the LP were influenced by natural and human social factors. To some extent, these results can provide a reference for the terrestrial ecosystem in the LP to reduce emissions and increase sinks and to achieve the goal of double carbon.

Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique

AbstractExploring the effect of climate change and human activities on vegetation is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long‐term vegetation GIMMS Normalized Difference Vegetation Index (NDVI) data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detailed human activities with some innovations. The results show that (1) NDVI shows an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PI: 1982–2000) than after it (PII: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate‐driven promotion had the strongest effect, followed by human‐driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis shows that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PI and economization during PII.

Characteristics and Drivers of Vegetation Change in Xinjiang, 2000–2020

Examining the features of vegetation change and analyzing its driving forces across an extensive time series in Xinjiang are pivotal for the ecological environment. This research can offer a crucial point of reference for regional ecological conservation endeavors. We calculated the fractional vegetation cover (FVC) using MOD13Q1 data accessed through the Google Earth Engine (GEE) platform. To discern the characteristics of vegetation changes and forecast future trends, we employed time series analysis, coefficient of variation, and the Hurst exponent. The correlation between climate factors and FVC was investigated through correlation analysis. Simultaneously, to determine the relative impact of meteorological change and anthropogenic actions on FVC, we utilized multiple regression residual analysis. Furthermore, adhering to China’s ecological functional zone classification, Xinjiang was segmented into five ecological zones: R1 Altai Mountains-Junggar West Mountain Forest and Grassland Ecoregion, R2 Junggar Basin Desert Ecoregion, R3 Tianshan Mountains Mountain Forest and Grassland Ecoregion, R4 Tarim Basin-Eastern Frontier Desert Ecoregion, and R5 Pamir-Kunlun Mountains-Altan Mountains Alpine Desert and Grassland Ecoregion. A comparative analysis of these five regions was subsequently conducted. The results showed the following: (1) During the first two decades of the 21st century, the overall FVC in Xinjiang primarily exhibited a trend of growth, exhibiting a rate of increase of 4 × 10−4 y−1. The multi-year average FVC was 0.223. The mean value of the multi-year FVC was 0.223, and the mean values of different ecological zones showed the following order: R1 > R3 > R2 > R5 > R4. (2) The predominant spatial pattern of FVC across Xinjiang’s landscape is characterized by higher coverage in the northwest and lower in the southeast. In this region, 66.63% of the terrain exhibits deteriorating vegetation, while 11% of the region exhibits a notable rise in plant growth. Future changes in FVC will be dominated by a decreasing trend. Regarding the coefficient of variation outcomes, a minor variation, representing 42.12% of the total, is noticeable; the mean coefficient of variation stands at 0.2786. The stability across varied ecological zones follows the order: R1 > R3 > R2 > R4 > R5. (3) Factors that have a facilitating effect on vegetation FVC included relative humidity, daylight hours, and precipitation, with relative humidity having a greater influence, while factors that have a hindering effect on vegetation FVC included air temperature and wind speed, with wind speed having a greater influence. (4) Vegetation alterations are primarily influenced by climate change, while human activities play a secondary role, contributing 56.93% and 43.07%, respectively. This research underscores the necessity for continued surveillance of vegetation dynamics and the enhancement of policies focused on habitat renewal and the safeguarding of vegetation in Xinjiang.

Spatial and Temporal Variation Characteristics of Ecological Environment Quality in China from 2002 to 2019 and Influencing Factors

Since the beginning of the new century, there has been a notable enhancement in China’s ecological environment quality (EEQ), a development occurring in tandem with climate change and the extensive ecological restoration projects (ERPs) undertaken in the country. However, comprehensive insights into the spatial and temporal characteristics of China’s EEQ, and its responses to both climate change and human activities over the past two decades, have remained largely elusive. In this study, we harnessed a combination of multi-source remote-sensing data and reanalysis data. We employed Theil–Sen median trend analysis, multivariate regression residual analysis, and the Hurst index to examine the impacts and changing patterns of climatic factors and human activities on China’s EEQ during the past two decades. Furthermore, we endeavored to forecast the future trajectory of EEQ. Our findings underscore a significant improvement in EEQ across most regions of China between 2002 and 2019, with the most pronounced enhancements observed in the Loess Plateau, Northeast China, and South China. This transformation can be attributed to the combined influence of climatic factors and human activities, which jointly accounted for alterations in EEQ across 78.25% of China’s geographical expanse. Human activities (HA) contributed 3.93% to these changes, while climatic factors (CC) contributed 17.79%. Additionally, our projections indicate that EEQ is poised to continue improving in 56.70% of China’s territory in the foreseeable future. However, the Loess Plateau, Tarim Basin, and Inner Mongolia Plateau are anticipated to experience a declining trend. Consequently, within the context of global climate change, the judicious management of human activities emerges as a critical imperative for maintaining EEQ in China. This study, bridging existing gaps in the literature, furnishes a scientific foundation for comprehending the evolving dynamics of EEQ in China and informs the optimization of management policies in this domain.

Quantitative Analysis of Climate Variability and Human Activities on Vegetation Variations in the Qilian Mountain National Nature Reserve from 1986 to 2021

Rapid climate variability and intense human activities generate obvious impacts on the Qilian Mountains ecosystem. The time series of fractional vegetation coverage (FVC) from 1986 to 2021 were used to quantify the impact of climate variability and human activities on vegetation variations in the Qilian Mountain National Nature Reserve (QMNNR), using 3147 land satellite images based on the Google Earth Engine cloud platform. The contributions of climate variability and human activities to FVC were quantified using multiple regression residual analysis. Partial correlation and correlation methods were used to quantify the impact of temperature, precipitation, and human activity footprints on FVC. The results showed that from 1986 to 2021, the increase rate of FVC was 1.7 × 10−3 y−1, and the high vegetation coverage of the FVC was mainly distributed in the southeastern part of the reserve. In contrast, the low vegetation coverage was mainly distributed in the northwest part of the reserve. The Mann–Kendall mutation test found that the year of 2009 was the year of the mutation. The growth rate of FVC from 2010 to 2021 was greater than that from 1986 to 2009. In addition, climate variability and human activities exhibited a remarkable spatial heterogeneity in FVC changes. Climate variability and human activities contributed 49% and 51% to the increase in FVC in the reserve, respectively, and the contribution of human activities was greater than that of climate variability. The warming and humidification phenomena in the reserve were obvious. However, precipitation was the dominant factor affecting the dynamic changes in FVC. This study improves our understanding of the response of vegetation dynamics to the climate and human activities in the QMNNR.

The Latest Desertification Process and Its Driving Force in Alxa League from 2000 to 2020

Alxa League of Inner Mongolia Autonomous Region is a concentrated desert distribution area in China, and the latest desertification process and its driving mechanism under the comprehensive influence of the extreme dry climate and intense human activities has attracted much attention. Landsat data, including ETM+ images obtained in 2000, TM images obtained in 2010, and OLI images obtained in 2020, were used to extract three periods of desertification land information using the classification and regression tree (CART) decision tree classification method in Alxa League. The spatio-temporal variation characteristics of desertification land were analyzed by combining the transfer matrix and barycenter migration model; the effects of climate change and human activities on regional desertification evolution were separated and recombined using the multiple regression residual analysis method and by considering the influence of non-zonal factors. The results showed that from 2000 to 2020, the overall area of desertification land in Alxa League was reduced, the desertification degree was alleviated, the desertification trend was reversed, and the desertification degree in the northern part of the region was more serious than in the southern part. The barycenter of the slight, moderate, and severe desertification land migrated to the southeast, whereas the serious desertification land’s barycenter migrated to the northwest in the period of 2000–2010; however, all of them hardly moved from 2010 to 2020. The degree of desertification reversal in the south was more significant than in the north. Regional desertification reversal was mainly influenced by the combination of human activities and climate change, and the area accounted for 61.5%; meanwhile, the localized desertification development was mainly affected by human activities and accounted for 76.8%.

Shifts in land-greening hotspots in the Yellow River Eco-Economic Belt during the last four decades and their connections to human activities

The spatial patterns of Normalized Differences Vegetation Index (NDVI) changes and their potential causes in the Yellow River Eco-Economic Belt (YREEB) during the last four decades remain unclear. To clarify this issue, this study first depicts the spatial patterns of the NDVI changes by combining GIMMS3g NDVI data and MODIS NDVI data; second, regression residual analysis, cluster analysis and the generalized additive model (GAM) approach are used to explore the potential causes of the NDVI changes. The results show that (1) the regional mean NDVI exhibited an uptrend with a slope of 0.001 per decade from 1982 to 2019, which consists of two prominent uptrend periods, i.e, 1982–1990 and 2004–2019, with slopes of 0.038 per decade and 0.027 per decade, respectively, as well as one trendless period between them. (2) There has been greening hotspots shift. The first prominent greening trend from 1982 to 1990 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2004 to 2019 mainly occurred in the Loess Plateau. (3) The greening trend and greening hotspots shift were slightly caused by climate change while mainly caused by human activities. The first greening trend was closely related to the winter wheat sowing area, and the second greening trend was related to the afforestation area.

Land-Greening Hotspot Changes in the Yangtze River Economic Belt during the Last Four Decades and Their Connections to Human Activities

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI changes using global inventory modelling and mapping studies (GIMMS) NDVI data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data. Secondly, the Mann–Kendall test, regression residual analysis and cluster analysis are used to diagnose the potential causes of the NDVI changes. The results show that the regional mean NDVI exhibited an uptrend from 1982 to 2019, which consists of two prominent uptrend periods, i.e., 1982–2003 and 2003–2019. There has been a shift of greening hotspots. The first prominent greening trend from 1982 to 2003 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2003 to 2019 mainly occurred at the junction of Chongqing, Guizhou and Yunnan. The greening trend and shift of greening hotspots were slightly caused by climate change, but mainly caused by human activities. The first greening trend was closely related to the agricultural progress, and the second greening trend was associated with the rapid economic development and implementation of ecology restoration policies.

The Kumaraswamy Lindley Regression Model with Application on the Egyptian Stock Exchange

We introduce and study the Kumaraswamy Lindely Distribution (KLD) model, which has increasing, decreasing, upside-down bathtub and bathtub shaped hazard functions.. We perform a Monte Carlo simulation study to assess the finite sample behavior of the maximum likelihood estimates of the parameters. We define a new regression model based on the new distribution. The new regression was applied to data from the Egyptian stock exchange in the period of (2015-2019). Finally, we study some properties of regression Residual analysis The martingale residual, Deviance component residual.

Assessing the spatiotemporal dynamics of ecosystem water use efficiency across China and the response to natural and human activities

In the context of increasing global carbon emissions, and the relative shortage of water resources, research on the relationships between climate change, human activities and the carbon–water coupling response of China's ecosystems is crucial. Water use efficiency (WUE) is an important indicator reflecting the coupling of ecosystem carbon and water cycles. Based on trend analysis, correlation analysis and multiple regression residual analysis, this paper analyzes the spatiotemporal distributions, change trends, correlations with meteorological factors, changes in different vegetation coverage types, and relative contributions of the main driving factors of China's vegetation WUE from 2001 to 2017. The results show the following: (1) For the past 17 years, the multiyear average value of vegetation WUE in China is 1.08gCm- 2mm-1, the average annual growth rate is 0.003gCm- 2mm-1a-1. The area with increasing trend of WUE accounts for 59.09% of the total area. (2) The seasonal WUE is highest in autumn, followed by summer, spring and winter. (3) The areas in which vegetation WUE is positively correlated with temperature and precipitation account for approximately 50.21% and 66.16% of the total area, respectively. (4) In terms of the relative annual average value of the WUE, the different vegetation cover types, in descending order, are mixed forests > needleleaf forests > broadleaf forests > shrublands > grasslands > croplands > wetlands > barren lands. (5) Human activities and climate change contributed 66.75% and 33.25%, respectively, to the increase in vegetation WUE in China in the past 17 years. The research results have important scientific significance for clarifying the impacts of climate change and human activities on the carbon–water cycle of the terrestrial ecosystems of China and assessing regional differences in the potential of terrestrial ecosystems to increase carbon sinks.

ПРИМЕНЕНИЕ РЕГРЕССИОННОГО АНАЛИЗА ДЛЯ ОЦЕНКИ ЭФФЕКТИВНОСТИ РАБОТЫ НЕФТЯНЫХ СКВАЖИН С ПАРАФИНИСТОЙ НЕФТЬЮ

Актуальность исследования обусловлена необходимостью своевременного и быстрого анализа состояния нефтяного фонда скважин, работа которого осложнена парафинистостью. Рост числа показателей работы нефтяной скважины в связи с усложнением условий нефтедобычи приводит к трудностям их интерпретации специалистами геофизических служб и, как результат, – к неверному принятию решений об эффективности работы нефтяных скважин. Существующие модели анализа эффективности работы нефтяных скважин включают либо небольшое количество параметров работы скважины, что делает их недостаточно комплексными, либо избыточное количество показателей, что делает модели сложными и ресурсоемкими. Цель: определить геолого-физические показатели, оказывающие существенное влияние на парафинистость нефти для создания регрессионной модели прогнозирования эффективности работы нефтяной скважины с точки зрения геофизики. Объекты: нефтяные скважины и эффективность их работы с учетом парафинистости нефти. Методы: регрессионный анализ, оценка адекватности модели по t-критерию Стьюдента, коэффициенту детерминации, F-критерию Фишера, проверка несмещенности и эффективности оценок с помощью условий Гаусса–Маркова, оценка автокорреляции остатков с помощью статистики Дарбина–Уотсона, непараметрическая статистика, анализ остатков регрессии. Результаты. Определены форма и вид регрессионной зависимости между парафинистостью нефти, пластовой температурой и вязкостью нефти в пластовых условиях. Доказана адекватность полученной модели. Полученная математическая модель в совокупности с моделью анализа технологической эффективности работы нефтяных скважин может стать основой для систем поддержки принятия решений специалистов геофизических служб с целью комплексного анализа эффективности работы нефтяных скважин.

Estimating Relations of Vegetation, Climate Change, and Human Activity: A Case Study in the 400 mm Annual Precipitation Fluctuation Zone, China

The 400 mm annual precipitation fluctuation zone (75°55′–127°6′E and 26°55′–53°6′N) is located in central and western China, which is a transition area from traditional agricultural to animal husbandry. It is extremely sensitive to climatic changes. The corresponding changes of the ecosystem, represented by vegetation, under the dual influences of climate change and human activities are important issues in the study of the regional ecological environment. Based on the Savitzky–Golay (S–G) filtering method, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Differential Vegetation Index (NDVI) dataset (NDVI3g) was reconstructed in this paper. Sen’s slope estimation, Mann–Kendall (M–K), multiple regression residual analysis, and the Hurst index were used to quantify the impacts of climate change and human activities on vegetation; in addition, the future persistence characteristics of the vegetation changes trend were analyzed. Vegetation changes in the study area had an obvious spatio-temporal heterogeneity. On an annual scale, the vegetation increased considerably, with a growth rate of 0.50%/10a. The multi-year mean value of NDVI and growth rate of cultivated land were the highest, followed by the forest land and grassland. On a seasonal scale, the vegetation cover increased most significantly in autumn, followed by spring and summer. In the southeastern and central parts of the study area, the vegetation cover increased significantly (P < 0.05), while it decreased significantly in the northeastern and southwestern parts. In summer, the NDVI value of all vegetation types (cultivated land, forest land and grassland) reached the maximum. The change rate of NDVI value for cultivated land reached the highest in autumn (1.57%/10a), forest land reached the highest in spring (1.15%/10a), and grassland reached the highest in autumn (0.49%/10a). The NDVI of cultivated land increased in all seasons, while forest land (−0.31%/10a) and grassland (−0.009%/10a) decreased in winter. Partial correlation analysis between vegetation and precipitation, temperature found that the areas with positive correlation accounted for 66.29% and 55.05% of the total area, respectively. Under the influence of climate change alone, 62.79% of the study area showed an increasing tendency, among which 46.79% showed a significant upward trend (P < 0.05). The NDVI decreased in 37.21% of the regions and decreased significantly in 14.88% of the regions (P < 0.05). Under the influence of human activities alone, the vegetation in the study area showed an upward trend in 59.61%, with a significant increase in 41.35% (P < 0.05), a downward trend in 40.39%, and a significant downward trend in 7.95% (P < 0.05). Vegetation growth is highly unstable and prone to drastic changes, depending on the environmental conditions.

Trend Surface Analysis of Hand, Foot and Mouth Disease in Sarawak

In this study we aim to fit a trend surface model for Hand, Foot And Mouth Disease (HFMD) in Sarawak and also check the adequacy of the model obtained. For this, the epidemiological data of year 2012(outbreak year) and 2011 are used. Models were built up to second order polynomial based on the idea that number of HFMD cases can be described by regression residual analysis. With Moran I statistic, residuals from all the trend surface models were examined for spatial auto correlation. All six trend surface models for 2011 data had residuals that were not spatially auto correlated.However, one model for 2012 data showed spatial auto correlation. The best model for each year of the two years that represent disease outbreak and non-outbreak situations was selected based on Mallows’ Cp statistic. These models could be used to predict the number of cases for locations of interest.

The Impact of Levene’s Test of Equality of Variances on Statistical Theory and Practice

In many applications, the underlying scientific question concerns whether the variances of $k$ samples are equal. There are a substantial number of tests for this problem. Many of them rely on the assumption of normality and are not robust to its violation. In 1960 Professor Howard Levene proposed a new approach to this problem by applying the $F$-test to the absolute deviations of the observations from their group means. Levene's approach is powerful and robust to nonnormality and became a very popular tool for checking the homogeneity of variances. This paper reviews the original method proposed by Levene and subsequent robust modifications. A modification of Levene-type tests to increase their power to detect monotonic trends in variances is discussed. This procedure is useful when one is concerned with an alternative of increasing or decreasing variability, for example, increasing volatility of stocks prices or "open or closed gramophones" in regression residual analysis. A major section of the paper is devoted to discussion of various scientific problems where Levene-type tests have been used, for example, economic anthropology, accuracy of medical measurements, volatility of the price of oil, studies of the consistency of jury awards in legal cases and the effect of hurricanes on ecological systems.

Detection of water quality trends at high, median, and low flow in a Catskill Mountain stream, New York, through a new statistical method

The effects of changes in acid deposition rates resulting from the Clean Air Act Amendments of 1990 should first appear in stream waters during rainstorms and snowmelt, when the surface of the watershed is most hydrologically connected to the stream. Early detection of improved stream water quality is possible if trends at high flow could be separately determined. Trends in concentrations of sulfate (SO42−), nitrate (NO3−), calcium plus magnesium (Ca2++Mg2+), and acid‐neutralizing capacity (ANC) in Biscuit Brook, Catskill Mountains, New York, were assessed through segmented regression analysis (SRA). The method uses annual concentration‐to‐discharge relations to predict concentrations for specific discharges, then compares those annual values to determine trends at specific discharge levels. Median‐flow trends using SRA were comparable to those predicted by the seasonal Kendall tau test and a multiple regression residual analysis. All of these methods show that stream water SO42− concentrations have decreased significantly since 1983; Ca2++Mg2+ concentrations have decreased at a steady but slower rate than SO42−; and ANC shows no trend. The new SRA method, however, reveals trends that differ at specified flow levels. ANC has increased, and NO3− concentrations have decreased at high flows, but neither has changed as significantly at low flows. The general downward trend in SO42− flattened at median flow and reversed at high flow between 1997 and 2002. The reversal of the high‐flow SO42− trend is consistent with increases in SO42− concentrations in both precipitation and soil solutions at Biscuit Brook. Separate calculation of high‐flow trends provides resource managers with an early detection system for assessing changes in water quality resulting from changes in acidic deposition.

Residual analysis in random regressions using SAS and S-PLUS

A program package RRAP: Random Regression Residual Analysis Program using SAS [1] and S-PLUS [2] is available for performing random regression residual analysis. The PROCEDURE MIXED from SAS is used for statistical inference. Both elementary-level and individual-level residuals are used. The S-PLUS programs provide: (1) a transformation to orthogonalize the elementary-level correlated residuals for standard regression residual analyses; and (2) several statistics and plots for checking model assumptions, assessing model fitting and detecting outlying individuals. RRRAP starts with a SAS Macro RRRAPMAC on the data followed by a S-PLUS Program DoRRRAP on a UNIX system.

Query 22: Regression Residual Analysis

Query 22: Regression Residual Analysis