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It is pertinent to initiate our study of rural economic status by examining the compositional factors of Man-land ratio and Logistic regression of house ownership. The principal sources of economy of the rural sector are agro product. As per the capability of land, it continuously helps to develop its catchment rural area. The productive land directly provides opportunity through the traditional agriculture practice. Our study is emphasis on Man-land ratio distribution and Logistic regression analysis of two independent variables of four blocks of Purba Medinipur district. Consider two independent variables are number of household and house ownership. Actually, house ownership is one of the alternative setup that may stable due to stable income source. Secondary data have collected authorised source, from Ministry of Rural development, published by Government of West Bengal in 2001 and 2011. The results of man-land ratio indicate that, the considered blocks having with standard level of status due to satisfied level of Man-land ratio value. The population density of the considered area is exhibit at moderate level. Finally, a robust statistical technique like Logistic regression (Hosmer and Lemeshow Test.) was applied over the considered data set with specific level of section and after binary conversion. The result shows that, the expected house ownership and calculated house ownership makes a strong relation between them. B value (slope) is -3.515 and calculated test statistic value satisfied the significant level at 9th and 10th steps sections at 97 % (P =0.03) level of confidence.

the impact of climate change on annual air temperature has received a great deal of attention from clima - tologists worldwide. Many studies have been conducted to illustrate that changes in temperature are becoming evident on a global scale. Air temperature, one of the most important components of climate parameters, has been widely measured as a starting point towards the apprehension of climate change and variability. the main objective of this study is to analyse the temporal variability of mean monthly temperature for the period of 1941 to 2010 (70 years). to detect the magnitude of trend in mean monthly temperature time series, we have used non-parametric test methods such as The Mann-kendall test, often combined with the Theil-Sen's robust estimate of linear trend. Whatever test is used, the user should understand the underlying assumptions of both the technique used to generate the estimates of a trend and the statistical methods used for testing. The results of this analysis reveal that four months - January, February, March and december - indicate a decreasing trend in average temperature, while the remaining eight months have an increasing trend. the magnitude of Mann-Kendall trend statistic Zc for this declining temperature and the magnitude of slope for the months of January, February and december are confirmed at the high significance levels of α = 0.001, 0.01 and 0.1 respectively. Though, the overall trend is positive for monthly as well as seasonally

The long term temperature trends since 1941 to 2010 for Krishnanagar weather observatory, West Bengal, India are analysed. The significant change points in the trend are identified by applying CUSUM chart and Bootstrapping test to the time series of annual mean, annual mean maximum and annual mean minimum temperature time series. Different multi-length segments of the time series with similar trend have been detected by this method at different levels. For every segment, the independent error were adjusted the Mean Standard Error (MSE) with positive correlation and indicating change points at particular confidence level and confidence interval. This principle has been maintained by following bootstrapping technique. The major change point in the annual mean temperatures occurred around 2001 (confidence level 100%). This change point has been confirmed at level-1 (confidence interval 2000, 2001). Prior to the level1 change, the associated average temperature was 27.58◦C; while after the change, the same became 28.94◦C. On the other hand, the annual mean maximum and annual mean minimum temperatures have distinct change points at different levels. There are abrupt changes in the year of 1951, 1964, 1971, 1978, 1984, 1991 for annual mean maximum and 1971, 1978, 1986, 1991, 2000 for annual mean minimum temperature time series respectively. Moreover, the total number of change points, including level-1 change, is 18 for all the three annual mean, annual mean maximum and annual mean minimum temperature time series.

This paper aims to detect the short-term as well as long-term change point in the surface air temperature time series for Asansol weather observation station, West Bengal, India. Temperature data for the period from 1941 to 2010 of the said weather observatory have been collected from Indian Meteorological Department, Kolkata. Variations and trends of annual mean temperature, annual mean maximum temperature and annual minimum temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping were used for the detection of abrupt changes in the time series data set. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperatures occurred around 1986 (0.57°C) at the period of 25 years in the long-term regional scale. On the other side, the annual mean maximum and annual mean minimum temperatures have distinct change points at level 1. There are abrupt changes in the year 1961 (Confidence interval 1961, 1963) for the annual mean maximum and 1994 (Confidence interval 1993, 1996) for the annual mean minimum temperatures at a confidence level of 100% and 98%, respectively. Before the change, the annual mean maximum and annual mean minimum temperatures were 30.90°C and 23.99°C, respectively, while after the change, the temperatures became 33.93°C and 24.84°C, respectively. Over the entire period of consideration (1941-2010), 11 forward and backward changes were found in total. Out of 11, there are 3 changes (1961, 1986 and 2001) in annual mean temperatures, 4 changes (1957, 1961, 1980 and 1994) in annual mean maximum temperatures, and rest 4 changes (1968, 1981, 1994 and 2001) are associated with annual mean minimum temperature data set.

Researches are being carried out world-wide to understand the nature of temperature change during recent past at different geographical scales so that comprehensive inferences can be drawn about recent temperature trend and future climate. Detection of turning points in time series of meteorological parameters puts challenges to the researches. In this work, the temperature time series from 1941 to 2010 for Asansol observatory, West Bengal, India, has been considered to understand the nature, trends and change points in the data set using sequential version of Mann-Kendall test statistic. Literatures suggest that use of this test statistic is the most appropriate for detecting climatic abrupt changes as compared to other statistical tests in use. This method has been employed upon monthly average temperatures recorded over the said 70 years for detection of abrupt changes in the average temperature of each of the months. The approximate potential trend turning points have been calculated separately for each month (January to December). Sequential version of Mann-Kendall test statistic values for the months of July and August is significant at 95% confidence level (p 0.05). The average temperature for most of the other months has shown an increasing trend but more significant rise in July and August temperature has been recognized since 1960s.

This study examines the changes in annual mean (minimum and maximum) temperatures for Kolkata weather observatory during 1941-2010. The analysis focus on the air temperature time series records at Alipore station (Kolkata) of Indian Meteorological Department (IMD). Variation and trend in annual mean temperature, annual mean maximum temperature and annual mean minimum temperature time series were statistically examined using CUSUM chart and bootstrapping techniques to identify the abrupt change in the considered data set. The major change (level 1) in the annual mean temperature occurred around 2001 with a rise of 1.417 °C. The numbers of change points in annual mean maximum and annual mean minimum temperature time series are detected at different levels but not at the level 1. Over the period under consideration (1941-2010), 4 forward and backward abrupt changes (1961, 1963, 1971 and 1991) are noticed in annual mean maximum temperatures, while 6 changes (1961,1964,1967,1974,1985 and 2003) are associated with the annual mean minimum temperature data set.

This research article aims to detect the short term as well as long term significant changes in the surface air temperature time series for Midnapore Weather observation station, West Bengal, India. The temperature time series data has been collected from Indian Meteorological station, Kolkata, for the period from 1941-2010. Fluctuations and trends of annual mean temperature, annual mean maximum temperature and annual mean minimum temperature time series were statistically examined. To identify the abrupt change in trend, the cumulative sum chart (CUSUM) and Bootstrapping were employed on the considered data set. The major change point in the annual mean temperature occurred around 2001 at level 1 (Confidence level 100%). On the other hand, the annual mean maximum temperature and annual mean minimum temperatures have level 1 change points in 2001 and 1969 respectively. The results show that, one can be 100% confident that the annual mean maximum temperature significantly changed between 1998 and 2001. Similarly, annual mean minimum temperature changed between 1963 and 1971 as a confidence level of 98%. Before the change in 2001, annual mean temperature was 27.11°C; while after the level 1 change the temperature becomes 25.1°C. The mean of annual maximum temperature for the period from 1941-2010 has been 34.017°C which reduced to 30.25°C for rest of the period in consideration. For the annual mean minimum temperature, the time series can be divided into two segment taking 1968 as the last point of the first segment for which the average value is 22.38°C, while the second segment, the average value is 18.077°C. The analysis has identified 13 abrupt change points in three temperature time series.