T Test

To effectively evaluate medical literature, practicing physicians and medical researchers must understand the impact of statistical tests on research outcomes. Applying inefficient statistics not only increases the need for resources, but more importantly increases the probability of committing a Type I or Type II error. The t-test is one of the most prevalent tests used in the medical field and is the uniformally most powerful unbiased test (UMPU) under normal curve theory. But does it maintain its UMPU properties when assumptions of normality are violated? A Monte Carlo investigation evaluates the comparative power of the independent samples t-test and its nonparametric counterpart, the Wilcoxon Rank-Sum (WRS) test, to violations from population normality, using three commonly occurring distributions and small sample sizes. The t-test was more powerful under relatively symmetric distributions, although the magnitude of the differences was moderate. Under distributions with extreme skews, the WRS held large power advantages. When distributions consist of heavier tails or extreme skews, the WRS should be the test of choice. In turn, when population characteristics are unknown, the WRS is recommended, based on the magnitude of these power differences in extreme skews, and the modest variation in symmetric distributions. J CLIN EPIDEMIOL 52;3:229-235, 1999.

It is a conceptual paper that takes at in-depth look at the origins, pros and cons of Six Sigma and describes how it relates to some of the other quality initiatives in industry. DMAIC strategy of Six Sigma is briefly explained and realised the significance of analysis phase, specifically. Various prospects have been formulated to understand the reason of failure of Six Sigma particularly in SMEs and it has been found that doing 'wrong analysis' is one of the major reasons in them. It highlights the analysis phase and proposed a more applicable form for classification of analytical tools as far as their industrial applications are concerned. A road map for short listing the relevant analytical tool has been carpeted especially for students, people working in industry and professional statisticians. A case study in a medium scale non-ferrous foundry has been conducted which has validated the suggested approach successfully. It is based on utilising an integrated advantage of both quantitative and qualitative tools extensively for producing significant results quickly in real industrial environments.

Burnout is a negative situation that occurs triggered by chronic stress and leading to a reduction in
the individual's cynicism and personal accomplishment. Education, which has significant effects
on all stages of human life, may be exposed to situations that may pose barriers to success such as
burnout. Graduate students studying the problems they experienced were particularly dissertation
period, although in this case they caused the collapse spiritually may affect negatively their
success. Especially as natural sciences, production, intensive research and projects works, are
some of the problems identified which the students face during their research. In this study, the
Maslach Burnout Inventory was employed in analysing the data which was obtained from 241
students in Turkey; frequency analysis, factor analysis and t and reliability test analysis were the
analytical tools used. However; male student’s cynicism, in the course of work the students with
emotional burnout and those using cigarette where found to have more fatigue in their livings.

proposed a method to detect outliers which explicitly imposes the null hypothesis of a unit root. It works in an iterative fashion to select multiple outlier in a given series. We show, via simulations, that, under the null hypothesis of no outliers, it has the right size in finite samples to detect a single outlier but, when applied in an iterative fashion to select multiple outliers, it exhibits severe size distortions towards finding an excessive number of outliers. We show that his iterative method is incorrect and derive the appropriate limiting distribution of the test at each step of the search. Whether corrected or not, we also show that the outliers need to be very large for the method to have any decent power. We propose an alternative method based on first-differenced data that has considerably more power. We also show that our method to identify outliers leads to unit root tests with more accurate finite sample size and robustness to departures from a unit root. The issues are illustrated using two US/Finland real-exchange rate series.

To effectively evaluate medical literature, practicing physicians and medical researchers must understand the impact of statistical tests on research outcomes. Applying inefficient statistics not only increases the need for resources, but more importantly increases the probability of committing a Type I or Type II error. The t-test is one of the most prevalent tests used in the medical field and is the uniformally most powerful unbiased test (UMPU) under normal curve theory. But does it maintain its UMPU properties when assumptions of normality are violated? A Monte Carlo investigation evaluates the comparative power of the independent samples t-test and its nonparametric counterpart, the Wilcoxon Rank-Sum (WRS) test, to violations from population normality, using three commonly occurring distributions and small sample sizes. The t-test was more powerful under relatively symmetric distributions, although the magnitude of the differences was moderate. Under distributions with extreme skews, the WRS held large power advantages. When distributions consist of heavier tails or extreme skews, the WRS should be the test of choice. In turn, when population characteristics are unknown, the WRS is recommended, based on the magnitude of these power differences in extreme skews, and the modest variation in symmetric distributions. J CLIN EPIDEMIOL 52;3:229-235, 1999.

Employee productivity, organizational citizenship, employee loyalty, efficiency etc. totally depend on job satisfaction. Job satisfaction is an important indicator of how employees adjust about their jobs. The purpose of this study is to fit some mathematical models to percentages of employees' job satisfaction (PEJS) for men, women and both sexes. For this, the secondary data are used. Quasi Newton Method is employed to fit these mathematical models using the software STATISTICA. Moreover, t-test, F-test and cross validity prediction power (CVPP) are used to check the accuracy as well as validation of the model. In this study, it is seen that these PEJS for men, women and both sexes are showing U-shaped pattern in accordance with age. It is also found that PEJS is increasing with age after age 30 years. And it is seen that the PEJS for women is comparatively higher than that of men at every age group. Furthermore, it is found that PEJS for men, women and both sexes follow second degree polynomial models. These models are well fitted in accordance with t-test, F-test and CVPP. The stabilities of these models are more than 96.9%.

The main purpose of the study is to identify the symptoms of stress, strategies for coping with stress, and absenteeism among Slovenian middle management. The survey covered 211 middle-level managers employed in large and medium-sized enterprises in Slovenia. The data was collected by using two questionnaires. The first questionnaire was used in order to determine stress coping strategies (questionnaire of coping with stress CRI). The second questionnaire was used to identify stress loads and symptoms of stress among managers. Statistical analysis was carried out using SPSS 17.0, which showed that managers who use problem-focused coping strategies are significantly less absent from work and experience lower levels of stress than managers who use emotion-focused stress coping strategies. Based on the results of the analysis, suggestions are outlined for more problem-focused approaches of stress coping strategies in Slovenian large and medium-sized enterprises.

The growing size and number of the medical images necessitated the use of computers to facilitate processing and analysis. In medical world, diagnostic imaging is an invaluable and important tool for early detection of diseases. In this project, we propose a novel classification method in low dose computed tomography scans. In the proposed system, first, the images are preprocessed and features are extracted. Second, for feature selection, t test method is applied. This selects the significant features. These features were optimized and the resultant set was used for classification of lung nodules into four categories: juxtapleural, well-circumscribed, vascularized and pleural-tail, based on the extracted information. Finally, Decision trees classifier are utilized for classification and overall detection performance is evaluated. The system has been tested with a number of real Computed Tomography lung images and has achieved satisfactory results in classifying the lung diseases. The results show that the proposed method achieves higher classification performance than traditional methods.

The processes of KM studied here are Knowledge Capturing, Organizing, Refining and Transferring. KM has become an important to the organization, as it combines both the tacit and explicit knowledge from the expertise in that particular organization. Among academicians, knowledge sharing is crucial, and the KM processes should be practiced to ensure the knowledge sharing take place. The study was conducted at FIIT and academicians at FIIT are used as population. There are two groups of academicians responded to the questionnaires: academicians who served in the year 2006; and academicians who served in 2008. Statistical analysis was conducted to the data gathered and collected from the questionnaires. Using the t-Test, the study had concluded that the processes of KM do exist at FIIT; and the study can significantly prove that all academicians do practice the processes of KM.

The main purpose of the study is to identify the symptoms of stress, strategies for coping with stress, and absenteeism among Slovenian middle management. The survey covered 211 middle-level managers employed in large and medium-sized enterprises in Slovenia. The data was collected by using two questionnaires. The first questionnaire was used in order to determine stress coping strategies (questionnaire of coping with stress CRI). The second questionnaire was used to identify stress loads and symptoms of stress among managers. Statistical analysis was carried out using SPSS 17.0, which showed that managers who use problem-focused coping strategies are significantly less absent from work and experience lower levels of stress than managers who use emotion-focused stress coping strategies. Based on the results of the analysis, suggestions are outlined for more problem-focused approaches of stress coping strategies in Slovenian large and medium-sized enterprises.

Motivation can be changed significantly in dependence on meeting human needs, life situations, internal and external environment, etc. It is caused by different factors which affect motivation in different ways. These factors do not act separately but they are a part of mutually connected network of specific relations. In the paper we show the possibility of the impact of age, education and seniority on the motivation of employees. The level of employee motivation and employee performance can be influenced by means of their detailed knowledge.

E-learning is now emerging as the advance paradigm for higher education. The present study was conducted for measuring the attitude of university students towards e-learning in West Bengal by taking 308 University level students from four Universities namely Sidho-Kanho-Birsha University, Jadavpur University, Visva-Bharati and Gourbanga University. The survey method has been adopted for the present study and stratified random sampling technique has been used in selecting the samples. A well-designed questionnaire, developed by the Investigators has used to collect primary data. The result revealed that students' have high attitude towards elearning and their attitude scores did not differ significantly with their personal variables such as, gender, stream of study and residence.

This paper investigates, both in finite samples and asymptotically, statistical inference on predictive regressions where time series are generated by present value models of asset prices. We show that regression-based tests, including robust tests such as Jansson and Moreira's conditional test and Campbell and Yogo's Q-test, are inconsistent and thus suffer from lack of power in local-to-unity models for the regressor persistence. The main reason is that the near-integrated regressor from the present value model slows down the convergence rates of the estimates, an effect which is masked in predictive regressions analysis with exogenous constant covariance of innovations. We illustrate these properties in a simulation study and analyze the predictability of several stock returns series. JEL Classification: C12, C22, G1. We are grateful to the Co-Editor and two anonymous referees for helpful comments and suggestions. We also thank Enrique Sentana for helpful suggestions.

This paper reports on the influence of waiving the GMAT requirement on academic performance as measured by grade-point-average at graduation for 833 University of Hartford MBA students who graduated between 2003 and 2009. In seeking to better understand what factors might be influencing graduation GPA, we examined a variety of traditional attributes. In addition, we examined the potential influence of GMAT Waivers on graduation GPA because there was some thought that students who waived this test might have lower graduation GPAs than those who took the examination. The results of this study indicated that the most important factor for determining MBA graduation GPA was an individual’s undergraduate GPA, with higher undergraduate GPAs being associated with higher MBA graduation GPAs. Marginally significant differences in graduation GPA were also found based on the number of credits waived at entry into the MBA program, with more credits being waived contributing to a higher graduatio...

Recent research has shown that it is possible to find genes involved in the pathogenesis of a particular condition on the basis of microarray experiments. Genes which are differentially expressed, for example between healthy and diseased tissues, are likely to be relevant to the disease under study. Some of the properties of microarray datasets make the task of finding these genes a challenging one. This paper proposes a gene-ranking algorithm whose main novelty is the use of bootstrapped P-values. We present an analysis of the algorithm, showing how it takes account of small-sample variability in observed values of the test statistic, in a way conventional statistical tests cannot. Experimental results show that our algorithm outperforms the widely-used twosample t-test on challenging artificial data. Gene ranking is then performed on two well-known microarray datasets, with encouraging results. For example, a number of genes from one of the datasets, whose differential expression was subsequently confirmed by a more reliable biochemical analysis, are found to be ranked higher by the bootstrapped algorithm than by the conventional t-test, suggesting that the proposed algorithm may be better able to exploit the limited data available to infer biologically useful information.

Classification of yeast genes based on their expression levels obtained from micro array hybridization experiments is an important and challenging application domain in data mining and knowledge discovery. Over the past decade, neural networks and support vector machines (SVMs) have achieved good results for genes classification. This paper presents a methodology which uses two neural networks to classify unseen genes based on their expression levels. In order to remove some of the noise and deal with the imbalanced class distribution of the dataset, data pre-processing is firstly performed before data classification in which data cleaning, data transformation and data over-sampling using SMOTE algorithm are undertaken. Thereafter, two neural networks with different architectures are trained using Scaled Conjugate Gradient in two different ways: 1) the training-validation-testing approach and 2) 10-fold crossvalidation. Experimental results show that this methodology outperforms the previous best-performing SVM for this problem and 8 other classifiers: 3 SVMs, C4.5, Bayesian network, Naive Bayes, K-NN and JRip.

Microarray data analysis typically consists in identifying a list of differentially 1 expressed genes (DEG), i.e., the genes that are differentially expressed between two 2 experimental conditions. Variance shrinkage methods have been considered a better choice 3 than the standard t-test for selecting the DEG because they correct the dependence of the 4 error with the expression level. This dependence is mainly caused by errors in background 5 correction which affects more severely genes with low expression values. Here, we propose 6 a new method for identifying the DEG that overcome this issue and does not require 7 background correction or variance shrinkage. Unlike current methods, our methodology is 8 easy to understand and to implement. It consists of applying the standard t-test directly on the 9 normalized intensity data, which is possible because the probe intensity is linear dependent 10 with the gene expression level and because the t-test is scale-and location-invariant. This 11 methodology considerably improves the sensitivity and robustness of the list of DEG when 12 compared to the t-test applied to preprocessed data and to the most widely used shrinkage 13 methods, SAM and LIMMA. Our approach is useful especially when the genes of interest 14 have small differences in expression and therefore get ignored by standard variance shrinkage 15 methods.

Passive samplers with polyurethane disks (PUF) were applied in the determination of the concentration of polycyclic aromatic hydrocarbons (PAHs) in ambient air in six residential areas in the Philippines during four simultaneous sampling periods. The uptake profiles of PAHs were determined at one site during one sampling period. Most of the PAHs that were detected in air at concentrations that were significantly higher than their analytical detection limits exhibited a linear uptake trend on the PUF disk. The linear uptake profiles of some high molecular weight (HMW) PAHs were not established and this is attributed to the low concentration of the compounds in air in the gaseous phase. The retention concentrations of phenanthrene-d-10 were determined after depuration in four sampling sites during two sampling periods. The sampling rate for phenanthrene-d-10 was calculated at the linear phase of the uptake using the k A derived from depuration experiments and the relationship of k A and sampling rate which was established in a previous passive sampling study. The average sampling rate obtained for phenanthrene d-10 (2.9470.69 m 3 d À1 ) was applied for derivation of the concentrations of the PAHs in the field samples.

This contribution deals with the construction of environmental indicators, serving investors for the assessment of complex performance of companies in accordance with social and corporate governance indicators, the so-called ESG performance indicators. The research project "Construction of Methods for Multifactor Assessment of Company Complex Performance in Selected Sectors", solved by the team of authors has been introduced. In recent years, investment managers have preferred the importance of ESG indicators showing long-term sustainable performance of those companies in which they have invested their financial resources. The objective of presented contribution is the proposal of environmental indicators, i.e. key ESG performance indicators. Environmental indicators are used not only for decision-making, but also for determination of sustainable value. A number of successive steps have been taken to construct environmental indicators and a combination of various statistical methods has been employed. An empirical analysis of environmental performance indicators for the companies operating in the processing industry according to CZ-NACE has been carried out on the basis of the analysis of sources, suggested by international organisations and voluntary environmental instruments. Environmental performance indicators, facilitating investors to decide on their investment activities and forming the part of key ESG-performance indicators, represent expected results of the empirical research.

The aim of this study was to studying in the level of delivery satisfaction (DS), the effect of personal factors and service quality (SQ) on delivery satisfaction in online shopping with the empirical data. The quantitative research was used in this study by survey research that was used collect sample data from 92 Ramkhamhaeng University lecturers. The data analyses were conducted by t-Test, One Way ANOVA and Multiple Regression Analysis (MRA). The results showed that Ramkhamhaeng University lecturers have highest satisfaction. And most of personal factors and service quality factors have effect on delivery satisfaction. Moreover, the results found that the 3 factors of service quality such as tangibles factor, assurance factor and empathy factor have effect on satisfaction (Sig.=0.05) except for the reliability factor and responsiveness factor have not effect on delivery satisfaction.

The aim of this study was to identify the opinions of administrators and teachers about the knowledge management process in primary, secondary and high schools. The population of the study was teachers who work in primary, secondary and high schools in Kocaeli, Izmit. The sample of the study was 270 teachers that were randomly selected. The Knowledge Management Process in Schools Scale that was developed by the researcher was used to collect data. The reliability rate of the scale is 0.96 Cronbach Alpha. The Knowledge Management Process in Schools Scale's Descriptive Factor Analysis results revealed that the scale was comprised of six factors. These factors were: obtaining knowledge; using knowledge; learning knowledge; sharing knowledge; evaluating knowledge; and administrative support. The results of the study revealed that administrative support played an important role in the knowledge management process, and the participants' opinions varied according to their years in service, age and gender.

This paper focused on the Knowledge Management (KM) processes at the Faculty of Industrial Information Technology (FIIT), Universiti Industri Selangor (UNISEL). The processes of KM studied here are Knowledge Capturing, Organizing, Refining and Transferring. KM has become an important to the organization, as it combines both the tacit and explicit knowledge from the expertise in that particular organization. Among academicians, knowledge sharing is crucial, and the KM processes should be practiced to ensure the knowledge sharing take place. The study was conducted at FIIT and academicians at FIIT are used as population. There are two groups of academicians responded to the questionnaires: academicians who served in the year 2006; and academicians who served in 2008. Statistical analysis was conducted to the data gathered and collected from the questionnaires. Using the t-Test, the study had concluded that the processes of KM do exist at FIIT; and the study can significantly prove tha...

In the context of a general regression model in which some regression coefficients are of interest and others are purely nuisance parameters, we derive the density function of a maximal invariant statistic with the aim of testing for the inclusion of regressors (either linear or non-linear) in linear or semi-linear models. This allows the construction of the locally best invariant test, which in two important cases is equivalent to the one-sided t test for a regression coefficient in an artificial linear regression model. Key words: Invariance; linear regression model; locally best invariant test; non-linear regression model; nuisance parameters; t test. 1 Address for correspondence.

The Mann-Whitney U test, which is also known as the Wilcoxon rank sum test, tests for differences between two groups on a single, ordinal variable with no specific distribution (Mann & Whitney, 1947; Wilcoxon, 1945). In contrast, the independent samples t-test, which is also a test of two groups, requires the single variable to be measured at the interval or ratio level, rather than the ordinal level, and to be normally distributed. We accordingly refer to the Mann-Whitney U test as the nonparametric version of the parametric t-test. Both ...

The aims of the research are to know: 1) differences of  reading comprehension achievement through PQRST, and 2) how big the influences of independent variables to dependent variable. Method used in the research is the non-equivalent control group design. The findings show that there is a significant difference in term of reading comprehension  achievement between experimental and control group. To know the differences of reading comprehension achievement  between experimental and control group, t-test is applied. To know how big the influences of independent variables to the dependent  variable, the two tailed regression analysis is used. The contributions of independent variables, t-test and group, ara significant (0.000), meanwhile the contribution of Gender variable is not significant (0.166) to dependent variable.

Many adaptive tests have been developed in an effort to improve the performance of tests of significance. We will consider a test of significance to be "adaptive" if the test procedure is modified after the data have been collected and examined. They are carefully constructed so that they maintain their level of significance. That is, a properly constructed adaptive test that is designed to maintain a significance level of will have a probability of rejection of the null hypothesis at or near when the null hypothesis is true. Hence, adaptive tests are recommended because their statistical properties are often superior to those of traditional tests.
Theoretical statisticians have all too often accepted a model and considered many statistical inferences using that model without checking its validity. That is, we have accepted a dogma of normal distribution and routinely performed the appropriate statistical inference. Possibly a better way to proceed would be to assume that an appropriate model is to found among a number of models, say, which are suitably placed throughout the spectrum of possible models. Then use the data to select the model which seems most appropriate and with his model and the same data, make the desired inference.

The processes of KM studied here are Knowledge Capturing, Organizing, Refining and Transferring. KM has become an important to the organization, as it combines both the tacit and explicit knowledge from the expertise in that particular organization. Among academicians, knowledge sharing is crucial, and the KM processes should be practiced to ensure the knowledge sharing take place. The study was conducted at FIIT and academicians at FIIT are used as population. There are two groups of academicians responded to the questionnaires: academicians who served in the year 2006; and academicians who served in 2008. Statistical analysis was conducted to the data gathered and collected from the questionnaires. Using the t-Test, the study had concluded that the processes of KM do exist at FIIT; and the study can significantly prove that all academicians do practice the processes of KM.

Recent research has shown that it is possible to find genes involved in the pathogenesis of a particular condition on the basis of microarray experiments. Genes which are differentially expressed, for example between healthy and diseased tissues, are likely to be relevant to the disease under study. Some of the properties of microarray datasets make the task of finding these genes a challenging one. This paper proposes a gene-ranking algorithm whose main novelty is the use of bootstrapped P-values. We present an analysis of the algorithm, showing how it takes account of small-sample variability in observed values of the test statistic, in a way conventional statistical tests cannot. Experimental results show that our algorithm outperforms the widely-used twosample t-test on challenging artificial data. Gene ranking is then performed on two well-known microarray datasets, with encouraging results. For example, a number of genes from one of the datasets, whose differential expression was subsequently confirmed by a more reliable biochemical analysis, are found to be ranked higher by the bootstrapped algorithm than by the conventional t-test, suggesting that the proposed algorithm may be better able to exploit the limited data available to infer biologically useful information.

Pretest is a measurement tool used in evaluating the performance of students on any subject knowledge. The purpose of this study is to investigate whether students can perform better in their studies throughout the learning process in the classroom. In particular, the pre-test and post-test are used as a diagnostic and developmental tool for improving teaching and learning in mathematical courses. The sample of this study consists of First Semester Engineering students at the Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM). The relationship between these tests is also investigated. The pretest and post-test results have revealed that several topics are in need of due attention.

Motivation can be changed significantly in dependence on meeting human needs, life situations, internal and external environment, etc. It is caused by different factors which affect motivation in different ways. These factors do not act separately but they are a part of mutually connected network of specific relations. In the paper we show the possibility of the impact of age, education and seniority on the motivation of employees. The level of employee motivation and employee performance can be influenced by means of their detailed knowledge.

In this paper in which we address the evaluation of measurement process quality, we mainly focus on the evaluation procedure, as far as it is based on the numerical measurement outcomes. We challenge the approach where the "exact" value of the observed quantity is compared to the error interval obtained from the measurements under test and we propose a procedure where reference measurements are used as "gold standard". To this purpose, we designed a specific t-test procedure for this purpose, explained here. We also describe and discuss a numerical simulation experiment demonstrating the behaviour of our procedure.

Basic English Writing (BEW) courses have been part of English learning curricula in many universities. Some research has been conducted to investigate various class designs for English language courses. However, class designs for English writing are not yet abundantly developed to provide more experimental results. The purpose of this study was to experiment with a class design in accordance with a student-orient textbook. It explored the learning effect of beginning writers who participated in an experiment of the student-oriented class design and textbook. Participants included native Chinese seniors, taking Basic English Writing (BEW) classes. They were intermediate level students in terms of English proficiency. The study was conducted with two groups and it lasted 18 weeks. The two groups were evaluated by a final writing test. The final writing test was analyzed with four variables included in text analysis and an untried variable included in grammar. The results indicated tha...

The aim of this research was to analyse the somatotype differences in young Croatian rowers and soccer players. The research was conducted on the sample of 19 soccer players and 14 rowers aged 16 years. The average somatotype value in rowers was 4.64-4.54-2.84±2.11-1.14-1.02 (mezomorph-endomorph), while in soccer players it was 2.43-4.39-3.28±0.96-1.13-1.21 (ectomorph-mezomorph). The independent samples T-test showed that the endomorph component was significantly more expressed in rowers than in soccer players. Eight somatotype categories for rowers and six for soccer players were created based on the category diagram. The highest percentage of rowers belonged to the mesomorphic endomorph category (29%), while in soccer players the most represented was the ectomorphic mezomorph (28%) category. 50 % of rowers belonged to the somatotype categories dominated by the endomorphic component, while 62% of soccer players belonged to the somatotype categories dominated by the mesomorphic component.

The trends and periodicities in the annual and seasonal temperature time series at fifteen weather stations within Ontario Great Lakes Basins have been analyzed, for the period 1941-2005, using the statistical analyses (Fourier series analysis, t-test, and Mann-Kendall test). The stations were spatially divided into three regions: northwest (NW), southwest (SW), and southeast (SE) to evaluate spatial variability in temperature. The results of the study reveal that the annual maximum mean temperature showed increasing trend for NW, and mixed trends for SW and SE regions. The variability was found to be more for northern stations as compared to southern stations for annual extreme minimum temperature. In addition, the trend slope per 100 years for the average annual extreme minimum temperature increased within the range of −0.8˚C (Stratford) to 15˚C (Porcupine). The seasonal analysis demonstrated that extreme maximum temperature has an increasing trend and maximum mean temperature has a decreasing trend during summer and winter. The extreme minimum temperature for winter illustrated an increasing trend (90%) with 22% statistically significant for NW region. For the SW region, the trend is also increasing (80%) for most of the temperature variables and 25% of temperature data were significantly increased in the SW region. The SE region stations showed overall very clear increasing trends (95%) for all the temperature variables. The data also showed that 47% of data were statistically significant in the SE region. The analysis of variance accounted for by trend, significant periodicities, and random component show that the pattern is similar for the percent of variance accounted for periodicities, and random component contribute dominantly for the four temperature variables and frost free days (FFD) for all three regions. Overall, the study reveals that the extreme minimum temperature is increasing annually and seasonally, with statistically significant at many stations.