Durbin-Watson Research Articles

Toward an Innovative Strategic empowerment: The role of strategic empowerment on organizational innovation

The study aimed to examine strategic empowerment dimensions and its influence on organizational innovation at private sectors in Developed countries. To measure the current study, the researchers used four strategic empowerment dimensions (empowerment culture, innovation culture, managerial ethics, and managerial transparency). A quantitative technique applied in order to analyze the current study. Sample design refers to the procedure or method the researcher is willing to accept in choosing items for the sample. Research sample was selected using a procedure of random sampling and it was carried out in different banks. A total of 120 questionnaires were distributed, however 109 participants properly filled out the questionnaires. The results show that empowerment culture as strategic empowerment dimension influences significantly and positively organizational innovation at 5% level. The results show that innovation culture influences as strategic empowerment dimension significantly and positively organizational innovation at 5% level. The results show that managerial ethics influences as strategic empowerment dimension significantly and positively organizational innovation at 5% level. The results show that managerial transparency as strategic empowerment dimension influences significantly and positively organizational innovation at 5% level. Moreover, all beta value is higher than .001. All models have very high adjusted R2 (0.693, 0.621, 0.712, and 0.763 respectively) indicating the ability of the models explaining the variation of organizational innovation due to variation of independent variables is very high. The F-value shows that the explanatory variables are jointly statistically significant in the model and the Durbin-Watson (DW) statistics reveals that there is autocorrelation in the models. 

Optimal Design of Transport Tax on the Way to National Security: Balancing Environmental Footprint, Energy Efficiency and Economic Growth

To ensure the progress of sustainable development and overcome threats to national security, the development of tools for balancing ecological, economic and energy aspects of economic activity acquires special importance. This study is related to the influence of choosing different functional elements of transport taxes on their effectiveness in ensuring national security. For calculations, panel regression modelling with panel-adjusted standard errors based on the use of the Durbin–Watson test using dummy variables was chosen. The assessment involves the selection of the main factor variable (tax revenues from transport taxes, as well as an additional dummy variable) and compliance with a country’s transport taxation system, with a certain criterion for its application. The resulting criterion was chosen as an integral indicator of national security, which summarizes the parameters of environmental footprints, economic growth, energy efficiency and security. Calculations were made for nine countries (i.e., the Czech Republic, Finland, France, Ireland, Israel, Portugal, Romania, Slovakia and Turkey) for the period 1996–2019. The calculations proved that the establishment of transport taxes, depending on the type and weight of the car, increases their effectiveness on ensuring national security or, depending on the cost and age of the car, weakens such effectiveness. The use of emissions generated by the car, its capacity and its type of fuel requires additional research; the obtained results did not allow for unambiguously determining their impact on the effectiveness of transport taxes. The conducted research forms the basis for choosing the optimal design of transport taxes to ensure the maximization of their regulatory effectiveness. The developed approach can be extended to evaluate the effectiveness of other environmental taxes.

ASSESSING THE IMPACT OF HUMAN RESOURCES MANAGEMENT PRACTICES ON EMPLOYEE ENGAGEMENT IN SOUTH AFRICA

Employee engagement has become an area of interest among scholars and corporates alike. Employees who are not engaged do not perform at their maximum and this effect productivity. Hence, it is important for organisations to establish factors influencing levels of engagement. This study assesses the impact of selected HRM practices in the Gauteng Cooperative Governance and Traditional Affairs Department. This study employed the quantitative approach using a survey in the form of a questionnaire. A systematic sampling method was used, from the population (N=376). The questionnaire was distributed to a sample of (n=188) respondents and 157 questionnaires were valid. The results indicated a weak positive correlation between (HRM practices) recruitment, appointment and selection; compensation, rewards and benefits, performance appraisal; and employee engagement with p-values of (r=.07, n=157, p=.38); (r=.23, n=157, p=.00); and (r=.20, n=157, p=.01) respectively. The former was not statistically significant, while the latter two were statistically significant. Collinearity between variables was found to be (β=.07, t (.87) =.38, p0.05); and (B=.18, t (2.58) = 0.11, p>0.05). Recruitment, selection and appointment practices did not predict employee engagement whereas compensation, rewards and benefits practices, and performance appraisal practices predicted employee engagement. The analysis of variance (ANOVA) model was statistically significant (F (3.154) = 4.183, p=.01). The R-Squared (R2) was 8% and the Durbin-Watson statistic 1.93. The results contributed to the available literature on employee engagement.

Towards sustainability: The effect of industries on CO2 emissions

During the last decades, environmental crises through energy consumption and economic growth were noticed as a growing concern among researchers. The industrial sector is the main part of economic growth in each country using conservative energy and emitting carbon dioxide that causes global warming. The Paris agreement and Kyoto protocol were two agreements to prevent governments from emitting CO2 freely. Purpose of this research was to investigate the cement industry, steel industry, and automobile industry products’ effects on CO2 emissions in Iran and to rank them according to their measured effects on CO2 emissions. The methodology used in this study was to estimate equations with CO2 emissions as a dependent variable and cement, steel, and automobile industries’ products with ordinary least squares (OLS) and generalized moment method (GMM) approaches. Stationary, Johansen cointegration, Durbin-Watson, Breusch-Godfrey, Chow breakpoint, and normal residual tests were checked. In-sample forecasting was implemented to check the precision of the estimation and an updated ranking was reported as a final result to consider which industry has affected CO2 emissions more than the others per unit of production cost. In conclusion, the cement industry, steel industry, and automobile industry had the most positive effects on CO2 emissions, respectively. This result is suitable to prioritize the industries for enhancing green technology and optimizing industrial production for a more sustainable economy.

PENGARUH LOKASI, SUASANA CAFE, DAN MEDIA SOSIAL TERHADAP KEPUTUSAN PEMBELIAN

ABSTRACT “This study aims to determine the influence of location, café atmosphere and social media on the purchasing decisions of Waroeng Damandiri Selo Boyolali consumers. The data collection technique for distributing questionnaires with a sample of 100 respondents used the Accidental sampling method”. The research methods used are quantitative methods and descriptive analysis of case studies. Testing data instruments with validity and reliability tests, data analysis used normality tests, multi choline therapy tests, heteroscedasticity tests, autocorrelation tests with durbin Watson tests and hypothesis tests to find out between variables have a significant relationship. the results of the t-test study ( partial ) obtained tcount the location variable 0.934 < 1984 and a significant value of 0.353 > 0.05 which means insignificant. So Ho was accepted and Ha was rejected. The café atmosphere variable resulting from the calculation of the t-test ( partial) obtained 15 tcount, the café atmosphere variable 0.955 < 1.984 and the significant value 0.342 > 0.05 which means insignificant. And the social media variable obtained t-test calculation ( partial) obtained calculate the social media variable 6,174 >1,984 and a significant value of 0.000 < 0.05 which means significant. The results of the F test (simultaneous) obtained Fcounted 53,964 > 2.70 using a α (alpha) level of 0.05 or 5% then Ho was rejected and Ha was accepted. it can be concluded that all independent variables simultaneously affect the dependent variables. Keywords : Location, Cafe Atmosphere, Social Media and Purchasing decisions

Infodemiology of RSV in Italy (2017-2022): An Alternative Option for the Surveillance of Incident Cases in Pediatric Age?

The aim of this study was to evaluate whether or not online queries for Respiratory Syncytial Virus (RSV) retrieved by means of Google Trends™ and the Italian Wikipedia analysis program mirror the occurrence of influenza-like illnesses (ILI), as reported by the Italian Influenza Surveillance network (InfluNet). Estimated rates for ILI in the general population and in the age groups 0-4 years and 5-14 years were obtained for the influenza seasons 2017-2018 to 2020-2021. Similarly, a weekly fraction of online searches was retrieved for a series of terms associated with Respiratory Syncytial Virus. Next, trends for daily visualization of Italian Wikipedia Pages for Human Respiratory Syncytial Virus, Pneumonia, Bronchiolitis, Influenza, and Respiratory Failure were similarly retrieved. The correlation of all search terms with ILI was analyzed by means of Spearman's rank correlation analysis. Among search terms associated with the clinical diagnosis of Respiratory Syncytial Virus infections, the occurrence of ILI was highly correlated only with Bronchiolitis in the age group 0-4 years (β 0.210, p = 0.028), while more generic search terms, such as Bronchitis, fever, influenza, and Pneumonia, were identified as effective predictors of ILI, in general and by age groups. In a regression analysis modeled with ILIs as the outcome variable, daily visualizations for the Wikipedia pages on Bronchiolitis were identified as negative predictors for ILI in general (β = -0.152, p = 0.032), ILI in age group 0-4 years (β = -0.264, p = 0.001) and 5-14 years (β = -0.202, p = 0.006), while Influenza was characterized as a positive effector for ILIs in the age group 5-14 years (β = 0.245, p = 0.001). Interestingly, not only were the search terms extensively correlated with one another, but all of them were also characterized by autocorrelation through a Durbin-Watson test (all estimates DW < 2.0) In summary, our study identified a complicated pattern of data visualization as no clear association between rates of ILI in pediatric age group 0-4 and 5 to 14 years was actually found. Finally, our data stress that the infodemiology option may be quite problematic for assessing the time trend of RSV infections in Italy until more appropriate reporting will be made available, by sharing estimates of Lower Respiratory Tract Infections, and through a more accurate characterization of younger age groups.

Prediction of the Ability to Develop the Relaxation Skill in Drivers of Locomotive Crews

The effectiveness of biofeedback training for the development of relaxation skills using the NeuroDog hardware-software complex is investigated. The relaxation skill is one of the conditions for maintaining the functional state of drivers and assistant drivers of locomotive crews at an optimal level during a working trip, and it is also one of the factors for a successful inter-trip rest. A multiple regression analysis has been carried out, which allows to predict the indicators characterizing the success of developing the relaxation skill, depending on the severity of psychophysiological and personal indicators of drivers and assistant drivers of locomotive crews. Two regression models have been constructed to explain the dependence of the indicators of developing the skill of achieving relaxation on the psychophysiological and personal characteristics of drivers and assistant drivers of locomotive crews. Models are characterized by sufficient quality. Quality indicators: of the first regression model – coefficient of determination R2 = 0.71 (with F(40, 65) = 4.06, p = 0.00000027), Durbin-Watson statistics d = 1.81, of the second regression model – R2 = 0.75 (with F(49, 56) = 3.51, p = 0.0000043), d = 2.09. To predict the indicators of developing the skill of achieving relaxation, regression equations were obtained depending on the severity of psychophysiological and personal indicators of locomotive crew drivers.

Effects of Financial Inclusion on the Economic Growth of Developing Countries: An Empirical Analysis of the Bangladesh Economy

Financial inclusion in its widest meaning refers to inexpensive access to an extensive variety of financial facilities. These include banking products, insurance, stock investments, and other financial services. Financial inclusion helps economic development by widening the financial system's resource base and encouraging rural residents to save. It also helps the rural economy grow. In this study, from 2011 to 2020, the connection between financial inclusion and economic development is examined. Financial inclusion is assessed by the number of Automated Teller Machines (ATMs) and the credit-deposit ratio (CDR), among other factors. Multiple regression analysis was used to analyze secondary data, along with other approaches. Before analysis, the data were checked for completeness and correctness. VIF and Durbin-Watson tests examined multicollinearity and autocorrelation in the data. The study's policy implications include that financial inclusion, in all of its forms, is determined to be one of the most important components of economic development. The study focuses that the government and other policymakers must strive to preserve and promote sustainable and inclusive growth, which may be achieved via the expansion of financial inclusion. Furthermore, the results provide support to the government of Bangladesh’s social banking initiatives, which are being carried out in order to promote financial inclusion. The research found that ATMs significantly boost the country's GDP. Nonetheless, the CDR has a little beneficial influence on the GDP.

Simultaneous Equations System vis-aviz Classical Regression Estimators on Impacts of Some Economic Indicators in Nigeria

The choice of models for analyzing the relationship between Total Debt Services (TDS), Real Interest Rate (RIR), Inflation Consumer Price (ICP) and Gross Domestic Product (GDP) variables using recursive models, though produces non-spurious results since the coefficient of determination (R2) is strictly less than the Durbin Watson (DW) statistic. However, the recursive model did not give a good fit for the relationship between TDS and GDP, since macroeconomic variables are usually prone to endogeneity, first order serial correlation, autocorrelation problems among others. This research work x-rays the TDS-GDP relationship on one hand and TDS versus GDP, RIR and ICP indices on the other hand in Nigeria. The TDS, GDP and RIR are endogenous variables, while the ICP, TRA, and are exogenous variables. Pre-tests analyses using time series datasets extracted from the repository of World Governance Index showed level stationary series I (0) and a causal relationship between TDS and GDP variables. Furthermore, the results from the estimation techniques showed that the Three-Stage Least Square (3SLS) and Seemingly Unrelated Regression (SUR) outperformed the Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) estimators when applied to both the exactly and over-identified structural equations in the simultaneous equation system (SES).

Canadian E-learning platform: Using Moodle for Course Creation

The education sector is not really averse to emerging technology, including the Internet. Technology-Enhanced Learning has evolved into a field of study and practice centered on the use of information and communication technologies in teaching and learning. This research aimed to analysis the use of Moodle for course creation as E-learning platform at selected private institutions in Canada. The study took place at chosen private institutions in Canada. To support the research to evaluate use of Moodle for course creation as E-learning platform at private institutions, the researchers used four Moodle creation aspects, first instructors’ technology experience, second was university’s system quality, third was information quality and last was instructors ‘internet experience. The study used a survey to assess the current study using a quantitative analysis approach. The data was collected at random among 78 instructors from Canada's private institutions. The findings revealed that Instructors’ internet experience as the use of Moodle element has significant positive influence on course creation at 5% level. Furthermore, all beta value is higher than .001. All models have very high adjusted R2 (0.681, 0.627, 0.712, and 0.732 respectively) indicating the ability of the models explaining the variation of course creation due to variation of independent variables is very high. The F-value shows that the explanatory variables are jointly statistically significant in the model and the Durbin-Watson (DW) statistics reveals that there is autocorrelation in the models.

Modeling of Log Kow of a Series of PAHs using Computational Chemistry

The importance of Chemometrics Methods in Modeling (in QSAR analysis) of the mathematical model’s study of large datasets of molecules with huge numbers of physicochemical and structural parameters quantitative structure-Toxicity relationship (QSTR) are mainly based on multiple regression analysis in QSAR analysis The study of Least Square in deriving QSTR models for datasets of Quantitative Structure-Toxicity relationship on Log kow (Octanol-water partition coefficient) for 16 Hydrocarbons compounds has been using the software Hyperchem 6.3 for computing descriptors and MINITAB 16 for data modeling. A three -descriptors model [two electronics molecules’ descriptors (QSER descriptor), HOMO (is Highest occupied molecular orbital) and LUMO (Lowest unoccupied molecular orbital), one QSAR descriptor E_H (Hydration Energy) by Least Squares with correlation coefficient r=0.868, S=0.635, R2 = 75.4%, R2ajd=73.7% and Durbin-Watson statistic =1.85277 and graphical analysis by diagram of goodness of fit and line plot. The results statistical of new model after removing the aberrant compounds (Toxicity compounds) shows high Coefficient of correlation r=0.9581, S=0.4316, determination coefficient R2 =91. 8%, ajustemed R2ajd = 89.3%, Durbin-Watson statistic D=2.373, Three explanatory Variable model selected is robust and has good fitness. Two influential compounds detected and important the model and absence aberant compounds of the studied sample.

Application of ARIMA Model in Forecasting Exchange Rate: Evidence from Bangladesh

This paper attempts to apply the ARIMA time series model to forecast the exchange rate of seven currencies (United States Dollar, Euro, Pound sterling, Australian Dollar, Japanese Yen, Canadian Dollar and Swedish Krona) in terms of Bangladeshi Taka (BDT) and to investigate the accuracy of the model by comparing the forecasted rates with the actual exchange rates. It considered daily currency exchange rates (244 selling price) of seven currencies for twelve months from January 2018 to December 2018 to forecast the subsequent one month (25 selling rate) in January 2019 rate. The Durbin-Watson test result shows an autocorrelation in the daily foreign currency exchange rate with the previous rate. The Augmented Dickey-Fuller test result shows data have unit roots and non-stationary. But the 1st differencing becomes data stationary to apply d equal to 1 in ARIMA model. Also, autocorrelation function considers MA(0) and partial autocorrelation function considers AR(1) for the ARIMA model. So, ARIMA (1,1,0) models are selected based on Ljung-Box test, root mean square error, mean absolute percent error, mean absolute error and R-square values. By using the above ARIMA models, forecasted foreign currency exchange rates next one month calculated and compared with the respective actual rates, which validate with Chi-Square test, mean absolute percent error, mean square error, root mean square error values of Goodness fit test. The result shows that predicted foreign currency exchange rates follow ARIMA (1,1,0) model, which may be applied to forecast the foreign currency exchange rates in Bangladesh.

Panel Vector Auto-Regressive Model For COVID-19 Infected Cases and Deaths

Abstract This study aims to model the dynamic relationships between the number of COVID-19 infected cases and deaths in all the districts of Kerala state, India, from January 2021 to December 2021 based on the panel vector auto-regressive model. The random effect panel vector auto-regressive model of order two was found suitable to model dynamic relationships. This model explains 62 % variations in the endogenous variable, deaths (number of deaths). The exogenous variable deaths (-1) are highly significant, whereas the exogenous variable cases (-1) are significant at a 5% level. Both of these exogenous variables positively influence the endogenous variable. The other exogenous variables, viz., deaths (-2) and cases (-2), are non-significant. The Durbin-Watson test statistic value confirms the independence of the residuals, and the Wald test confirms the validity of the significance of the estimated regression coefficients. JEL classification numbers: E18, HO, I1, J64, J88. Keywords: Fixed and Random Effect Models, Panel VAR model, Cointegration test, Levin-Lin-Chu unit root test, Granger causality test, Hausan test, Wald test.

Multi-factor style rotation – an empirical study in the US stock market

ABSTRACT To evaluate the rotation characteristics of different styles, this study employs the design of experiments (DOE) to systematically combine factor weights to generate stock selection models that express various stock selection styles. This study uses the S&P 500 constituent stocks as the stock pool. At the beginning of each month, we select the highest-scoring 4% stocks with a weighted scoring method and hold them until the end of the month. The back-test period goes from 1999/Jan to 2020/June, a total of 21.5 years (258 months). The results show that (1) many stock selection models mixing value style with growth style have high synergy effect. (2) many models have positive autocorrelations according to the Durbin–Watson (DW) statistics of each model’s time series of monthly excess rate of return. (3) the candidate models of the style timing strategy should include a value style factor, a growth style factor, and a two-factor model with them.

Cash Management and Financial Value of Commercial Banks in Kenya

Purpose: The concept of firm value has become a great concern to shareholders, managers, potential investors, creditors and other stakeholders globally since it measures the firm’s worth and posits a positive public image. Despite measures put in place to improve cash management, the commercial banks in Kenya general performance is on a downward trend this therefore has a negative impact of the financial value of commercial banks. The main objective of the study was to assess the effect of cash management on the financial value of commercial banks in Kenya.
 Methodology: The study was guided by Cash conversion theory. The study employed correlation research design on the panel data collected over a span of 10 years. The target population was 38 commercial banks in Kenya. A secondary data collection sheet was used to document information from audited financial statements downloaded from Nairobi stock exchange and Central bank of Kenya websites. Normality was tested through Shapiro –Wilk and confirmed. Stationarity was tested using Levin-Lin-Chu test and results confirm stationarity. Multicollinearity was assessed through Variance inflation factors and found to be within the range of less than 10, thus Multicollinearity established to be absent in the independent variables. Heteroscedasticity was tested through Breach – Pagan test. The probability of Chi-square of 2 degrees of freedom is 0.21> 0.05 at 5% level of significance confirming homoscedasticity. Auto-Correlation was tested by using Durbin-Watson test. 
 Findings: The results depicted a value of 1.988 which confirms no autocorrelation. Descriptive statistics comprised of mean, standard deviation and variance. The overall descriptive statistics show high variation between the dependent and independent variables among different commercial banks. Inferential statistics comprised of Pearson’s correlation analysis and Random Effects Model. The Pearson’s correlation coefficient depicts r = 0.48 with a p-value of 0.000 for financial value and cash management. The regression coefficients were established as 0.02, p-values < 0.05 for cash management and financial value showing that cash management had a significant positive influence on financial value of commercial Banks.
 Recommendation: It was recommended that commercial banks should improve on income generation and return on assets. Efficiency in using fixed assets should be a key concern to commercial banks.

Economic value-added (EVA) myths and realities: evidence from the Indian manufacturing sector

PurposeThe main aim of this paper is to examine the claim that economic value added (EVA) advocates its superiority over the traditional accounting-based financial performance measures, i.e. profit after tax (PAT), earnings per share (EPS), return on assets (ROA), return on equity (ROE) and return on investment (ROI) in the Indian manufacturing sector and at the same time, give empirical facts. It also tests and examines the information content of various performance measures and their relationship with stock returns.Design/methodology/approachThe paper uses the sample of 534 Indian manufacturing companies from the Bombay Stock Exchange (BSE) during the period 2000–2018. Multiple regression models are applied to examine the information content of EVA and traditional performance measures in explaining shareholders’ returns.FindingsRelative information content tests revealed that traditional accounting-based measures such as EPS, ROE and ROA performed better than EVA in explaining the returns of Indian manufacturing companies. Incremental information content of EVA adds little contribution to information content above traditional performance measures. The claim of superiority of EVA over accounting-based measures in association with shareholder returns is proved invalid in Indian manufacturing companies.Originality/valueThis study concludes that EVA has no superiority over traditional accounting-based financial performance measures in explaining stock returns of Indian manufacturing companies. To achieve heftiness in outcomes, panel data are tested by using Breusch–Pagan–Godfrey (BPG) test for heteroskedasticity, Hausman’s test for fixed and random effect, variance inflation factor (VIF) test for multicollinearity and Durbin–Watson test for autocorrelation.

Statistical analysis of rainfall trend and its variability (1901–2020) in Kolkata, India

The current study focuses on the altering historical rainfall data analysis and its variability in Kolkata (Kolkata Municipal Corporation), a metropolitan city in India. The research area experiences detrimental urban floods (pluvial floods) at near-annual regularity during the monsoon, and during the pre-monsoon seasons it commonly experiences water shortage problems. Analysing trends and temporal variability of rainfall over 120 years from 1901 to 2020 is the main objective of this study. The original Mann–Kendall (M–K) test has been applied to the rainfall dataset in conjunction with Sen’s Slope Estimator using Python 3.10, after the Durbin-Watson (DW) statistic initially suggested that there is no serial correlation effect. The M–K test, with a Kendall’s tau of 0.17058 (significant at a 5% level), shows an upward trend in annual rainfall between 1901 and 2020. The Sen’s slope, which measures the rate of change annually, has a value of 2.48152. Regression analysis and other dispersion measures are also used in this study to investigate the monthly rainfall trend and its variability. The phase-wise (30-year) analysis of annual rainfall variability reveals a considerable variation over 120 years. While fitting the linear regression line month by month over the entire period, mostly negative trends were found in the pre-monsoon and positive trends in the monsoon and post-monsoon seasons. The findings of this analysis could be useful to urban planners for water supply and management in the study area. The primary concern of planners for effectively managing rainwater and the accompanying issues should be the growing variability of annual precipitation.

Measuring the Connotations of Economic Variables with External Debts of Bangladesh

This study ascertains the associations of the macroeconomic variables with external debts, the impacts of external debts on annual GDP, GDP growth, and per capita income, and the timing effects on the economic variables of Bangladesh. The analysis is inferential and eight hypotheses have been set and tested with the regression analysis. To complete the study, the chi-squared test and Durbin-Watson test have been used along with trends and growth analysis. The results of the study reveal that the budget deficit, current account deficit, and defense budget have a positive association with external debts. Furthermore, foreign direct investment, foreign exchange reserve, foreign remittance, and trade of balance have a negative association with external debts. The external debts have no negative impact on annual GDP, GDP growth, and per capita income. Finally, there is a significant timing effect on the economic variables of Bangladesh during the study period.

A comparative analysis of factors influencing colorectal cancer's age standardized mortality ratio among Korean women in the hot and cold spots.

The study aimed at exploring factors that most influence colorectal cancer (CRC) age standardized mortality ratio (ASMR) among Korean women, as reported in previous studies. The factors used the data of 250 municipalities from the Korean Statistical Information Service (KOSIS) from 2010 to 2018. In the exploratory survey, over 70% of women aged 65 and above died of colorectal cancer. After investigating the existing literature and theories, 250 regions were classified into hot and cold spots according to age standardized mortality ratio (ASMR). The Nearest Neighbor Index (NNI), Moran’s I index and The Durbin-Watson test were also utilized. The ASMR’s regional cluster analysis showed that the inland areas were the hot spots and the cold spots were in the southwest coastal areas. The result also showed the differences in dwellers’ lifestyles between these two regions as well as the mean difference between the two. In addition, there was no significant difference in ASMR for breast cancer, CRC deaths, and agricultural product shipments between the two regions. In the multiple regression model, CRC mortality, diabetes, and CRC age standardized incidence ratio (ASIR) were analyzed as major influencing factors, demonstrated a significant result with 30.6% by examining the adjusted R-squared. However, this study showed that factors such as smoking, alcohol consumption, abdominal obesity, breast cancer, and food consumption indicated to have less influence on the occurrence of CRC. The aging rate, amount of food consumption, seafood production, livestock product shipments, and drinking rate were higher in the cold spot than in the hot spot.

Effects of Crop Insurance and Finance on Small-Scale Maize Productivity in Zambia

Productivity of maize is dependent on facilitative and competitive interactive effects on resource availability and other moderating factors. The study investigated the impact crop insurance and financing had on the productivity of small-scale maize farmers in Southern province of Zambia. It also sought to see the effect moderating factors have on maize productivity. The relationship between crop insurance and financing, and maize productivity was explored by interviewing 602 farmers in Mazabuka, Monze, Choma and Kalomo districts through a structured questionnaire. This also included interviews with insurance and finance providers. SPSS and hierarchical multiple regression analysis were used to evaluate the data after making some assumptions. The regression analysis was run to determine the relationship between maize productivity, loan, insurance and the interaction between loan and insurance over and above the control variables. The results showed that the relationship was not supported (t = -0.750, p > 0.05) and that insurance and financing in the four districts studied did not have any effect on productivity. There was no significant relationship between crop insurance and productivity (t = -1.741, p > 0.05). The model used to analyze the data excluded financing as it did not bring any additional significant information. The results further indicated that there was linearity as determined by partial regression plots, as well as residual independence as determined by the Durbin-Watson statistic of 1.745. Results showed no evidence of multicollinearity based on correlations and Variance Inflation Factors (VIFs) and that farmers relied heavily on the government subsidy program, the FISP which resulting in less or no effect of commercial crop insurance and financing on productivity.