Statistics Papers & Publications

Assessment of convergent validity of latent variables is one of the steps in conducting structural equation modeling via partial least squares (PLS-SEM). In this paper, we illustrate such an assessment using a loadings-driven approach. The analysis employs WarpPLS, a leading PLSSEM software tool.

Johnny Amora

Abstract Abundant solar energy is freely available almost round the year in India. As per the current scenario of global warming and climatic change, solar energy is the cleanest source in nature. Concentrated solar power (CSP)has hardly contributed to the overall installed solar power capacity in the country. CSP technologies are Parabolic Trough Collector (PTC), Linear Fresnel Reflector (LFR), Paraboloid Dish and Solar Power Tower. This paper presents a review of CSP in solar parabolic dish concentrator to understand thermal aspect like thermal efficiency, optical efficiency, useful heat gain, heat losses, solar irradiation, etc. for various applications and current development. The current scenario of global CSP is discussed to meet the future challenges and need of the society.

Pramod Wadate

Regression modeling analyses the relationship between two or more variables and can be used to predict the response variable from one or more independent variables. The present study uses linear regression analysis to evaluate the growth in the two fish species of genus Oreochromis, Nile tilapia and Jipe tilapia, under aquaculture conditions. The models were fitted using a collection of functions in the R-software library. The final models were selected using the goodness of fit criteria based on the coefficient of differentiation, the model p- values and Akaike information criteria. The significance of the linear relationship between predictor variables and the mean response was tested by comparing the computed standardized parameter estimates, whereas the confidence intervals were constructed to assess the uncertainty of predicting the response variable and determine outliers in the model. Generally, both species exhibited good condition during growth and all the measured water quality variables significantly afffected growth (p<0.05). However, only temperature and dissolved oxygen produced the most important linear relationship with fish weight. The study recommends that data from a controlled experiment should be used the determine the interactions between the two growth variables.

Omweno job

Micro-level assessment of vulnerability to climate change creates basis for policy formulation. The study specifically ascertained the levels and determinants of vulnerability to climate change among selected food crop farmers. Data collected were analysed using descriptive statistics and ordinary least square regression analysis. The result revealed that 15.95%, 68.97% and 15.08% of the households were highly vulnerable, moderately vulnerable and less vulnerable to climate change respectively. This implies a varied effect on crop farmers. The result also showed that amount saved, extension contacts, household expenditure and value of crop were significant at 1% level. The study recommended the provision of basic amenities and soft loans to farmers as well as an improvement in extension services. It also advocated the introduction of effective climate change mitigation and adaptive measures to boost agricultural output in their area.

Comfort chikezie

The study of diabetes is not only limited to particular symptoms, but it is consequently affects the pathological and functional changes in the metabolic pathways of human body system. In those symptomatic diseases various drugs are used to treat the diabetes such as biosimilar therapy including use of insulin and insulin analogues, oral hypoglycaemic agents and various other complementary medicines. In understanding of suggested potential antidiabetic, effect of M. charaantia Linn. on fasting blood sugar levels and its biochemical analysis in alloxan- induced diabetic rats were investigated. The extracts of M. charaantia Linn. Produced a significant antidiabetic activity at normal dose levels of their lethal doses. A comparison between the action of reduction in blood glucose level in different dose forms of M. charantia extract and Std. drug were seen. An oral glucose tolerance or oral tolerance test were performed with the use of glucose strip Accu-check meter. The different extract viz. ethanol extract + water, petroleum ether + Isopropyl alcohol extract were used for further dosing purpose. The ethanol + water extract were showed significant (P<0.001) antidiabetic activity. In alloxan induced rat model blood glucose level were as, 214.5±5 mg/dLfor std.drug and 216.5±5 mg/dL in comparison with diabetic control 225.5±5 mg/dL. An ANOVA was used for the statistical analysis and p-values less than 0.01 compared to normal group and 0.05 compared to diabetic control group were considered statistically significant. The extract of M.charantia Linn. from seed at the dose of 250 mg/kg, significantly shows the better result in reduction of blood glucose level as compared to the concentration of 500 mg/kg. The increased level of glucose due to the damage of pancreas showed regeneration of pancreatic enzymes by extract of M. charaantia Linn. Which were damaged by alloxan treatment. These solvent extract also balance the body weight loss in diabetic rat, hence the present extract shows the potential to act as antidiabetic drug.

Dr. Wahul Umesh B

The microbiological quality of purified water is a crucial aspect in the healthcare industry to ensure safety for different applications and uses. Understanding the trend and forecasting would be of prime importance to take proactive control and protective measures before catastrophic excursions might occur leading financial and health casualties. This study analyzes microbial density, a key metric for monitoring water purification system efficacy in healthcare facilities. The objective was to transform irregular, cumulative data into a regular time series and identify the optimal ARIMA model for forecasting to support predictive maintenance and regulatory compliance. Preliminary modeling attempts were conducted using simpler approaches such as linear, exponential and Holt-Winters methods without showing promising outcomes. Descriptive statistics and distribution analysis, including the Johnson Transformation for normality, were performed. ARIMA models with differencing orders d=0, d=1, and d=2 were fitted to the Aggregated cumulative logarithmically transformed data series, with the best model at each order selected based on minimum AICc. Model adequacy was assessed through parameter significance and residual diagnostics (Ljung-Box test). Descriptive statistics showed the aggregated series non-normal (p<0 d=0) AICc=319.39) d=2) AICc=258.98)>0.5). The ARIMA(2, 1, 2) model (d=1) was optimal (AICc=256.91), with all significant parameters and white noise residuals (p>0.3), effectively addressing non-stationarity. Forecasts from ARIMA(2, 1, 2) predict stable future growth. The ARIMA(2, 1, 2) model with first-order differencing is the most appropriate and robust model for forecasting data trends. Its strong statistical fit and reliable residual properties make it a valuable tool for predictive maintenance, optimizing resources, and enhancing patient safety in healthcare water systems, provided model performance is continuously monitored. Addressing data limitations and processing requires monitoring and exploring alternative models for future improvement.

Mostafa Eissa

Ensuring consistent raw material quality is a significant challenge in chemical manufacturing, particularly for medicinal compounds where safety and efficacy are paramount. In these situations, a unique methodology known as Statistical Process Control (SPC) come into play. This study provides statistical process control analysis of four critical operational parameters for most the raw chemical compounds, especially in the medicinal chemistry— Specific Optical Rotation (SOR), Water Content (WC), RI, and Chromatographic Purity (CP)—derived from a dataset of 26 observations in an applied engineering context. The methodology encompasses descriptive statistics, rigorous distribution identification using Goodness-of-Fit tests, and process stability assessment via Individual- Moving Range (I-MR) and Exponentially Weighted Moving Average (EWMA) control charts. Descriptive statistics revealed diverse data characteristics, notably the high positive skewness (2.623) and kurtosis (9.386) of WC (Mean ± Standard Deviation: 0.177±0.106987) and the presence of negative values for SOR (Mean: -0.1, Min: -2, Max: 2). Distribution fitting identified Logistic and Normal as the most suitable for SOR, while RI demonstrated a best fit for normal distribution with Johnson Transformation. WC and CP exhibited significant non-normality and challenges in fitting standard distributions, often accompanied by warnings regarding convergence or parameter estimation stability. Crucially, control chart analysis identified significant out-of-control conditions for SOR, WC, and RI, indicating inherent process instability. CP, conversely, demonstrated stability with the optimized EWMA chart. The findings underscore the necessity of tailored statistical approaches for diverse data characteristics in quality control. Implementation of Statistical Process Control should not be underestimated in the chemical manufacturing industry, notably in the developing nations.

Mostafa Eissa

Implementation of Statistical Process Control (SPC) techniques in food and beverage industry are crucial to deliver consumable product that meets customer expectations. This study investigated industrial pH forecasting and process stability in a syrup manufacturing facility. We analyzed 1,247 pH observations with three objectives: (1) Quantify instability via control charts, (2) Model pH dynamics using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Classification And Regression Trees (CART), and (3) Develop diagnostic frameworks for unstable processes. Methodologically, Exponentially Weighted Moving Average (EWMA) charts assessed stability; Box-Cox transformed SARIMA (λ=2) with seasonal differencing was used for forecasting; CART identified variable importance. Control charts revealed profound instability: 83.3% of points violated 3σ limits; run tests significant (p<0.001). For SARIMA, (1,0,1)(0,1,1)₁₂ achieved significant parameters (p<0.0001) with improved residual diagnostics versus non-seasonal ARIMA, though minor autocorrelation remained at lag 12 (p=0.003). CART explained training R²=18.86% and test R²=17.93% of pH variation, identifying filling weight and sodium benzoate as key predictors. Crucially, this study demonstrates that forecasting retains diagnostic utility even in unstable environments: SARIMA residuals provide seasonal fingerprints of assignable causes, while CART thresholds guide intervention priorities. SARIMA(1,0,1)(0,1,1)₁₂ demonstrated superior residual properties: eliminated back forecast warnings (present in ARIMA), reduced autocorrelation at lag 24 (p=0.017 vs 0.040), and explicitly modeled 12-period seasonality. While process instability persists, SARIMA provides diagnostic fingerprints of assignable causes through seasonal parameters (SMA₁₂=0.9846, T=513.12) and residual patterns. We conclude that SARIMA offers enhanced short-term forecasting capability, but process intervention remains essential for reliability. The study advocates for integrated instability-informed forecasting combining SARIMA diagnostics, real-time control charts, and expanded sensor deployment.

Mostafa Eissa