Beta Distribution for Estimating Risk Associated Time Schedule of Hydropower Projects in Nepal

Background: Risk is associated with every kind of project work whether it is related to engineering construction project, software development project, financial transaction process or business process. There isn't any project which is free of risks. It is inherent in all types of projects. Observing risk associated with a project can help in successful completion of projects in expected time and expected cost with good assurance of quality. This article is concerned with quantitative analysis of risks coined with hydropower construction project in Nepal. Objective: The main objective of this paper is (a) to identify different activities involved in hydropower construction projects (b) to estimate risk associated time schedule of the identified project activities. Materials and Methods: Data required for the fulfillment of the objective are obtained by interview and discussion with executives of “Shiva Shree Hydropower Limited” and by using project schedule charts of projects won by the company. In this article quantitative analysis of schedule risk of hydropower project is studied by simulation method. Results: Different activities involved in hydropower construction project are identified. Also, risk associated with time schedule of project are observed quantitatively by simulation using beta-PERT distribution. Conclusion: Estimation of time schedule associated with project activities is more realistic when it is analyzed by using beta-PERT distribution compared to other statistical distributions.

Factors Affecting Household Expenditure using OLS Method: A Case Study of Ward Number 6 of Sidhalek Rural Municipality, Dhading, Nepal

Background: The role of annual household expenditure is very important to run a family smoothly. It incorporates the total expenditure of a family who is residing in a house for their livelihood. There are different factors associated with annual household expenditure, and the assessment of them and identification of such factors play a vital role in the concerned area. Objective: To identify the most significant factors associated with annual household expenditure using an appropriate statistical model. Materials and Methods: The study based on cross-sectional study design with 168 households from ward number 6 of Sidhalek rural municipality of Dhading district were considered for the analysis. The multiple linear regression model followed by appropriate bivariate analysis was used to identify the significant factors associated with the outcome variable. Results: Number of literate persons of working age (β = 0.018, 95% CI: 0.004 - 0.032), remittance-receiving status (β = 0.093, 95% CI: 0.026 - 0.160), and gender of household head (β = 0.089, 95% CI: 0.023 - 0.155) was significantly associated with the annual household expenditure. The goodness of fit and diagnostics of the fitted model were also performed. Conclusion: The annual household expenditure is significantly higher among those households having more literate persons of working age, headed by a male and receiving remittance. Considerable improvements need to be made towards the improvements on human capital and remittance-receiving status particularly in the rural area of Nepal.

Multinomial Logistic Regression Model for Assessing Factors Associated with Junk Food Consumption of Secondary Level Students

Background: Globalization, economic progress, urbanization, and industrialization have ushered in considerable changes in lifestyles and diet regimes. Consumption of junk food has surged at a rapid pace nowadays. In Nepal, there is a lack of sufficient research regarding junk food practices and persuading factors for its consumption, among teenage students. It is a global concern and has threatened the health of many people. Objective: This research aims to explore the factors associated with junk food consumption among students of secondary level in Ratnanagar Municipality of Chitwan. Materials and Methods: A cross-sectional research design consisting of 371 secondary level students, and selection based on primary data collection method by the researchers was used in this study. The data was analyzed using descriptive as well as an inferential statistical method. To find the association of different factors with junk food consumption levels (Low, Moderate, and High) multinomial logistic regression model (MLRM) was used, and the goodness of fit of the model was assessed. Results: The fitted MLRM satisfied the criteria of the diagnostic test including a test of goodness of fit, multi-collinearity test, and minimum criteria of the model utilization with the classification accuracy of 55%. The variables like family monthly income (OR=1.000011, C.I=1.000001 - 1.000020), students’ daily pocket money (OR=1.02, C.I=1.007 - 1.033), gender (OR=0.354, C.I= 0.179 - 0.7), knowledge regarding junk food consumption (OR=2.744, C.I=1.366 - 5.511), attitude towards food choice (OR=2.487, C.I=1.288 – 4.803), friend’s encouragement for junk food consumption (OR=0.178, C.I=0.045 – 0.701), and occupation of father (business) (OR=2.867, C.I=1.084 – 7.581) are seen to be significant to the model High versus Low consumption. Students’ daily pocket money (OR=1.012, C.I=1.002 – 1.023), knowledge regarding junk food consumption (OR=2.427, C.I=1.293 – 4.553), restrictive food parenting practice (OR=2.228, C.I=0.945 – 5.253), friend’s encouragement for junk food consumption (OR=0.252, C.I=0.067 – 0.944), recognition of marketing promotion strategy (OR=0.523, C.I=0.295 – 0.928), educational level of mother (lower secondary level) (OR=5.465, C.I=1.069 – 27.952), occupation of father (business) (OR=2.47, C.I=1.056 – 5.777) are seen to be significant to the model Moderate versus Low consumption. Conclusion: Study revealed that more than one-fourth of the sampled students were high consumers of junk foods. Many factors are associated with the consumption of junk foods. Thus, addressing the issue of increasing consumption, developing strategies & conducting different research in this field is the must. Concerned authorities should also pay special attention to this subject matter.

Negative Binomial Distribution to Explain the Domestic Fire Incidence in Nepal

Background: Fire disaster is one of the most destructive disasters. According to global dataset of Sendai Framework, domestic fire incidence was 9.9% up to 2019. In Nepal, 62% fire incidence was reported during 2017 and 2018. However, many studies have been conducted on fire incidence, few of them are based on domestic fire incidence. Objective: To find the descriptive statistics of fire occurrences and fire fatalities, and to identify the probability distributions that best fit the data of fire occurrences observed in three ecological regions as well as overall in Nepal. Material and Methods: The data of fire incidences from May 2011 to April 2021 were retrieved from Nepal Disaster Risk Reduction Portal, Government of Nepal. At first, a statistical software "Mathwave EasyFit" of 30 days trial version was used to identify the candidate probability models. Further, the best probability model was determined after testing the goodness of fit of the candidate models by using graphical tools-histogram and theoretical densities, empirical and theoretical CDFs, Q-Q plot and P-P plot; and mathematical tools-maximum likelihood, Akaike Information Criteria and Bayesian Information Criteria by using the package “fitdistrplus” of software R version 4.1.1. Results: On an average, 135 fire incidences per month were occurred in Nepal. However, the Terai faced the highest monthly fire incidences compared to the Hill and the Mountain, it has less fatality per 100 fire incidence followed by the Hill and the Mountain. Descriptive statistics reveals that fire occurrences are moderate during November to February and high in March and April. The fire incidences were reported high during spring and winter and low during summer and autumn season which reveals that fire incidence might be related with the precipitation and temperature. The sample data was run in "Mathwave EasyFit" software which suggested Poisson, geometric and negative binomial distribution as candidate probability models. The goodness of fit of these models were further tested by graphical as well as mathematical tools where negative binomial distribution was found to be best among the candidate models for the data set. Conclusion: Incidence of fire disasters varies by ecological regions as well as by seasons. It is low in the Mountain region and during Monsoon/rainy season. Negative binomial distribution fits the best to monthly data of fire incidence in Nepal.

A Note on Confusion between Linear and Affine Functions and the Generalized Forms of Gradient

Background: Arguably the most frequently used term in science, particularly in mathematics and statistics, is linear. However, confusion arises from the various meanings of linearity instructed in different levels of mathematical courses. The definition of linearity taught in high school is less correct than the one learned in a linear algebra class. The correlation coefficient of two quantitative variables is a numerical measure of the affinity, not only linearity, of two variables. However, every statistics book loosely says it is a measure of linear relationship. This clearly show that there is some confusion between use of the terms the linear function and affine function. Objective: This article aims at clarifying the confusion between use of the terms linear function and affine function. It also provides more generalized forms of the gradient in different branches of mathematics and show their equivalency. Materials and Methods: We have used the pure analytical deductive methods to proof the statements. Results: We have clearly presented that gradient is the measure of affinity, not just linearity. It becomes a special case of the derivative in calculus, of the least-squares estimate of the regression coefficient in statistics and matrix theory. The gradient can ­­­­be seen in terms of the inverse of the informative matrix in the most general setting of the linear model estimation. Conclusion: The article has been clearly written to show the distinction between the linear and affine functions in a concise and unambiguous manner. We hope that readers will clearly see various generalizations of the gradient and article itself would be a simple exposition, enlightening, and fun to read.

Assessment of Factors Associated with Indoor Air Pollution using Multinomial Logistic Regression: A Case Study of Barbardiya Municipality

Background: Indoor air pollution (IAP) is a major risk factor of acute respiratory infections (ARIs). Most of the households in rural Nepal still depend upon biomass fuel. Associations between socio-economic variables and IAP can be captured using different statistical models. Objectives: The objective of the study was to determine association between IAP condition with socio-economic and demographic factors across the households of Barbardiya municipality, Bardiya, Nepal. Material and Methods: Based upon the primary data of 370 households, association between IAP condition (high, moderate, low) with socio-economic and demographic variables (Ethnicity, education level, income, family size, land size etc.) were established using multinomial logistic regression model. Results: Descriptive analysis revealed that almost 21% of the households were exposed to high (sever) IAP, 41% to low and rest (38%) were moderate. Fitted model predicted that the odds ratio of socio-economic variables like Ethnicity: Tharu community relative to Bhramin/Chhetri community was 2.5 (95% Confidence Interval (CI): 1.08 -5.78), household head’s education level: illiterate relative to literate was 8.21 (95% CI: 3.30-59.54), family’s monthly income: ≤ NRs 30000 relative to > NRs 30000 was 2.38 (95% CI: 1.10-5.16) considering high with respect to low IAP. Conclusion: Households of Tharu community have significant proportion of high and moderate levels of IAP (or household air pollution) compared to other ethnic groups. Literacy status of household head also have an association with IAP along with several other factors. Various methods can be adopted to reduce IAP which was recommended to concerned authorities.

Impact of Family Functioning, Family Violence and Other Factors on Juvenile Delinquency

Background: Juvenile delinquency is the act of participating in unlawful behavior as minors or individuals younger than the age of majority. Nepal shows an increasing trend of violent crimes committed by a juvenile. Objective: To examine the impact of family functioning, family violence, and other family factors on juvenile delinquency. Materials and Methods: The study is a case-control study based on primary data collection of 354 respondents which include equal number of juvenile delinquents as cases taken from juvenile correction homes and school students as controls who have never been convicted for any act of juvenile delinquency. Data was collected through the convenience sampling method. Structured questionnaire was used to collect data on demographic, socioeconomic, individual and family factors. Bivariate and logistic regression analysis were performed to determine which factor act as a risk or protective factor for juvenile delinquency. Results: From bivariate analysis, family factors such as family functioning, parental monitoring, parental involvement, parental supervision, and parental attachment were found higher in controls than cases. However, family violence was found higher in cases than control group. Further, from fitted logistic regression child age, aggressive behavior, family structure, family financial condition, punitive parenthood, mother education level, and parental attachment were found significant factors impacting on juvenile delinquency. Conclusion: This study revealed that age, aggressive behavior, family structure, and family financial condition are the risk factors for juvenile delinquency. Further, mother education, punitive parenthood, and parental attachment are found protective factors for juvenile delinquency.

Supplementary Materials: Drought or Wet Assessment of Daily Rainfall Pattern of the Budhi Gandaki River Basin, Nepal: Standardized Precipitation Index Approach Using Probabilistic Model

Supplementary material for the article on pages 57-72 https://doi.org/10.3126/njs.v4i0.33497

Binary Logistic Regression Model for Assessing Factors Associated with Nutritional Status of Children Under Five Years among Chepang Community in Siddi, Chitwan, Nepal

Background: Chepang Community in Nepal has often been characterized as the poorest among Nepal’s poor, and has faced food insecurity at a household level each year. Health status of their children is poor compared to other community children and more so for the age group under the age of five years. Objective: To find the proportion of stunting, wasting and underweight, and to assess the most promising factors associated with the nutritional status of children under five years of Chepang community in Siddi, Chitwan, Nepal. Materials and Methods: A cross-sectional research design was used with 170 samples. To find the association of different factors with the nutritional status of children under 5 years of age (stunting, wasting and underweight, each) binary multiple logistic regression model was used and the goodness of fit of the model was assessed through Hosmer and Lemeshow test. Results: The overall proportion of children under five years of the Chepang community is 60.6%, 30.6% and 44.7% for underweight, stunting and wasting respectively. Among many factors, the mother’s illiteracy (OR: 5.30, 95 % CI: 1.37 to 21.57), external monthly family income (≤ NRs. 2000) (OR: 9.65, 95 % CI: 3.23 to 30.78) and food availability for six months from own land (OR: 4.17, 95 % CI: 1.90 to 17.98) have a significant association with underweight. Similarly, for stunting, female child (OR: 2.19, 95 % CI: 1.02 to 4.70), number of children (more than one) below 14 years at home (OR: 4.15, 95 % CI : 1.77 to 11.04) and external family income (≤ NRs. 2000) (OR: 4.42, 95% CI: 1.01 to 17.06) are significantly associated, and for wasting, a number of children (more than one) below 14 years at home (OR: 4.64, 95 % of CI: 1.71 to 12.60) and external family income (≤ NRs. 2000) (OR: 5.17, 95 % CI: 2.27 to 11.79) are significantly associated. Conclusion: Substantial numbers of children from Chepang community are having the problem of underweight, stunting and wasting. Different demographic and socio-economic variables have been found associated for odds of having low level of nutritional status of children.

Determinants of Trade Deficit in Nepal: An Econometric Investigation

Background: Nepal has been importing some of goods and services from other countries and is also capable of exporting some other goods and services to foreign countries. Because of over dependency on foreign goods, Nepal has been suffering from trade deficit for more than 45 years. Objective: This study aims to investigate the relationship of trade deficit in Nepal with its determinants by using econometric analysis where exchange rate, real gross domestic product and foreign direct investment are taken as determinants of trade deficit. Its main objective is to examine the long-run, short-run and causal relationship among the variables. Materials and Methods: Annual time series data from 1974/75 (from 1988/89 in case of FDI) to 2018/2019 obtained from different sources: Nepal Rastra Bank, Economic Survey of Nepal, The World Bank and Department of Industry, Nepal Government were used in this study. Unit root test was used to check stationary. Autoregressive Distributed Lag (ARDL), ARDL bound test and Error Correction Model (ECM) were applied to find short-run as well as long-run relationship. Finally, pair-wise causal relationship was tested by using Granger causality. Results: All variables were found to be stationary at first difference. The test statistic of ARDL bound test was 8.17 and was greater than upper bound of 6.36 at 1% significance level. Error correction term’s p-value was 0.0052 and the corresponding values for pair-wise Granger causality from trade deficit to FDI and FDI to trade deficit were 0.00009 and 0.00005, respectively. Conclusion: There was positive and significant long-run relationship between exchange rate and trade deficit whereas there was negative and significant long-run relationship between real GDP and trade deficit. Moreover, real GDP positively affected trade deficit in short-run. Furthermore, bidirectional causal relationship has been observed between FDI and trade deficit.