Quadratic Trend Model Research Articles

Modelling the Relationship Between Merchandise Trade Flows and Some Macroeconomic Variables in Ghana

Macroeconomic variables serve as economic indicators that offer valuable insights into the overall health and stability of an economy. Changes in these variables can have significant impacts on a country's trade balance and overall economic performance. This study employed multivariate time series analysis to study the relationship between Merchandise Trade Flows (MTF), Monetary Policy Rate (MPR), Commercial Lending Rate (CLR), Nominal Growth Rate (NGR) and Consumer Price Index (CPI) with Money Supply (MoS) as exogenous variable. The nature of trend in each series was investigated. The results revealed that quadratic trend model best models MTF, MPR, CLR and NGR whiles an exponential trend best models CPI. Johansen’s co-integration test with unrestricted trend performed revealed the existence of long-run equilibrium relationships between the variables and three (3) co-integrating equations described this long-run relationship. In terms of short-run relationships, the VEC (2) model revealed that, CLR, NGR, MoS have positive and significant impact on MTF. CLR, NGR and MoS have positive and significant impact on MPR, NGR have positive and significant impact on CLR, CPI and MoS have significant impact on NGR whiles NGR and MoS have significant impact on CPI. Model diagnostics performed on the VEC (2) model showed that, all the model parameters are structurally stable over time and the residuals of the individual models are free from serial correlation and conditional heteroscedasticity. Forecast error variance decomposition (FEVD) analysis showed that each variable primarily explained its own variance and the influence of other variables increase over time. Hence, adopting a broad perspective on macroeconomic variables can help policymakers anticipate and mitigate ripple effects across various economic sectors.

Development of average run length formulas for EWMA control chart under the AR(1) with quadratic trend model for detecting and monitoring process variability

Development of average run length formulas for EWMA control chart under the AR(1) with quadratic trend model for detecting and monitoring process variability

Price Forecasting Analysis of Red Chilli Using Seasonal Regression and Quadratic Trend Models

This study aims to compare the effectiveness of the seasonal regression model and the quadratic trend model with seasonal indices in predicting the price of red chilli in Johor, Malaysia. Historical price data from 2018 until 2022 of red chilli in Johor, Malaysia, were collected and analyzed using the seasonal regression model and the quadratic trend model with seasonal indices. The forecasting accuracy and predictive performance of each model were evaluated and compared. The results indicated that the seasonal regression model outperforms the quadratic trend model with seasonal indices in predicting the price of red chilli. This study contributes to the field of agricultural forecasting by comparing different models for price prediction and highlighting the importance of considering multiple crops in the analysis. The findings of this study have practical implications for policymakers, stakeholders, and researchers involved in agricultural planning and food security. The identified seasonal regression model can serve as a valuable tool for predicting the price of red chilli, enabling more informed decision-making in crop production and market interventions.

The Effectiveness of E-Books in Learning: An Analysis of Trends in Elementary Schools

Less than optimal student learning outcomes are a problem experienced by teachers in elementary schools. It is evidenced by the student learning outcomes that have not been maximized. The objective of this study is to provide a comprehensive description of the effectiveness of incorporating e-books into the learning process. This study used a quantitative approach with a time series design. The research subjects consisted of elementary school students and teachers. The student subjects involved came from the fifth-grade level. Data collection was done by testing and observation. Data analysis was performed by testing four trend models. These are the Linear Trend Model, Quadratic Trend Model, Growth Curve Model, and S-Curve Trend Model. The research findings indicate that e-books are a viable tool for facilitating learning in elementary school settings. It is based on the positive trend shown in student learning outcomes after applying the e-book as a learning tool. It strengthens the recommendation regarding implementing e-books by teachers to maximize student learning outcomes.

Statistical Trend Analysis on Infant Mortality Rate and Estimation of Live Births in Nigeria

Particularly in nations like Nigeria, the likelihood that a child may pass away before turning one is rather distressing. Infant Mortality Rate is the term used to describe this. Ending all preventable deaths of children under the age of five is goal 3.2 of the Sustainable Development Goals (SDGs). This study's foundation is the statistical analysis of Nigeria's infant mortality rate. The secondary data was gathered from the World Bank Survey, which included annual records of the infant mortality rate in Nigeria over the ten-year period from 2012 to 2021. To assess the trend and estimate the model parameters, Time Series Analysis (TREND) was the statistical method employed. The researchers used the trend to predict for the following ten years. Both the number of infant fatalities and the infant mortality rate are moving decrease, according to the trend study. This demonstrates that the Infant Mortality Rate in Nigeria steadily decreases as the live birth rate rises, yet it is unlikely that it will reach zero or be insignificant by 2032. According to the analysis, the quadratic trend model fits the data sets the best because it has the smallest error margin and an R-square value that is extremely close to 100%. According to the model developed, Nigeria's infant mortality rate is expected to continue to drop throughout the course of the following ten years. Yet, the finding is not striking enough because, in 2031, the infant mortality rate will probably still be higher than 45. Hence, if appropriate measures are not implemented, we will not be able to achieve the goal of eliminating all preventable deaths of children under the age of five and lowering new birth deaths to no more than 12 per thousand. Also, it was seen from the table and graphs that the infant mortality rate decreased more rapidly from 2016 to 2017. This shows that the Sustainable Development Goals (SDGs), which went into effect in 2016, contributed to a decrease in the infant mortality rate.

Growth models for forecasting the area, production and yield of major cereal crops in Ethiopia

ABSTRACT Although accurate forecasting of the production of major cereal crops is important to make policy decisions related to their supply, demand and import/export, very little work has been done in Ethiopia. Therefore, this study aimed to develop the best performing trend models for area, production, and yield of wheat (Triticum aestivum L.), teff [Eragrostis tef (Zucc.) Trotter], and maize (Zea mays L.) in Ethiopia, and to provide forecasts for 2019 to 2023. For each crop, the best-performing model was selected among linear, quadratic, exponential growth, and S-curve trend models using five performance measures (mean absolute percent error [MAPE], mean absolute deviation [MAD], mean squared deviation [MSD], coefficient of efficiency [E], and index of agreement [d]). Quadratic trend model was the best for area of wheat and teff, production of wheat, teff, and maize, and wheat and maize yields. S-curve and Linear trend models were the best for area of maize and yield of teff, respectively. Forecasts for 2019 to 2023 using the best-performing model revealed that cultivated area for these three cereals will decrease by 3,581 ha in Ethiopia, but their combined total production will increase by 4,896,630 tons, and productivity of wheat, teff, and maize will increase by 20.3%, 13.2%, and 13.8%, respectively, which is slightly higher than the projected population growth (10.9%) during this time. Although these results indicate that Ethiopia is on track to meet the second SDG, government policymakers and other stakeholders can use these findings to ensure sustainable food and nutrition security in Ethiopia.

ANALISIS KOMPARATIF METODE TREND DALAM PERAMALAN PRODUKSI KOPI INDONESIA

Indonesian coffee production is not the largest production in Indonesia, but in In 2020 the value of coffee exports in Indonesia occupies the 9th largest position in the world. This matter illustrates the high economic potential of coffee production. The purpose of this study is to analyze the best Indonesian coffee production forecasting model and get the forecasting value of Indonesian coffee production for the next 60 years. This research using secondary data derived from FAOstat 2022. Data used is data on Indonesian coffee production starting from production in 1961 to production 2020. Using this data, forecasts are carried out for 60 periods, namely: period 61-120. The analysis used is one of trend analysis (linear, quadratic). and exponential) which has the smallest MAPE, MAD and MSD. The quadratic trend model has MAPE is 10, MAD value is 32183, and MSD value is 1807665950 which is smaller than other models. Therefore, it can be concluded that the quadratic linear function model is the best model. With quadratic trend analysis, it is known that Indonesian coffee production over the next 60 periods tends to increase. Highest production predicted in period 120 and the smallest in period 61. The quadratic trend model is the best model for forecasting Indonesian coffee production because it has MAPE, MAD and MSD smallest and forecasting coffee production for the next 60 periods has a value that is tends to increase every period.

Improving Student Learning Outcomes through Mobile Assessment: A Trend Analysis

The integration of technology in learning is one of the popular issues today which is an alternative to solve the problems experienced in the difficulty of achieving maximum learning goals for students. This study aims to examine the effectiveness of the application of mobile assessment in learning activities in elementary schools. This research is a quantitative study using descriptive type. The respondents of this study were elementary school students who were at the fifth-grade level. Data were collected through learning outcomes tests using mobile assessment technology. The data analysis technique used is trend analysis. Research findings indicate that there is a positive trend in student learning outcomes when using mobile assessments in learning activities. Based on the model fit, the findings show that the Quadratic Trend Model is the best model with the smallest measurement error.

Trend analysis of cane crushed and sugar production in Bihar

The goal of this study was to look at the trends in Bihar's cane crushing and sugar production. From 1939 to 2016, time-series data on cane crushed and sugar production was used (78 years). For this reason, three trend analysis models were used: linear, exponential, and quadratic, with the quadratic trend model being the best fit for the current study's trend analysis. A model was considered better if it processed low values of MAPE, MAD, and MSD and high values of R2 7 and R2 8. It was suggested that forecasted values have a positive increasing trend and are very close to that of actual values in Bihar as the next coming ten years are showing a good picture of sugar production. The result revealed that using the established model, it is possible to see that anticipated cane crushed and sugar production has constantly increased trends for the next ten years, from 2017 to 2026. The percentage increase in cane crushed ranged from 1.41 to 1.44 during 2017-26. The percentage increase in sugar production ranged between1.42 to1.68 during 2017-26. Farmers are becoming more interested in producing sugarcane in their fields as a result of the excellent profits it provides. These estimates will aid in the formulation of sound policies in Bihar's sugar and cane crushing industries.

Response of vegetable oil production to monetary policy in Nigeria

Monetary policy is one of the macroeconomic variables that control economy. This is why the study evaluated the response of vegetable oil production to mone- tary policy in Nigeria. Annual time series that spanned for 39 years were obtained from the database of Central Bank of Nigeria and Food and Agriculture Organisa- tion. Data obtained were analysed using descriptive statistics, quadratic trend model and ARDL model. The result indicated that the coefficient of beta 2 (B2) for vegetable oil production (9.15E-05) is positively signed at P-value > 0.1. The implication is that vegetable oil production in Nigeria experienced stagnated growth. The result showed that monetary policy determinants such as inflation rate and broad money supply made significant negative impact on vegetable oil production in Nigeria. Therefore the study strongly recommended that the mone- tary authorities should make available commercial loan for industries involved in the production of vegetable oil at the single-digit prime lending rate to encourage them to grow and expand their vegetable oil agribusiness.

Growth Model and Forecasting Prices of Some Agricultural Products in Bangladesh

The aim of this paper was to explore the appropriate deterministic time series model using the latest selection criteria considering the price pattern of onion, garlic and potato products in Bangladesh (January 2000 to December 2016). It appeared from our analysis that the time series data for the prices of potato was first order homogenous stationary but onion and garlic were found to be the second order stationary. Four different forecasting models namely, linear trend model, quadratic trend model, exponential growth model, and S-curve trend model were used to find the best fitted model for the prices of above mentioned products in the Bangladesh. Three accuracy measures such as mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean squared deviation (MSD) were used for the selection of the best fitted model based on lowest value of forecasting error. Lowest values of these errors indicated a best fitted model. After choosing the best growth model by the latest model selection criteria, prices of selected agricultural commodities were forecasted using the following time-series analysis methods: Simple Exponential Method, Double Exponential Method using the time period from January 2017 to December 2021. The findings of this study would be useful for policy makers, researchers, businessmen as well as producers in order to forecast future prices of these commodities.

When will the Covid-19 pandemic peak?

We carry out some analysis of the daily data on the number of new cases and the number of new deaths by (191) countries as reported to the European Centre for Disease Prevention and Control (ECDC). Our benchmark model is a quadratic time trend model applied to the log of new cases for each country. We use our model to predict when the peak of the epidemic will arise in terms of new cases or new deaths in each country and the peak level. We also predict how long the number of new daily cases in each country will fall by an order of magnitude. Finally, we also forecast the total number of cases and deaths for each country. We consider two models that link the joint evolution of new cases and new deaths.

RAINFALL TREND AND IT IMPACT IN KEFFI NASARAWA STATE

Rainfall is the vital ingredient and factor of soil nourishment on which crops are grown. The basic agricultural practices all over the world depend on rainfed cultivation for their sustainable development. This study focus on the rainfall trend between 2010 -2018 as it affects crop production in Keffi Nasarawa State, Nigeria. The time series analysis was conducted by using appropriate techniques of data collection and analysis. The linear integration model, quadratic trend model, growth curve model and regression analysis was applied to show the correlation between the rainfall and crop production in the study area. It was discovered that the period of rainy months have high correction and significantly fitted the model which indication high crop yields per harvest.

Analysis of statistical models for forecasting PM10 in Kototabang region

PM10 is one of the aerosol particles that can endanger human health. This research conducted by forcasteing for PM10 concentration. Forecasting is an activity of estimating or predicting events in the future, therefore it is necessary to do analysis simple statistical model to know goog results. In this case, several forecasting models are used for the daily PM10 concentration in Kototabang, that is Liniear, Quadratic, and Exponential Trend Model. As the results of this research, monthly forecasting using Linear Trend Model has the highest correlation value in October (-60) with MAD value 0.254, MSE 0.0651, RMSE 0.255, and MAPE 1848.8. Monthly forecasting using the Quadratic Trend Model has the highest correlation value in February (+0.52) with MAD value 0.013, MSE 0.0002, RMSE 0.015, and MAPE 103.934. then, for monthly forecasting using the Exponential Trend Model has the highest value in October (+0.61) with MAD value 0.124, MSE 0.0154, RMSE 0.124, and MAPE 893.484. the output of the forecasting model obtained the best model for forecasting the daily PM10 concentration in Kototabang i.e by using a simple statistical model, linear trend model.

Violence Against Women and Gender Equality in the Philippines: Are they Related?

The Philippine Commission on Women furthered campaign in promoting gender equality in the Philippines which has been a socio-cultural issue over the past years. The strict implementation of the law may have caused Filipino women to speak up and report abuses against them. The study determined the model for the trends, a sample forecast for the next 10 years, and relatedness of the reported crime of violence against women and the Gender Gap Index (GGI) in the Philippines. It retrieved data from the reports of the Philippines Statistics Authority and of the World Economic Forum from 2006 to 2017 through data mining. This study employed descriptive methods via time trend analyses using Minitab R and Symbolic Regression using Eureqa Pro R . Using time trend analysis, the reported cases of violence against women fits the Quadratic Trend Model (MAD=1968) while the quality of gender equality in the Philippines fits the Linear Trend Model (MAD=0.00306). Both reports are forecasted to be increasing for the next 10 years. The symbolic regression analysis provided a forecasted relatedness of the reported cases of violence against women (R=0.9958, R2=0.9911, MAE=0.00067) to GGI. The reported cases of violence against women have a 73% positive association of increasing GGI. Such disclosure supports the necessity of pragmatically real social action from governmental and non-governmental organizations which prevent the occurrence of the crime of violence against women in the future and increase gender equality to promote social welfare as part of the sustainable development goals.

Fitting and Forecasting of Trend Models for the HYV Boro Yields of Dinajpur District

Bangladesh is a densely populated country and the main food of the country is rice. Although the High Yielding Variety (HYV) Bro rice is being cultivated in almost all areas of Bangladesh it is enormously cultivated in Dinajpur district of Bangladesh. Trend is very important to know the HYV Boro rice yields of the country. Hence the objective of this research is to fit and forecast of trend models of HYV Bro rice yields of Dinajpur district for the year from 1971 to 2007. The regression diagnostics revealed in this study indicate that the data is autocorrelated. Hence the Cochrane-Orcutt method was employed to fit such data set. Finally, we forecasted the yields of the three trend models for HYV Boro rice yields of Dinajpur up to 2021. On basis of the regression diagnostics, the quadratic trend model is an appropriate model for this data set and in this model would be useful for the decision makers for their agriculture and food policy formulation.

Forecasting of Cashew Area Harvested, Yield and Production Using Trend Analysis

The study was conducted to examine the trend analysis of area, yield and production for Cashew in Nigeria. The findings of the study are based on data from the years (1961 to 2016) and was taken from the database of FAO (2018). Three Models of trend analysis were applied. The models were Linear Trend Model, Quadratic Trend Model, and cubic trend Model. The most appropriate Model for trend analysis of the present study was Cubic Trend Model based on the highest R2 of (95.76 %), (95.76%) and (88.12%) for cashew area harvested, production and yield respectively, coupled with the lowest residual sum square and mean square error. Forecasting of the data was done up to 2026. The forecasted values were area harvested (409459.07ha -486296.12), yield (24272.09hg/ha – 27422.91hg/ha) and production (990382.68tons-1.127E+06). The study presents an insight to national policy makers regarding this essential crop and provides them with a reference range of values in area harvested, yield and production in future so that they may be able to effectively deal with cashew production in Nigeria.

Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

Comparison of various models on cancer rate and forecasting

In this research work, three models were identified; linear regression model, exponential growth model and the quadratic trend model and the results of the work compared. Data collected from Niger State Hospital Management Board was used for the forecast and the result revealed that the quadratic trend model gave the best fit with mean percentage error of -1.08 and mean absolute percentage error of 43.62. Hence, the quadratic trend model was used for the forecast and the result indicated 27.5 percent annual increase in the future trend and the number of patients with cancer that will visit the hospitals within the study area. This figure is well above the national risk of getting cancer before age 75 years fixed at 10.4 percent. Keywords: Cancer, Tumor, Leukemia, Linear Regression, Mean Percentage Error

Fitting and Forecasting of Trend Models for the HYV Boro Yields of Dinajpur District

Bangladesh is a densely populated country and the main food of the country is rice. Although theHigh Yielding Variety (HYV) Bro rice is being cultivated in almost all areas of Bangladesh it isenormously cultivated in Dinajpur district of Bangladesh. Trend is very important to know the HYVBoro rice yields of the country. Hence the objective of this research is to fit and forecast of trend modelsof HYV Bro rice yields of Dinajpur district for the year from 1971 to 2007. The regression diagnosticsrevealed in this study indicate that the data is autocorrelated. Hence the Cochrane-Orcutt method wasemployed to fit such data set. Finally, we forecasted the yields of the three trend models for HYV Bororice yields of Dinajpur up to 2021. On basis of the regression diagnostics, the quadratic trend model is anappropriate model for this data set and in this model would be useful for the decision makers for theiragriculture and food policy formulation.