Applied Regression Analysis for Business

Welc, Jacek; Welc,; Esquerdo, Pedro J. Rodriguez

doi:10.1007/978-3-319-71156-0

Cited by 14 publications

(12 citation statements)

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“…where n � i is the number of observations, β 0 is the yintercept, which is a constant term, β n represents the slope coefficients for each input variable, and ε is the model deviation. PR is a nonlinear regression model in which the output (response variable) is modeled in proportion to a power (polynomial) of the inputs (explanatory variables) [30].…”

Section: Development Of Modelsmentioning

confidence: 99%

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Wickramasinghe

Weliwatta

Ekanayake

et al. 2021

Journal of Mathematics

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This paper presents the application of a multiple number of statistical methods and machine learning techniques to model the relationship between rice yield and climate variables of a major region in Sri Lanka, which contributes significantly to the country’s paddy harvest. Rainfall, temperature (minimum and maximum), evaporation, average wind speed (morning and evening), and sunshine hours are the climatic factors considered for modeling. Rice harvest and yield data over the last three decades and monthly climatic data were used to develop the prediction model by applying artificial neural networks (ANNs), support vector machine regression (SVMR), multiple linear regression (MLR), Gaussian process regression (GPR), power regression (PR), and robust regression (RR). The performance of each model was assessed in terms of the mean squared error (MSE), correlation coefficient (R), mean absolute percentage error (MAPE), root mean squared error ratio (RSR), BIAS value, and the Nash number, and it was found that the GPR-based model is the most accurate among them. Climate data collected until early 2019 (Maha season of year 2018) were used to develop the model, and an independent validation was performed by applying data of the Yala season of year 2019. The developed model can be used to forecast the future rice yield with very high accuracy.

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Section: Development Of Modelsmentioning

confidence: 99%

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Wickramasinghe

Weliwatta

Ekanayake

et al. 2021

Journal of Mathematics

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“…Analisis regresi linier berganda ialah suatu alat analisis dalam ilmu statistik yang berguna untuk mengukur hubungan antara lebih dari dua peubah (Welc dan Esquerdo, 2018).…”

Section: Analisis Regresi Linier Bergandaunclassified

“…Pengujian parameter dalam model regresi bertujuan untuk mengetahui apakah parameter tersebut telah menunjukkan hubungan yang nyata antara variabel bebas dan variabel respon (Welc dan Esquerdo, 2018). Terdapat dua tahap pengujian yaitu uji serentak (simultan) dan uji parsial (individu).…”

Section: Pengujian Parameter Regresiunclassified

“…Adanya autokorelasi pada error mengindikasikan bahwa ada satu atau beberapa faktor (variabel) penting yang memengaruhi variabel terikat Y yang tidak dimasukkan ke dalam model regresi (Welc dan Esquerdo, 2018). Hipotesis: 0 ∶ 1 = 2 = ⋯ = = 0 (tidak ada autokorelasi) H1 : minimal ada satu ≠ 0 (ada autokorelasi)…”

Section: ) Uji Asumsi Independenunclassified

“…Multikolinearitas merupakan korelasi atau hubungan yang kuat diantara variabelvariabel bebas dalam persamaan regresi linear berganda (Welc dan Esquerdo, 2018 (Fox, 2016).…”

Section: ) Uji Multikolinieritasunclassified

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Faktor–Faktor yang Memengaruhi Optimalisasi Penangkapan Ikan dengan Metode Transformasi Box Cox pada Regresi Linier Berganda

Wilujeng

2018

JIEMS

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In general, the ratio of the ocean and land area on this earth is 70 to 30. Many potentials of water can be used to promote human well-being. Even today, many developed countries from all over the world are competing to advance their maritime activities and their auspices for the country's interests so that the role of the sea becomes dominant for developing of the countries. One of the most common marine activities people do is sea fishing. the marine and fisheries sector has enormous potential to become a national economist. In Indonesia, many people depend on the fisheries sector. So, in this research that will be discussed is to determine the model of the number of marine fish production in East Java Province with Box-Cox Transformation method on Multiple Linear Regression to get significant control variable on the amount of sea fish production in East Java Province. The result of regression using Box-Cox transform shows that some variables such as the number of fishermen, fishing equipment, and boat or fishing boat motor have a significant influence on R2 89.3%. With this model, it is hoped to be one strategy to increase the number of fishing through the selection of appropriate control variables.

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Predictive Analytics in Corporate Financial Management: A Case Study on Earnings Forecasting with a Global Logistics Service Provider

Kovacevic,

Waterstraat

2023

Contributions to Finance and Accounting

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Applied Regression Analysis for Business

Cited by 14 publications

References 0 publications

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Modeling the Relationship between Rice Yield and Climate Variables Using Statistical and Machine Learning Techniques

Faktor–Faktor yang Memengaruhi Optimalisasi Penangkapan Ikan dengan Metode Transformasi Box Cox pada Regresi Linier Berganda

Predictive Analytics in Corporate Financial Management: A Case Study on Earnings Forecasting with a Global Logistics Service Provider

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