Analysis method for linear regression model with unequally spaced autoregression series error

2011 ◽  
Author(s):  
Ma Xiaobing ◽  
Chang Shihua
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Analysis Method ◽  
Unequally Spaced

scholarly journals Kesejahteraan, pengeluaran pemerintah sektor kesehatan dan harapan hidup di Provinsi Aceh: Sebuah pendekatan data panel

2019 ◽  
Vol 17 (1) ◽  
pp. 31-37
Author(s):  
Nanda Rahmi ◽  
Afdhal Putera
Keyword(s):  
Linear Regression ◽  
Life Expectancy ◽  
Regression Model ◽  
Linear Regression Model ◽  
Significant Relationship ◽  
Health Spending ◽  
Quantitative Approach ◽  
Domestic Product ◽  
Analysis Method

This study to investigate the effect of health spending and Gross Regional Domestic Product (GRDP) on life expectancy in Aceh. The data are collected for 23 districts in Aceh over 4 years from 2012-2016. The analysis method uses a quantitative approach with applying a linear regression model with data panel. The findings of this study indicated that health spending and GRDP have a positive and significant effect on life expectancy. Furthermore, GRDP has a positive and significant relationship with life expectancy.

scholarly journals PENGARUH INFLASI, BI RATE, KURS DAN INDEKS DOW JONES INDUSTRIAL AVERAGE TERHADAP JAKARTA ISLAMIC INDEX (JII)

Media Ekonomi ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 87
Author(s):  
Muhammad Rasyid Ridha ◽  
Harmaini Harmaini
Keyword(s):  
Exchange Rate ◽  
Linear Regression ◽  
Regression Model ◽  
Foreign Exchange ◽  
Significant Influence ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Analysis Method ◽  
Dow Jones Industrial Average ◽  
Research Show

<em>This research discusses the influence of inflation, BI Rate, Exchange rate (IDR/USD) and Dow Jones Industrial Average</em>. <em>The analysis method used is multiple linear regression model with α = 5%. With EViews 9.0 applications.</em> <em>The results of this research show that inflation, BI Rate, Foreign Exchange and Dow Jones Industrial Average simultaneously had significant influence towards on the Jakarta Islamic Index (JII). Meanwhile, partially Inflation had positive and significant influence towards on the JII. BI Rate partially had negative and significant influence towards on the JII. But Exchange rate (IDR/USD) partially do not influence on the JII and Dow Jones partially had positive and significant influence towards on the JII</em>.

Faktor-Faktor Yang Mempengaruhi Permintaan Tahu Jawa

2020 ◽  
Vol 2 (2) ◽  
pp. 76-85
Author(s):  
Hotman tuah ◽  
Marlan ◽  
Fitria Nazar
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Confidence Level ◽  
Household Income ◽  
Family Income ◽  
Family Members ◽  
Analysis Method ◽  
Income Family

Tujuan penelitian ini adalah untuk menganalisis bagaimana pengaruh harga tahu jawa, pendapatan rumah tangga, jumlah tanggungan dan harga tempeterhadap permintaan tahu jawa di Kota Pematangsiantar.Metode analisis data yang digunakan adalah model regresi linier berganda yang diolah dengan program SPSS 23 dengan pengujian hipotesis yang terdiri dari koefisien (R2), uji F, dan uji t. Harga tahu jawa, pendapatan keluarga, jumlah anggota keluarga, dan harga tempe  mampu  menjelaskan variasi permintaan sebesar 46,6%, sedangkan  sisanya sebesar 53,4% & dijelaskan oleh faktor-faktor lain yang tidak disertakan dalam persamaan. Secara bersama-sama,variabel harga tahu jawa,pendapatan rumah tangga, dan jumlah anggota keluarga, dan harga tempe  berpengaruh secara tidak nyata terhadap permintaan tahu jawa. Secara parsial, pendapatan konsumen berpengaruh nyata terhadap permintaan tahu jawa pada tingkat kepercayaan 95% Nilai thitung (2,216) dan hipotesis dapat diterima. sedangkan  harga tahu jawa, harga tempe, jumlah anggota keluarga tidak berpengaruh nyata terhadap permintaan tahu jawa.    ABSTRACT  The purpose of this research is to analyze how the influence of Javanese tofu price, household income, number of dependents and price of tempeh to the request of tofu Jawa in Pematangsiantar city. The data analysis method used is a double linear regression model that is processed with the SPSS 23 program with hypothesis testing consisting of coefficient (R2), test F, and T test. Javanese tofu prices, family income, family members, and Tempe prices were able to explain the variation in demand by 46.6%, while the remaining of 53.4% & explained by other factors not included in the equation. Together, variable prices of Javanese tofu, household income, and the number of family members, and the price of Tempe effect is not noticeable to the demand for Javanese tofu. Partially, the consumer income has a real impact on the demand for Javanese tofu at a confidence level of 95% of the Thitung value (2.216) and the hypothesis acceptable. While the price of Tofu Jawa, Tempe Price, the number of family members does not affect the demand for Javanese tofu.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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