EXCHANGE RATE DETERMINATION USING A LINEAR REGRESSION MODEL: A MONETARY APPROACH / Bepaling van die wisselkoers deur middel van 'n linere regressie model: 'n monetre benadering.

Agrekon ◽  
1992 ◽  
Vol 31 (1) ◽  
pp. 33-35
Author(s):  
E J Goedecke ◽  
V Y Dushmanitch ◽  
G F Ortmann
Keyword(s):  
Exchange Rate ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Exchange Rate Determination ◽  
Monetary Approach

APPLE EXPORT FROM POLAND IN THE YEARS 1995-2015

2018 ◽  
Vol XX (2) ◽  
pp. 105-110
Author(s):  
Paweł Kraciński
Keyword(s):  
Exchange Rate ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Positive Impact ◽  
Import Demand ◽  
Model Research ◽  
Producer Prices ◽  
The Exchange Rate

The aim of the research was to determine the factor affecting the volume of apple export from Poland in the years 1995-2015. The studies used a linear regression model. Research has shown that the most important factors determining the volume of apple exports from Poland were: the volume of Russian apple import, apples harvest in Poland, the exchange rate of PLN/USD and producer prices in the previous year. The increase in import demand for apples in Russia by 1 thousand tons caused, on average, a rise in export from Poland by 585 tons. The increase in apple harvest in Poland by 1 thousand tons resulted in an increase in export by 292 tons. The zloty depreciation and higher producer prices in previous years also had a positive impact on the volume of apple export from Poland.

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>.

scholarly journals The Analysis of the Influence Factors of China’s Outbound Tourism Market

2018 ◽  
Vol 228 ◽  
pp. 05004
Author(s):  
He Liu
Keyword(s):  
Exchange Rate ◽  
Quantitative Analysis ◽  
Linear Regression ◽  
Qualitative Analysis ◽  
Regression Model ◽  
Linear Regression Model ◽  
Influence Factors ◽  
Multiple Linear Regression Model ◽  
Literature Study ◽  
Econometric Methods

Outbound tourism gets special attention from tourism academic circles and tourism companies recently. And now China has become the world’s largest outbound tourism consumer. So this paper is written to research the influence factors of China’s outbound tourism market, providing reference for the study of China’s outbound tourism market. The literature study have found that the number of China’s outbound tourism related to the duration of their leisure trips, exchange rate, residents’ deposits and the number of tourism agency. By using of the econometric methods and R-studio, we build a multiple linear regression model. In the end, we get a conclusion by quantitative analysis of data and qualitative analysis of tourism’s law. We found that there are linear relations exists in logarithm of the number of China’s outbound tourism, the residents’ deposits ,the logarithm of duration of their leisure trips and the logarithm of number of tourism agency.

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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