scholarly journals Identifying High Significance Input Factors in Strawberry Production using Linear Model

2020 ◽  
Vol 9 (2) ◽  
pp. 1680-1684
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Nutrient Solution ◽  
Linear Regression Model ◽  
Ph Value ◽  
Correlation Method ◽  
Linear Regression Method ◽  
High Significance ◽  
Different Types ◽  
Growth Production

This study is focused on identifying the high significance of input factors in strawberry growth and production using a linear regression model. Greenhouse strawberry cultivation is increasing so fast due to the high demand for strawberry and farmers are also taking different types of technics for greenhouse cultivation to get high productions of strawberry. This study aims to increase the production of strawberries in order to maximize the profits from the cultivation of strawberries and also to fulfill the demand for strawberries. The strawberry data consist of average strawberry productions (AvgSP), electric conductivity (EC), potential of Hydrogen (PH) value, greenhouse inside temperature (Temp), greenhouse inside humidity, CO2 , nutrient solution with water, and supply of water nutrient solution. To find out the relationship among each input factor we use the correlation method and after that based on the correlation we make different types of combination of input factors. In this study, we use the linear regression method to find out the R2 value and significance factors of different combinations of input factors. For the linear regression model we take average strawberry production as output and different combination of input factors as input. In result and discussion, we concluded the high significance input factors in strawberry growth production.

scholarly journals Proving the Efficiency of Alternative Linear Regression Model Based on Mean Square Error (MSE) and Average Width using Aquaculture Data

2019 ◽  
Vol 8 (2S3) ◽  
pp. 377-381
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Mean Square Error ◽  
Linear Regression Model ◽  
Regression Method ◽  
Alternative Methods ◽  
Linear Regression Method ◽  
Mean Square ◽  
Average Width ◽  
Alternative Method

Multiple linear regressions (MLR) model is an important tool for investigating relationships between several response variables and some predictor variables. This method is very powerful and commonly used in finance, economic, medical, agriculture and many more. The main objective of this paper is to compare mean square error (MSE) and the average width between alternative linear regression models and linear regression model. The alternative method in this study is a combination of four methods, namely multiple linear regression method, the bootstrap method, a robust regression method and fuzzy regression through the construction of algorithms by using SAS software. Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods

ASYMPTOTIC THEORY FOR M-ESTIMATORS OVER A CONVEX KERNEL

1998 ◽  
Vol 14 (4) ◽  
pp. 387-422 ◽  
Author(s):  
Miguel A. Arcones
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Limit Distribution ◽  
Linear Regression Model ◽  
Asymptotic Theory ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Taylor Expansions ◽  
Wide Range ◽  
Different Types

We study the convergence in distribution of M-estimators over a convex kernel. Under convexity, the limit distribution of M-estimators can be obtained under minimal assumptions. We consider the case when the limit is arbitrary, not necessarily normal. If some Taylor expansions hold, the limit distribution is stable. As an application, we examine the limit distribution of M-estimators for the multivariate linear regression model. We obtain the distributional convergence of M-estimators for the multivariate linear regression model for a wide range of sequences of regressors and different types of conditions on the sequence of errors.

scholarly journals Pengaruh Modal Terhadap Pendapatan Pedagang (Studi kasus: Pasar Angso Duo Kota Jambi)

2020 ◽  
Vol 11 (2) ◽  
pp. 137
Author(s):  
Sudirman Sudirman ◽  
Ubaidillah Ubaidillah
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Method ◽  
Primary Data ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Positive Effect ◽  
Market Trader

research aims to find out the effect of variable capital on the income of traders in Angso Duo Market Jambi City. The study used primary data by disseminating qusioners to 92 respondents. The method used in this study is a simple linear regression method. while econoetry analysis using a simple linear regression model is carried out to identify the variables that affect. Variables used are capital variables as free variables and income variables as bound variables. The results of the analysis showed that the capital had a positive effect on the earnings of Angso Duo Market traders. This means that if the market trader increases his capital, then the income of the market trader will also increase.

scholarly journals EVALUATION OF MULTIPLE LINEAR REGRESSION MODEL TO OBTAIN DBH OF TREES USING DATA FROM A LIGHTWEIGHT LASER SCANNING SYSTEM ON-BOARD A UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 449-454 ◽  
Author(s):  
M. V. Machado ◽  
A. M. G. Tommaselli ◽  
V. M. Tachibana ◽  
R. P. Martins-Neto ◽  
M. B. Campos
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Statistical Tests ◽  
Low Cost ◽  
Multiple Linear Regression Model ◽  
Urban Trees ◽  
Scanning System ◽  
Linear Regression Method

<p><strong>Abstract.</strong> Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R<sup>2</sup> adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7&amp;thinsp;cm.</p>

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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