Efficient Methods of Estimating a Regression Equation with Equicorrelated Disturbances

Author(s):  
P. A. V. B. Swamy
Keyword(s):  
2018 ◽  
Vol 19 (2) ◽  
pp. 68
Author(s):  
Raden Sudarwo ◽  
Yusuf Yusuf ◽  
Anfas Anfas

This study aims to determine the influence of learning facilities and student learning motivation towards the independence of student learning. The result of the research shows that there is positive and significant influence of learning tool (X1) on learning independence (Y). It is obtained by tvalue (2,159) with p = 0,034 <0,05 and ttable at 5% significant level with df = 78 equal to 1,991. There is a positive and significant influence of learning motivation (X2) on learning independence (Y). It is obtained tvalue (7,858) with p = 0,000 <0,05 and ttable at 5% significant level with df = 78 equal to 1,991. There is a positive and significant influence of learning facilities (X1) and learning motivation (X2) simultaneously to the independence of learning (Y). This shows the coefficient of double correlation RY (1,2) = 0,746 and R² = 0,557 and price Fvalue equal to 48,980 with p = 0,000 <0,05 and Ftable = 3,11 at 5% significant level. Coefficient value X1 = 0,186 and X2 = 0,647, constant number equal to 8,650 so that can be made regression equation Y = 8,650 + 0,186X1 + 0,647X2. The higher the learning means (X1) and the learning motivation (X2), the higher the learning independence (Y). Coefficient of Determination is R² of 0,557. Means 55,7% learning independence is explained by learning tools and learning motivation. Meanwhile, 44,3% is explained by other factors not discussed in this study. The study concludes that partially, learning facilities and student learning motivation has a positive and significant effect on student independence (self-sufficiency) in learning.  In addition, both learning facility and motivation have a positive and significant effect on student learning independence or sense of self-sufficiency. Penelitian ini bertujuan untuk mengetahui pengaruh fasilitas belajar dan motivasi belajar siswa terhadap kemandirian belajar siswa. Hasil penelitian menunjukkan bahwa ada pengaruh yang positif dan signifikan sanara belajar (X1) terhadap kemandirian belajar (Y). Hal ini diperoleh dengan nilai thitung (2,159) dengan p = 0,034 <0,05 dan ttabel pada 5% tingkat signifikan dengan df = 78 sama dengan 1,991. Ada pengaruh positif dan signifikan motivasi belajar (X2) pada kemandirian belajar (Y). Diperoleh nilai thitung (7,858) dengan p = 0,000 <0,05 dan ttabel pada taraf signifikan 5% dengan df = 78 sebesar 1,991. Ada pengaruh yang positif dan signifikan dari fasilitas belajar (X1) dan motivasi belajar (X2) secara bersamaan terhadap kemandirian belajar (Y). Hal ini menunjukkan koefisien korelasi ganda RY (1,2) = 0,746 dan R² = 0,557 dan harga Fhitung sebesar 48,980 dengan p = 0,000 <0,05 dan Ftabel = 3,11 pada taraf signifikan 5%. Nilai koefisien X1 = 0,186 dan X2 = 0,647, bilangan konstan sebesar 8,650 sehingga dapat dibuat persamaan regresi Y = 8,650 + 0,186X1 + 0,647X2. Semakin tinggi nilai sarana belajar (X1) dan motivasi belajar (X2), semakin tinggi kemandirian belajar (Y). Koefisien Determinasi adalah R² 0,557. Berarti 55,7% kemandirian belajar dijelaskan oleh alat belajar dan motivasi belajar. Sementara itu, 44,3% dijelaskan oleh faktor-faktor lain yang tidak dibahas dalam penelitian ini. Penelitian ini menyimpulkan bahwa secara parsial, baik ketersediaan sarana prasaran belajar dan motivasi berpengaruh positif dan signifikan pada kemandirian mahasiswa, dari dari kedua variable tersebut motivasi mempunyai pengaruh lebih besar. Secara simultan ketersediaan sarana prasarana dalam belajar dan pembelajaran, serta motivasi berpengaruh positif terhadap kemandirian belajar.


Author(s):  
Jantianus Jantianus ◽  
Khairul Khairul

Ease of understanding Accounting Computers in principle is influenced by mastery of Introduction to Accounting in a systematic manner, assuming that it is capable of operating computers properly. To find out the magnitude of the influence in this study taken a sample of introductory Accounting values from a number of first semester 2017 students and the same data sample for students of Computer Accounting (Accurate) courses when they are in the fourth semester 2018. Feasibility until the data is tested by the normality test to find out the distribution of data and by linearity test to obtain linear functions. The data that has been obtained and tested for its feasibility is processed by Linear Regression using SPSS 24. From the results of the research that has been done obtained a regression equation: Y = 67,953 0.35X, which describes each increase in the value of introductory Accounting one unit will affect 0.35 to Computer Accounting value, but in testing the hypothesis that the value of Introduction to Accounting obtained by students does not affect their ability to obtain Computer Accounting values, one of the causes of this is due to the lack of skills of students to operate computers.Keywords: influence, value, ability


2018 ◽  
Author(s):  
Sigit Haryadi

We cannot be sure exactly what will happen, we can only estimate by using a particular method, where each method must have the formula to create a regression equation and a formula to calculate the confidence level of the estimated value. This paper conveys a method of estimating the future values, in which the formula for creating a regression equation is based on the assumption that the future value will depend on the difference of the past values divided by a weight factor which corresponding to the time span to the present, and the formula for calculating the level of confidence is to use "the Haryadi Index". The advantage of this method is to remain accurate regardless of the sample size and may ignore the past value that is considered irrelevant.


2018 ◽  
Vol 3 (1) ◽  
pp. 85-90
Author(s):  
Murwani Wulansari ◽  
Yunidyawati Azlina

This study aims to determine and analyze the effect of promotion costs on income at PT. Bank XYZ. The study was conducted by linear regression that was processed by means of statistical program SPSS16.00. The analysis shows the regression equation as follows: Y = -162982.754 + 247.964X. The result of research shows that there is influence between variable X that is promotion cost with variable Y that is income. However, the effectis not significant because the p-value 0.216 is greater than the 0.05 significance level. Keywords: biaya promosi, penjualan


2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.


Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 464
Author(s):  
Wenjie Zhang ◽  
Tianzhong Zhao ◽  
Xiaohui Su ◽  
Baoguo Wu ◽  
Zhiqiang Min ◽  
...  

Stem analysis is an essential aspect in forestry investigation and forest management, as it is a primary method to study the growth law of trees. Stem analysis requires measuring the width and number of tree rings to ensure the accurate measurement, expand applicable tree species, and reduce operation cost. This study explores the use of Open Source Computer Vision Library (Open CV) to measure the ring radius of analytic wood disk digital images, and establish a regression equation of ring radius based on image geometric distortion correction. Here, a digital camera was used to photograph the stem disks’ tree rings to obtain digital images. The images were preprocessed with Open CV to measure the disk’s annual ring radius. The error correction model based on the least-square polynomial fitting method was established for digital image geometric distortion correction. Finally, a regression equation for tree ring radius based on the error correction model was established. Through the above steps, click the intersection point between the radius line and each ring to get the pixel distance from the ring to the pith, then the size of ring radius can be calculated by the regression equation of ring radius. The study’s method was used to measure the digital image of the Chinese fir stem disk and compare it with the actual value. The results showed that the maximum error of this method was 0.15 cm, the average error was 0.04 cm, and the average detection accuracy reached 99.34%, which met the requirements for measuring the tree ring radius by stem disk analysis. This method is simple, accurate, and suitable for coniferous and broad-leaved species, which allows researchers to analyze tree ring radius measurement, and is of great significance for analyzing the tree growth process.


2020 ◽  
Vol 53 (5) ◽  
pp. 89-96
Author(s):  
Changhui Song ◽  
Lisha Liu ◽  
Yongqiang Yang ◽  
Changwei Weng

2012 ◽  
Vol 503-504 ◽  
pp. 543-547 ◽  
Author(s):  
Ze Ping Xu ◽  
Chuan Lun Yang ◽  
Xin Qing Zhang ◽  
Xiu Zhi Wang ◽  
Bao Sheng Huang

Objective: To establish a common method to detect the content of chitosan oligosaccharide. Methods: Chitosan oligosaccharide was hydrolyzed completely by concentrated hydrochloric acid, and the solution was regulated into neutral with NaOH. Then, determined the absorbance in 525nm, and substituted into the regression equation to determine the results. Results: The results showed there was a good linear relationship when the concentration of chitosan oligosaccharide ranged from 0.02 mg/mL to 0.12 mg/mL, r2 = 0.999. The average recovery of chitosan oligosaccharide samples was 99.25%. Conclusion: The method is sensitive, accurate and simple. It is applied to determine of the content of chitosan oligosaccharide.


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