Mean Squared Error Performance of Simple Linear Regression Conditional upon the Outcome of Pretesting the Intercept

1992 ◽  
Vol 46 (2) ◽  
pp. 89 ◽  
Author(s):  
Timothy G. Gregoire ◽  
Alexandros A. Arabatzis ◽  
Marion R. Reynolds
Keyword(s):  
Linear Regression ◽  
Mean Squared Error ◽  
Simple Linear Regression ◽  
Error Performance ◽  
Squared Error

scholarly journals Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
Imam Halimi ◽  
Wahyu Andhyka Kusuma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Composite Index ◽  
T Test ◽  
Sliding Windows ◽  
Root Mean Squared Error ◽  
Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

scholarly journals Linear Regression Algorithm in Machine Learning through MATLAB

2021 ◽  
Vol 9 (12) ◽  
pp. 989-995
Author(s):  
Kalva Sindhu Priya
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Critical Part ◽  
Squared Error ◽  
Master Level ◽  
Predicted Model ◽  
The Given

Abstract: In the present scenario, it is quite aware that almost every field is moving into machine based automation right from fundamentals to master level systems. Among them, Machine Learning (ML) is one of the important tool which is most similar to Artificial Intelligence (AI) by allowing some well known data or past experience in order to improve automatically or estimate the behavior or status of the given data through various algorithms. Modeling a system or data through Machine Learning is important and advantageous as it helps in the development of later and newer versions. Today most of the information technology giants such as Facebook, Uber, Google maps made Machine learning as a critical part of their ongoing operations for the better view of users. In this paper, various available algorithms in ML is given briefly and out of all the existing different algorithms, Linear Regression algorithm is used to predict a new set of values by taking older data as reference. However, a detailed predicted model is discussed clearly by building a code with the help of Machine Learning and Deep Learning tool in MATLAB/ SIMULINK. Keywords: Machine Learning (ML), Linear Regression algorithm, Curve fitting, Root Mean Squared Error

Mean-squared error experiment design for linear regression models*

2012 ◽  
Vol 45 (16) ◽  
pp. 1629-1634 ◽  
Author(s):  
Diego Eckhard ◽  
Håkan Hjalmarsson ◽  
Cristian R. Rojas ◽  
Michel Gevers
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Mean Squared Error ◽  
Experiment Design ◽  
Linear Regression Models ◽  
Squared Error

scholarly journals Implementation of Multiple Linear Regression Methods as Prediction of Village Spending on Village Financial Management System

Kursor ◽  
2020 ◽  
Vol 10 (2) ◽  
Author(s):  
Nisa Hanum Harani ◽  
Hanna Theresia Siregar ◽  
Cahyo Prianto
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Financial Management ◽  
Mean Squared Error ◽  
Sampling Technique ◽  
Absolute Error ◽  
Budget Process ◽  
Squared Error ◽  
Financial Management System ◽  
The Village

The realization of village welfare and improvement of Village development can be started from the financial management aspects of the village.  The village government has authority ranging from planning, implementation, reporting to accountability.  There are two important variables as the financial aspects, there is village income, and village expenditure.  The village budget process is a plan that will be compiled systematically. Planning has an association with predictions which is an indication of what is supposed to happen and predictions relating to what will happen.   To provide a  good village budget planning the village budget prediction feature is required. This prediction feature is done using data mining which is modeled i.e. multiple linear regression algorithm.  The variable is selected using a purposive sampling technique and the sample count is 29 villages.  Dependent variables are village Expenditure as Y, and independent variables i.e. village funds as  X1 and village funding allocation as X2.   The best values as validation were gained in the 3rd fold with a correlation coefficient of 0.8907, Mean Absolute Error value of 87209395.37, the value of Root Mean Squared Error of 114867675.6, Roll Absolute  Error  (RAE) Percentage was 42 %, and  Root  Relative  Squared Error was 44 %.

Sign in / Sign up

Export Citation Format

Share Document