Forecasting Cloud Storage Consumption Using Regression Model

Mbata David

ISBN 10: 3668660409 / ISBN 13: 9783668660403

Published by GRIN Verlag, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Scientific Study from the year 2017 in the subject Computer Science - Commercial Information Technology, grade: A, , language: English, abstract: The primary aim of the study was to develop a regression model for forecasting monthly cloud storage consumption. Second, to ascertain if the month is a reliable predictor of cloud storage capacity consumed. The model was developed using Minitab18 statistical software. The dependent variable was cloud storage capacity consumed, while the independent variable was the month of cloud storage consumption. The model was validated by checking the assumptions of regression to establish its suitability in making future predictions. Twelve-month data sets was analyzed to make future prediction for each passing month. The model made predictions with near accuracy from the actual cloud storage data consumed in each month. The model determines the intervals of monthly storage consumption. The study concluded that the month is a globally significant linear predictor of cloud storage capacity consumed over a period.

Seller Inventory # 9783668660403

Bibliographic Details

• Title: Forecasting Cloud Storage Consumption Using Regression Model
• Publisher: GRIN Verlag
• Publication Date: 2018
• Language: English
• ISBN 10: 3668660409
• ISBN 13: 9783668660403
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 2D millimeters 210 millimeters width by 148 millimeters height by 2 millimeters depth
• Weight: 45 grams

About this title

Synopsis

Scientific Study from the year 2017 in the subject Computer Science - Commercial Information Technology, grade: A, language: English, abstract: The primary aim of the study was to develop a regression model for forecasting monthly cloud storage consumption. Second, to ascertain if the month is a reliable predictor of cloud storage capacity consumed. The model was developed using Minitab18 statistical software. The dependent variable was cloud storage capacity consumed, while the independent variable was the month of cloud storage consumption. The model was validated by checking the assumptions of regression to establish its suitability in making future predictions. Twelve-month data sets was analyzed to make future prediction for each passing month. The model made predictions with near accuracy from the actual cloud storage data consumed in each month. The model determines the intervals of monthly storage consumption. The study concluded that the month is a globally significant linear predictor of cloud storage capacity consumed over a period.

About the Author

Mr Ziraba Abdallah holds Masters of Education degree in Information and Communication Technology, Integration of ICT in Education, ICT and Educational Management, and ICT pedagogy from Makerere University. He is currently a lecturer at the ICT University USA, Cameroon Campus. A seasoned educationist with over 10 years of teaching experience at Secondary school and several academic institutions including Universities, Mr Ziraba has taught students from diverse social and cultural backgrounds. He possesses excellent administrative, verbal communication and written skills along with constructive and effective teaching methods that promote a stimulating learning environment. Contacts: Email: abdallah.ziraba@ictuniversity.org Phone: ]237651625643