Description

Book Synopsis
This is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of sectors.

Table of Contents
1 Introduction 1.1 Overview1.2 SAS Enterprise Guide2 Basic Statistics and Regression Models2.1 Calculating new variables2.2 Normality test2.3 Simple linear regression2.4 Multiple linear regression 3 Time Series3.1 Smoothing methods3.2 ARIMA model3.3 Regression model with AR errors4 Panel Models4.1 Fixed effect4.2 Random effectReferences

Time Series Analysis Using SAS Enterprise Guide

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    £49.49

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    RRP £54.99 – you save £5.50 (10%)

    Order before 4pm today for delivery by Fri 12 Jun 2026.

    A Paperback by Timina Liu, Shuangzhe Liu, Lei Shi

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      View other formats and editions of Time Series Analysis Using SAS Enterprise Guide by Timina Liu

      Publisher: Springer Verlag, Singapore
      Publication Date: 20/02/2020
      ISBN13: 9789811503207, 978-9811503207
      ISBN10: 9811503206

      Description

      Book Synopsis
      This is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of sectors.

      Table of Contents
      1 Introduction 1.1 Overview1.2 SAS Enterprise Guide2 Basic Statistics and Regression Models2.1 Calculating new variables2.2 Normality test2.3 Simple linear regression2.4 Multiple linear regression 3 Time Series3.1 Smoothing methods3.2 ARIMA model3.3 Regression model with AR errors4 Panel Models4.1 Fixed effect4.2 Random effectReferences

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