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Multivariate Methods and Forecasting with IBM® SPSS® Statistics 1st ed. 2017 [Hardback]

  • Formāts: Hardback, 178 pages, height x width: 235x155 mm, weight: 4203 g, 80 Illustrations, color; 53 Illustrations, black and white; XVII, 178 p. 133 illus., 80 illus. in color., 1 Hardback
  • Sērija : Statistics and Econometrics for Finance
  • Izdošanas datums: 14-Jul-2017
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319564803
  • ISBN-13: 9783319564807
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Multivariate Methods and Forecasting with IBM® SPSS® Statistics 1st ed. 2017Palielināt
  • Formāts: Hardback, 178 pages, height x width: 235x155 mm, weight: 4203 g, 80 Illustrations, color; 53 Illustrations, black and white; XVII, 178 p. 133 illus., 80 illus. in color., 1 Hardback
  • Sērija : Statistics and Econometrics for Finance
  • Izdošanas datums: 14-Jul-2017
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319564803
  • ISBN-13: 9783319564807
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319564807.html
Keywords:
This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naļve techniques. This part also covers hot topics suchas Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).
Part I Forecasting Models
1 Multivariate Regression
3(24)
1.1 The Assumptions Underlying Regression
4(7)
1.1.1 Multicollinearity
4(1)
1.1.2 Homoscedasticity of the Residuals
5(3)
1.1.3 Normality of the Residuals
8(1)
1.1.4 Independence of the Residuals
8(3)
1.2 Selecting the Regression Equation
11(1)
1.3 Multivariate Regression in IBM SPSS Statistics
12(7)
1.4 The Cochrane-Orcutt Procedure for Tackling Autocorrelation
19(8)
2 Other Useful Topics in Regression
27(32)
2.1 Binary Logistic Regression
28(12)
2.1.1 The Linear Probability Model (LPM)
28(3)
2.1.2 The Logit Model
31(1)
2.1.3 Applying the Logit Model
32(1)
2.1.4 The Logistic Model in IBM SPSS Statistics
33(6)
2.1.5 A Financial Application of the Logistic Model
39(1)
2.2 Multinomial Logistic Regression
40(1)
2.3 Dummy Regression
40(7)
2.4 Functional Forms of Regression Models
47(12)
2.4.1 The Power Model
49(3)
2.4.2 The Reciprocal Model
52(3)
2.4.3 The Linear Trend Model
55(4)
3 The Box-Jenkins Methodology
59(22)
3.1 The Property of Stationarity
59(7)
3.1.1 Trend Differencing
60(2)
3.1.2 Seasonal Differencing
62(1)
3.1.3 Homoscedasticity of the Data
63(1)
3.1.4 Producing a Stationary Time Series in IBM SPSS Statistics
63(3)
3.2 The ARIMA Model
66(1)
3.3 Autocorrelation
67(7)
3.3.1 ACF
67(3)
3.3.2 PACF
70(1)
3.3.3 Patterns of the ACF and PACF
71(1)
3.3.4 Applying an ARIMA Model
71(3)
3.4 ARIMA Models in IBM SPSS Statistics
74(7)
4 Exponential Smoothing and Naive Models
81(16)
4.1 Exponential Smoothing Models
81(7)
4.2 The Naive Models
88(9)
Part II Multivariate Methods
5 Factor Analysis
97(10)
5.1 The Correlation Matrix
98(1)
5.2 The Terminology and Logic of Factor Analysis
98(4)
5.3 Rotation and the Naming of Factors
102(3)
5.4 Factor Scores in IBM SPSS Statistics
105(2)
6 Discriminant Analysis
107(10)
6.1 The Methodology of Discriminant Analysis
107(1)
6.2 Discriminant Analysis in IBM SPSS Statistics
108(2)
6.3 Results of Applying the IBM SPSS Discriminant Procedure
110(7)
7 Multidimension Scaling (MDS)
117(18)
7.1 Types of MDS Model and Rationale of MDS
119(1)
7.2 Methods for Obtaining Proximities
120(1)
7.3 The Basics of MDS in IBM SPSS Statistics: Flying Mileages
121(5)
7.4 An Example of Nonmetric MDS in IBM SPSS Statistics: Perceptions of Car Models
126(1)
7.5 Methods of Computing Proximities
127(3)
7.6 Weighted Multidimensional Scaling in IBM SPSS, INDSCAL
130(5)
8 Hierchical Log-linear Analysis
135(18)
8.1 The Logic and Terminology of Log-linear Analysis
135(3)
8.2 IBM SPSS Statistics Commands for the Saturated Model
138(4)
8.3 The Independence Model
142(2)
8.4 Hierarchical Models
144(4)
8.5 Backward Elimination
148(5)
Part III Research Methods
9 Testing for Dependence
153(6)
9.1 Introduction
153(2)
9.2 Chi-Square in IBM SPSS Statistics
155(4)
10 Testing for Differences Between Groups
159(8)
10.1 Introduction
159(1)
10.2 Testing for Population Normality and Equal Variances
160(2)
10.3 The One-Way Analysis of Variance (ANOVA)
162(2)
10.4 The Kruskal-Wallis Test
164(3)
11 Current and Constant Prices
167(6)
11.1 HICP and RPI
167(1)
11.2 Current and Constant Prices
168(5)
References 173(2)
Index 175
Abdulkader Aljandali, Ph.D., is Senior Lecturer at Regents University London. He currently leads the Business Forecasting and the Quantitative Finance module at Regents in addition to acting as a Visiting Professor for various universities across the UK, Germany and Morocco. Dr Aljandali is an established member of the Higher Education Academy (HEA) and an active member of the British Accounting and Finance Association (BAFA).