176 Index. G Gradient, 4, 17, 22, 24, 42, 44, 45, 51, 52, 55, 56

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1 References Aljandali, A. (2014). Exchange rate forecasting: Regional applications to ASEAN, CACM, MERCOSUR and SADC countries. Unpublished PhD thesis, London Metropolitan University, London. Aljandali, A. (2016). Quantitative Analysis and IBM SPSS Statistics: A Guide for Business and Finance (1st ed.). New York: Springer. Brace, N., Kemp, R., & Sneglar, R. (2016). IBM SPSS for psychologists (6th ed.). Basingstoke: Palgrave Macmillan. Bryman, A., & Cramer, D. (2011). Quantitative data analysis with IBM SPSS 17, 18 and 19: A guide for social scientist (1st ed.). London: Routledge. Charry, K., & Coussement, K. (2016). Marketing research with IBM SPSS statistics: A practical guide (1st ed.). Oxon: Routledge. Coshall, J. T. (2008). SPSS for windows, a user s guide: Volume 2. Unpublished manuscript, London Metropolitan University, London. Elliott, A. C., & Woodward, W. A. (2015). IBM SPSS by example: A practical guide to statistical data analysis (2nd ed.). Los Angeles: SAGE. Field, A. (2013). Discovering statistics using IBM SPSS statistics and sex, drugs and rock n roll (4th ed.). London: SAGE. Macinnes, J. (2016). An introduction to secondary data analysis with IBM SPSS statistics (1st ed.). Los Angeles: SAGE. Pallant, J. (2013). SPSS survival manual: A step by step guide to data analysis using SPSS (5th ed.). Maidenhead: Open University Press. Salkind, N. J. (2014). Statistics for people who (think they) hate statistics (5th ed.). Los Angeles: SAGE. Wagner, W. E. (2016). Using IBM SPSS statistics for research methods and social science statistics (6th ed.). Los Angeles: SAGE. Springer International Publishing AG 2017 A. Aljandali, Multivariate Methods and Forecasting with IBM SPSS Statistics, Statistics and Econometrics for Finance, DOI /

2 Index A Auto Regressive Integrated Moving Average (ARIMA) applications, autoregressive parameter, 66 computation, 66 exponential smoothing class, 88 in IBM SPSS Statistics, mixed models, 66 on stationary data, 66 1 order, 84 zero orders, 82 Autocorrelation, 19 25, 60, 67 74, 79 B Backward selection, 11 Bartlett s method, 68 Binary logistic regression dependent variable, 28 functional forms, independent variable, 28 logit model, LPM, 28, 30 Bivariate correlations dialogue box, 12 Box-Jenkins approach, 62 ACF, homoscedasticity, 63 PACF, 70 patterns of the ACF and PACF, 71 phase procedure, 59 predictable movements, 59 property of stationarity, seasonal differencing, 62 trend differencing, C Chi square, 111 Cochrane-Orcutt (C-O) procedure, 9, Constant prices, Consumer price index (CPI), 168 Correlation matrix, 13, 98 regressor variables, 5, 12 Current prices, D Differences between groups, 108, 111 explore dialogue box, 160 Kruskal-Wallis (KW) test, 164, 165 Levene test, 162 multiple comparisons procedure, 163 normality and equal variance assumptions, 160 normality output, 162 one-way analysis of variance (ANOVA), plots dialogue box, 161 Post Hoc tests, 163 Shapiro-Wilk statistic, 161 test of homogeneity, 162 types of shares, 159 Discriminant analysis classification dialogue box, 110 Confusion Matrix, 114 define ranges dialogue box, 109 discriminant Analysis dialogue box, 109 functions at group centroids, 111 high population group, 115 IBM SPSS output, 111 LIBRARY.SAV, 108 Springer International Publishing AG 2017 A. Aljandali, Multivariate Methods and Forecasting with IBM SPSS Statistics, Statistics and Econometrics for Finance, DOI /

3 176 Index Discriminant analysis (cont.) low population group, 114 Mahalanobis distance, 114 methodology, save dialogue box, 115 Standardized Canonical Discriminant Function Coefficients, 113 structure matrix, 113 variable POPN, 108 working file, 116 Discriminant function coefficients, 108 Dummy regression categorical variables, 40 compute variable dialogue box, 46 cross product term, 47 cutting tool, 42 data file, 47 equation, 42 gradient, 46 intercept, 45 least squares, 43 output, 44 plot of residuals, 45 qualitative variable, 41 regression equation, 44 scatterplot, 43 tool type, 42 TOOLLIFE, 43 types of tool, 42 Durbin-Watson (D-W) test, 9, 13 E Estimation, 3 5, 9, 20, 52, 59, 71, 89, 168 Exponential Smoothing models Brown s linear trend, 84 damped trend, 84 dialogue box, 82, 83 forecast error, 81 Holt s linear trend, 82 options dialogue box, 86 parameters dialogue box, 84 retention, 81 saving dialogue box, 85 simple model, 82 simple seasonal, 84 single parameter model, 81, 86 stock levels, 82, 83 website page, 82 winters additive, 85 winters multiplicative, 85 F Factor analysis, 107 factor loadings, 97 factor score coefficients, 97 in IBM SPSS Statistics, observed correlations, 97 rotation, terminology and logic, variables, 97 Forecasting, 3, 36, 59, 79, 113 Forward selection, 11, 148 G Gradient, 4, 17, 22, 24, 42, 44, 45, 51, 52, 55, 56 H Harmonized Indices of Consumer prices (HICP), 167, 168 Homoscedasticity, 5, 6, 8, 63 Hosmer-Lemeshow (HL) test, 34, 38 I IBM SPSS Statistics, 34 Individual scaling Euclidean distance model (INDSCAL), 131 K Kruskal-Wallis (KW) test, 164, 165 L Likelihood ratio chi-square, 141 Linear probability model (LPM), 28, 30 Linear regression dialogue box, 13 Logistic regression, financial application, 39 IBM SPSS Statistics Classification Table, 37, 38 data file, 33, 36 dialogue box, 34 FERUSE variable, 34 options dialogue box, 35 probabilities, 36 save dialogue box, 34, 35 variables, 37 multinomial logistic regression, 40

4 Index 177 Logit model, Log-linear analysis, backward elimination, 148, 149 chi-square approach, 135 IBM SPSS statistics 2-way interaction, way interaction, way loglinear model, 147 chi-square statistic, 146 independence model, model building, 139 model dialogue box, 139 model selection, 139, 140 options dialogue box, 139, 140, 146 partial associations, 146 saturated model, 141 unsaturated model, 145 logic and terminology, Log-linear analysis, 158 M Multicollinearity, 4 5 Multidimension scaling (MDS) airmiles data, 121, 125 business applications, 119 car models, computing proximities, data format, 122 dimensions, 117 hidden structure/underlying dimensionality, 117 hypothetical MDS perceptual map, 118, 119 intercity flying mileages, 124 matrix of distances, 117 methods, model dialogue box, 122 multidimensional Scaling dialogue box, 121 options dialogue box, 123 raw data versus distances, 123, 126 S-stress, 124 types, underlying dimensions, 118 weighted multidimensional scaling, Multinomial logistic regression, 40 Multivariate regression autocorrelation, 13 backward selection, 11 C-O procedure, 24 correlations, 13 Durbin-Watson statistic, 24 forward selection, 11 gradients, 16 histogram, 18 homoscedasticity assumption, 5 8, 13 IBM SPSS Statistics, independence of the residuals, 8 11 iterations, 23 linear dependence, 3, 13 multicollinearity, 4 5, 15, 19 normality of the residuals, 8 null hypothesis, 17 observed vs predicted values, 21 plots dialogue box, 15 predictor variables, 11 regression coefficients, 3 regression residuals over time, 22 residuals, 19 save dialogue box, 16 SPSS Syntax Editor, 23 standardized residuals, 18, 20 stepwise regression procedure, 17 unstandardized predicted values, 18 variables, 3 X-ray exposures, 15 N Naïve models ARIMA, 88 computation, 91 compute variable dialogue box, 89 graphs, 92 growth rate, 88 LAG12, 91 LAG24, 91 lagged values, 88, 90 observed and predicted stock levels, 87 predicted values, 87 residual values, 92 time period, 88 time series models, 88 Negative autocorrelation, 9 Non-accelerating inflation rate of unemployment (NAIRU), 53 Normality, 8, 144, O One-way analysis of variance (ANOVA), 162, 163 Ordinary least squares (OLS), 47

5 178 Index P Partial autocorrelation coefficients (PACF), 70 Power model, R Reciprocal model, Retail price index (RPI), 167, 168 S Sample autocorrelation function (ACF), Seasonal differencing, 62 Single parameter model, 81 SPSS Syntax Editor, 23 Standardized, 4, 8, 13, 17, 18, 40, 44, 108 Stationarity, 59 63, 153 in IBM SPSS Statistics, Stepwise selection, 11 Syntax, 5, 22, 23 T Testing for dependence, business-related research projects, 153 chi-square statistics, 155 cell display dialogue box, 157 crosstabs dialogue box, 156 crosstabulation, 158 IBM SPSS Statistics, 155 standardized residuals, 155 statistics dialogue box, 157 contingency table, 154 crosstabulation or contingency table, 154 expected frequency, 154 multiplication law, 154 observed frequency, 154 SERVQUAL literature, 153 W Weirdness index (WI), 132