000			01919nam a2200217Ia 4500
999			_c7203 _d7203
001			10499
003			SV-SsUGB
005			20211022141646.0
008			180402s1994||||es |||||||||||||| ||spa||
020			_a0132010550
040			_aUGB _cUGB
041	0		_aeng
082	4		_a519.28 _bS677
110	2		_aSPSS _928720
245	1	0	_aSPSS trends 6.0 / _cSPSS
260			_aNew York _bSPSS _c1994
300			_a356 p.
520			_aOVERVIEW. Time series analysis. How trends can help. Other facilities. WORKING WITH SPSS TRENDS. Defining time series data. Historical and validation periods. Case weighting. NOTES ON THE APPLICATIONS. Working through the applications on your PC. The data files. AN INVENTORY PROBLEM: EXPONENTIAL SMOOTHING. Plotting the series. Smoothing the series. When to use exponential smoothing. FORECASTING SALES WITH A LEADING INDICATOR: REGRESSION FORESCASTING. The sales data. Plotting the sales data. Simple regression. A QUALITY-CONTROL CHART: INTRODUCTION TO ARIMA. Plotting the series. Exponential smoothing. Steps in using arima. A RANDOM WALK WITH STOCK RPICES: THE RANDOM-WALK MODEL. Dating the stock series. Plitting the series. Identify the model. TRACKING THE INFLATION RATE: OUTLIERS IN ARIMA ANALYSIS. Estimating the model. Diagnosing the model. Removing the outlier. CONSUMPTION OF SPIRITS: CORRELATED ERRORS IN REGRESION. The durbin-Watson data. Smoothing the series. Regression methods. AN EFFECTIVE DECAY-PREVENTIVE DENTIFRICE: INTERVENTION ANALYSIS. Intervention analysis. Steps and pulses. Diagnosis. TRENDS IN THE OZONE: SEASONAL REGRESSION AND WIGHTED LEAST SQUARES. Replacing the missing data. Dummy-variable regression. TELEPHONE CONNECTIONS IN WISCONSIN: SEASONAL ARIMA. Plotting the series. Lengthen of the series. Length of the series. CYCLES OF HOUSING CONSTRUCTION: INTRODUCTIN TO SPECTRAL ANALYSIS. The housing starts data. The census methods.
542	2		_aSPSS _g1994 _i1994
710	2		_aSPSS _928720
942			_cBK _2ddc