Statistical product and service solution

Transcript

1 SPSS Statistical product and service SPSS SPSS solution SPSS Statistics 17.5 << program << start : :. Run the tutorial.1. Type in data.2. Run an existing query.3 278

2 . Create new query using data base wizard.4. Open an existing data source.5 cancel Type in data cases SPSS Statistics Data Editor.cell variables : :.1 : : :.2 279

3 : :.3 :.4 :.5. : Data view.6 Variable view : - :.8. : - : View >> toolbars >>data editor. : View >> toolbars >> customize >> new save 280

4 toolbar name toolbar properties : edit. file categories. ok continue tools : variable view 281

5 var00001 name.1 var type type.2 variable type. : : 8 width numeric # decimal places 2 width.. decimal. comma # Dot # ,901 : 282

6 scientific notation #.24.7E E Date # : $ Dollar # :.$,128,

7 custom currency #. Edit >> option >> currency : negative values suffix all values : ok apply suffix 284

8 string # characters. values variable label :.3 variable view label value labels value. : 285

9 add value label value 1 value label value 2 value label value 3 add.ok value. change value label. remove measure :.4 data view. scale : 286

10 . ( / / ) :1. spss ( / / ) III II I / 3 / 3 28 / 3 28 / % :2. spss 287

11 III II I :3. SPSS I II I II I II I II I II I II I II ( )

12 compute transform : 7 :. : 4 (BHA) 3 (FE2) 2 (FE1) 1 (FE1+BHA) ( ) (2006). : : : spss 289

13 compute variable transform sum target variable one numeric expression numeric expression ok...sum 290

14 sum/4 : average 291

15 . A t exp h T T m C : T : h=5000, A=1.57, t=30, m= 256, C p = 3950, T 2 =90, T 1 = 20 : T : T T exp h A t m C. compute variable transform numeric expression TF target variable. numeric expression 292

16 : OK : % 30 M m %25 %5 M a %10 M f %20 M p %40 M c %35 %5 %10 %20 %30 : 293

17 C p =1.424 M c M p M f M a M m. compute variable transform numeric expression cp target variable If. numeric expression include if case satisfied condition compute variable continue Mm>30 cp cp ok if.%30 294

18 295

19 : Frequencies frequencies << descriptive statistics << analyze 296

20 : : : frequencies << descriptive statistics << analyze : variables : statistics 297

21 continue : chart continue with normal curve Histograms : ok frequency 298

22 299

23 Descriptive... : : Descriptive << Descriptive statistics << analyze : 300

24 option variables : : ok save standardized as variable 301

25 حيث x القيمة الداخلة و µالمتوسط الحسابي و z=(x-µ)/σ z. x المعياري للمتغير σاالنحراف 302

26 Correlation 1. R : Bivariate << Correlate << Analyze : mg/l sec. : << Correlate << Concentration Time analyze. : : Bivariate 303

27 variables : continue mean and standard deviation option Person ok tow tailed significant : 304

28 tow tailed significant= %1 :2. ( ) ( )

29 : : variable << bivariate << correlate << analyze : ok 306

30 :. 307

31 : m : M Linear << regression << analyze : :independent dependent 308

32 statistics :... X ZPRED Y ZRESID PLOTS : CONTINUE 309

33 . ) ( ). 1 (. ENTER 1 R 2 R. 310

34 Sig. COEFFICIENTS t Std. Error B 0.05 Beta Sig. B : residual values y= x m :X :y predicted values. 311

35 312

36 Multiple linear regression : 100/ 100/ : : 100/ 100/ 100/ 100/ (2009).. : : 313

37 dependent linear << regression << analyze independent statistics.... : 314

38 : ok 315

39 ANOVA R Enter : coefficients y= x x x x4 316

40 100/ :X2 100/ :x1 ( ) :y. :X4 100/ :X3. Stepwise T one sample T test T : : : sample T test compare means analyze test test vaiables one : value 317

41 sig.=0.264 one sample T test

42 PAIRED SAMPL T TEST T.( ) : A ) : : (B PAIRED SAMPL T compare means analyze PAIRED TEST :.VARIABLE 319

43 : OK 320

44 paired paired samples statistics paired sample. samples correlatione 0.05 sig.=0.522 test. Independent samples T test T 13 : : ( ) : ( ) %5 : : 321

45 INDEPENDENT SAMPL compare means analyze.test VARIABLE T TEST : 322

46 : DEFINE GROUPS continue. 2 1 :.ok 323

47 SIG.=0.911 F=0.013 Eevens test T sig. (2-tailed) =0.741 Equal variances assumed Kolmogrove-Smirov : :

48 : : sample K-S Nonparametric tests Analyze Test variable list. normal 325

49 : ok 326

50 sig.=0.983 sig.= One way analysis of variance.. B A : C. A B C A. : 3 C 2 B 1 327

51 << one way ANOVA << compare means << Analyze POST HOC. factor dependent list : option LSD 328

52 : ok 329

53 0.05 ANOVA POST HOC TEST sig. B A LSD (mean *. C B C A difference (I-J)) sig Two way analysis of variance.... : 10) (V/cm ) : ( o C V/cm 40 V/cm 60 V/cm المكررات 10 o C 30 o C 50 o C o C o C 50 o C 10 o C 30 o C 50 o C I II III

54 : : 331

55 << univariate << general linear model << analyze dependent variable ( ) : fixed LSD post hoc test for Post Hoc :.continue 332

56 display means for: Option : continue 333

57 : ok 334

58 335

59 336

60 337

61 0.05. Three way analysis of variance.. ( ) : hr -1 ( ) (m/sec. 4 2) : تجفيف شمسي طبيعي مجفف كھربائي مجفف شمسي التكرارات 2 m/s باميا باميا مشمش باميا مشمش مشمش 4 m/s 2 m/s 4 m/s 2 m/s 4 m/s 2 m/s 4 m/s 2 m/s 4 m/s 2 m/s 4 m/s I II III : : 338

62 << univariate << general linear model << analyze 339 dependent variable : ( ) fixed

63 OPTION LSD post Hoc : 340

64 OK CONTIUE : 341

65 342

66 343

67 344

68 345

69

70 المصادر بري عدنان ماجد عبد الرحمن (2005). طرق الحسابات االحصائية باستخدام اكسيل. /learning_word_2010_in_arabic_-.html 97.(1998) Abakarov, A.(2011). Software packages for food engineering needs. 2nd International Conference on Biotechnology and Food Science Baranyi J., Tamplin M. (2002). ComBase: A Common Database on Microbial Responses to Food Environments. J. Food Prot. (In press). Baranyi J., Ross T., Roberts T.A. and McMeekin T. (1996). The effects of overparameterisation on the performance of empirical models used in Predictive Microbiology. Food Microbiol Baranyi, J. and Roberts, T. A. (1994) A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, Gerard M. V.(2008). Excel 2007 for Scientists and Engineers. Holy Macro! Books.pp259. Gibson A. M., Baranyi J., Pitt I., Eyles M. J. and Roberts T. A. (1994).Predicting fungal growth: the effect of water activity on four species of Aspergillus. International Journal of Food Microbiology 23, Holman,J.P.(2001).Heat transfer:9 th edition.mcgraw Hill,Inc.,New york. Informa on Technology service (2001). Introduction to using macros in Microsoft Excel Guide 127 Version 1.2 IPCBEE vol.7 (2011). IACSIT Press, Singapore Mark D. N. ; U. Lesmes ; M. G. Corradini and M. Peleg (2010). Wolfram Demonstrations: Free Interactive Software for Food Engineering Education and Practice. Food Eng Rev 2: Paul Singh, R.(1996). Computer Applications in Food Technology:Use of Spreadsheets in Graphical, Statistical, and Process Analysis. Elsevier Science & Technology Books. P