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
. 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 :.4 :.5. : Data view.6 Variable view.7.... : - :.8. : - : View >> toolbars >>data editor. : View >> toolbars >> customize >> new save 280
toolbar name toolbar properties : edit. file categories. ok continue tools : variable view 281
var00001 name.1 var00001. type type.2 variable type. : : 8 width numeric # decimal places 2 width.. decimal. comma # Dot #.198.487.724,901 : 282
scientific notation #.24.7E5 10 5 24.7 10 E Date # : $ Dollar # :.$,128,345.36 283
custom currency #. Edit >> option >> currency : negative values suffix all values : ok apply suffix 284
string # characters. values variable label :.3 variable view label value labels value. : 285
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
. ( / / ) :1. spss ( / / ) III II I 0.32 0.30 0.31 0.426 0.41 0.418 0.52 0.50 0.51 0.51 0.53 0.52 0.63 0.600 0.615 3 / 3 / 3 28 / 3 28 / 0.05 0.1 0.05 0.1. % :2. spss 287
III II I 0.52 0.50 0.51 0.53 0.48 0.51 0.48 0.50 0.49 :3. SPSS 9.87 9.77 10.99 11.00 11.51 11.44 13.08 13.12 13.78 13.70 14.00 14.02 14.30 14.00 I II I II I II I II I II I II I II ( ) 0 1 2 3 4 5 6 288
compute transform : 7 :. : 4 (BHA) 3 (FE2) 2 (FE1) 1 (FE1+BHA) ( ) 6.21 6.21 6.21 6.21 6.21 1 6.01 6.13 6.22 6.28 5.89 5 5.89 6.01 6.09 6.19 5.50 10 5.51 5.65 5.72 5.98 5.20 15. (2006). : : : spss 289
compute variable transform sum target variable one numeric expression numeric expression ok...sum 290
sum/4 : average 291
. 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
: OK : % 30 M m %25 %5 M a %10 M f %20 M p %40 M c %35 %5 %10 %20 %30 : 293
C p =1.424 M c + 1.549 M p + 1.675 M f + 0.837 M a + 4.187M 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
295
: Frequencies frequencies << descriptive statistics << analyze 296
: 17 18 17 16 18 17 16 18 16 17 16 : : frequencies << descriptive statistics << analyze : variables : statistics 297
continue : chart continue with normal curve Histograms : ok frequency 298
299
Descriptive... :. 0.000106 0.000122 0.000137 0.000162 0.00018 0.000201 0.00021 0.000221 0.001 0.0015 0.002 0.003 0.004 0.005 0.006 0.007 : Descriptive << Descriptive statistics << analyze : 300
option variables : : ok save standardized as variable 301
حيث x القيمة الداخلة و µالمتوسط الحسابي و z=(x-µ)/σ z. x المعياري للمتغير σاالنحراف 302
Correlation 1. R.. 1-. 1-1 : Bivariate << Correlate << Analyze : mg/l sec. : << Correlate << Concentration Time 131 0 110 60 92 120 71 180 49 240 29 300 analyze. : : Bivariate 303
variables : continue mean and standard deviation option Person ok tow tailed significant : 304
tow tailed significant=.000 1-0.01.. %1 :2. ( ) ( ) 250 270 365 370 0 230 245 325 310 10 220 221 290 270 20 210 210 265 255 30 197 200 225 195 60 305
: : variable << bivariate << correlate << analyze : ok 306
. 0.01 0.05 :. 307
: m : M 101 0.01 201 0.02 302 0.03 402 0.04 503 0.05 604 0.06 704 0.07 805 0.08 905 0.09 1006 0.10 Linear << regression << analyze : :independent dependent 308
statistics :... X ZPRED Y ZRESID PLOTS : CONTINUE 309
. ) ( ). 1 (. ENTER 1 R 2 R. 310
. 0.05 0.000 Sig. COEFFICIENTS t Std. Error B 0.05 Beta Sig. B : residual values y=0.133+10057.576x m :X :y predicted values. 311
312
Multiple linear regression : 100/ 100/ : : 100/ 100/ 100/ 100/ 0.09 10.5 32.5 25 6.0 0.10 10.5 57.5 25 6.0 0.15 10.5 32.5 37.5 6.0 0.13 11 45 25 7.5 0.11 10 45 25 7.5 0.12 10 45 15 7.5 0.13 9.5 32.5 16.6 9.0 0.15 10.5 32.5 16.6 9.0 0.13 10.5 32.5 25 9.0...(2009).. : : 313
dependent linear << regression << analyze independent statistics.... : 314
: ok 315
ANOVA..0.705 R Enter : coefficients y=-0.048+0.012x1+0.001x2+0.00035x3+0.006x4 316
100/ :X2 100/ :x1 ( ) :y. :X4 100/ :X3. Stepwise T one sample T test T.1. 500 399 499 490 500 504 : 10 500.7 500 489 498 501. : : sample T test compare means analyze test test vaiables one : value 317
488.07 sig.=0.264 one sample T test 11.93-0.05. 318
PAIRED SAMPL T TEST T.( ) : 296 300 298 260 290 300 200 299 297 300 259 300 298 199.. A ) : : (B PAIRED SAMPL T compare means analyze PAIRED TEST :.VARIABLE 319
: OK 320
paired paired samples statistics paired sample. samples correlatione 0.05 sig.=0.522 test. Independent samples T test T 13 : 1.3 1.5 1.8 1.5 1.2 1.1 0.9 1.5 1 1.2 : ( ).1.5 1.8 1.4 1.8 1.1 : ( ) 15.1 1.3 1.2 1 1 1.2 1.7 1.6 1.2 1.2 1.4 1.5 1.7.%5 : : 321
INDEPENDENT SAMPL compare means analyze.test VARIABLE T TEST : 322
: DEFINE GROUPS continue. 2 1 :.ok 323
SIG.=0.911 F=0.013 Eevens test T sig. (2-tailed) =0.741 Equal variances assumed. 0.05..Kolmogrove-Smirov : : 281.12 542.16 763 943.7 1024 1004 883.53 602.4 361.44 324
363 500 700 823.29 905 843.37 763 602 321.3 : : sample K-S Nonparametric tests Analyze Test variable list. normal 325
: ok 326
sig.=0.983 sig.=0.942. 0.05 One way analysis of variance.. B A : C. A 8 9 8 7 9 B 7 7 8 9 7 C 5 5 6 8 7 A. : 3 C 2 B 1 327
<< one way ANOVA << compare means << Analyze POST HOC. factor dependent list : option LSD 328
: ok 329
0.05 ANOVA POST HOC TEST. 0.05 sig. B A LSD (mean *. C B C A difference (I-J)) sig.. 0.05. Two way analysis of variance.... : 10) (V/cm 60 40 20) : ( o C 50 30 20 V/cm 40 V/cm 60 V/cm المكررات 10 o C 30 o C 50 o C o C 10 30 o C 50 o C 10 o C 30 o C 50 o C I 0.51 0.62 0.71 0.55 0.64 0.73 0.61 0.76 0.91 II 0.53 0.65 0.70 0.57 0.65 0.71 0.60 0.73 0.90 III 0.52 0.64 0.73 0.56 0.66 0.75 0.63 0.76 0.87 330
: : 331
<< univariate << general linear model << analyze dependent variable ( ) : fixed LSD post hoc test for Post Hoc :.continue 332
display means for: Option : continue 333
: ok 334
335
336
337
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 0.071 0.11 0.084 0.14 0.077 0.13 0.088 0.15 0.061 0.092 0.054 0.11 II 0.064 0.12 0.082 0.13 0.074 0.12 0.086 0.14 0.60 0.096 0.055 0.099 III 0.073 0.13 0.080 0.15 0.70 0.14 0.087 0.15 0.062 0.098 0.047 0.11 : : 338
<< univariate << general linear model << analyze 339 dependent variable : ( ) fixed
OPTION LSD post Hoc : 340
OK CONTIUE : 341
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.0.05 346
المصادر www.cb4a.com.2000..spss.(2007).176. بري عدنان ماجد عبد الرحمن (2005). طرق الحسابات االحصائية باستخدام اكسيل. http://www.4shared.com/document/5_r_zeuz- /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. 13. 83-91 Baranyi, J. and Roberts, T. A. (1994) A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277-294. 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, 419-431. 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 2000. 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 2:157 167 Paul Singh, R.(1996). Computer Applications in Food Technology:Use of Spreadsheets in Graphical, Statistical, and Process Analysis. Elsevier Science & Technology Books. P.300. 347