Nghiên cứu sản xuất sản phẩm rượu vang dừa

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B e !G ' I*G("*G% G 1%IGDZ1*"* e S *"*G% + R ># - > R $ $: \ { OXo! 9PcPoW! jj / R< + \ RR : / \ * D % * ,S%D '*G% %e UZ, "I, "Z, S% 1G ! *W% * %D1 %e e S *"*G% + R ># - > $ \ #> jW 9VV j_ fff jP 9VVe H e+ \ \6 R R \> n%SID WG V > e \ V X! * IZ R 1G* "*G% " D % *I%%\ + \ R $ W N R >3 #8 K jX 9VVfff > \ #V jO 9VVfff o e #V c 9VVfff c! 9VVfff f > R>H R H6 GVe # Ve # VH> \ V V f RV G>\G >fVf >† \ X$ H 0* ?0* R R R !9 H T G> H K [# R>T #> \45 4N 64N VietNamNet Bridge - Ba Thanh, a farmer in southern Ben Tre province, Viet Nam's "coconut country," has made wine from coconut milk and exported the new product to several Asian and European countries A prescribed dose of this kind of wine will give drinkers silky hair and digestive stability without having headache The wine can be added to soda or heated Ba Thanh succeeded making the wine from coconut milk in late 2005 His wine plant, located in An Thanh commune, Mo Cay district, turned out 15,000 bottles a month for domestic use and export The wine has won favour of many customers in the UK, Thailand, Cambodia and Laos R R $9 - K T Q G N 45 \Y Q Multiple Regression Analysis Dependent variable: ethanol Standard T Parameter Estimate Error Statistic P-Value CONSTANT -146,867 52,3749 -2,80416 0,0127 Bx 5,60999 2,03491 2,75688 0,0140 Bx*Bx -0,0718936 0,0348395 -2,06356 0,0557 Bx*Giong -0,0342448 0,0388134 -0,882293 0,3907 Bx*Nhietdo oC -0,0492188 0,0465761 -1,05674 0,3063 Bx*pH -0,308125 0,186304 -1,65388 0,1176 Giong 0,895468 1,52246 0,58817 0,5646 Giong*Giong -0,0783549 0,0241941 -3,23859 0,0051 Giong*Nhietdo oC 0,0514323 0,0388134 1,32512 0,2037 Giong*pH -0,205729 0,155254 -1,32512 0,2037 Nhietdo oC 5,80867 2,38493 2,43557 0,0269 Nhietdo oC*Nhietd -0,0978311 0,0348395 -2,80805 0,0126 Nhietdo oC*pH 0,120625 0,186304 0,647462 0,5265 pH 9,93842 7,95809 1,24884 0,2297 pH*pH -0,855298 0,557432 -1,53435 0,1445 Analysis of Variance Source Sum of Squares Df Mean Square F-Ratio P-Value Model 19,5682 14 1,39773 2,52 0,0398 Residual 8,88558 16 0,555349 Total (Corr.) 28,4538 30 R-squared = 68,7719 percent R-squared (adjusted for d.f.) = 41,4473 percent Standard Error of Est = 0,745217 Mean absolute error = 0,406886 Durbin-Watson statistic = 2,35447 The StatAdvisor The output shows the results of fitting a multiple linear regression model to describe the relationship between ethanol and 14 independent variables The equation of the fitted model is ethanol = -146,867 + 5,60999*Bx - 0,0718936*Bx*Bx - 0,0342448*Bx*Giong - 0,0492188*Bx*Nhietdo oC - 0,308125*Bx*pH + 0,895468*Giong 0,0783549*Giong*Giong + 0,0514323*Giong*Nhietdo oC - 0,205729*Giong*pH + 5,80867*Nhietdo oC - 0,0978311*Nhietdo oC*Nhietdo oC + 0,120625*Nhietdo oC*pH + 9,93842*pH - 0,855298*pH*pH R Since the P-value in the ANOVA table is less than 0.05, there is a statistically significant relationship between the variables at the 95% confidence level The R-Squared statistic indicates that the model as fitted explains 68,7719% of the variability in ethanol The adjusted R-squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 41,4473% The standard error of the estimate shows the standard deviation of the residuals to be 0,745217 This value can be used to construct prediction limits for new observations by selecting the Reports option from the text menu The mean absolute error (MAE) of 0,406886 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file Since the DW value is greater than 1.4, there is probably not any serious autocorrelation in the residuals In determining whether the model can be simplified, notice that the highest P-value on the independent variables is 0,5646, belonging to Giong Since the P-value is greater or equal to 0.10, that term is not statistically significant at the 90% or higher confidence level Consequently, you should consider removing Giong from the model 4N \Y 245 R K 2> ] [ 2> ^ Q J 3< R5 OOm : 245 R K 2> \Z ƒ 45 H G N 45 \Y Q G5 J \Z R5 W J45 K 3< \>T OOm W J>[ 45 8> Q K Multiple Regression Analysis Dependent variable: ethanol Standard T Parameter Estimate Error Statistic P-Value CONSTANT -114,128 43,4866 -2,62443 0,0152 Bx 3,98148 1,92771 2,06539 0,0503 Bx*Nhietdo oC -0,0492188 0,047906 -1,0274 0,3149 Bx*Bx -0,0666007 0,0356214 -1,86968 0,0743 Giong*Giong -0,0772509 0,0240187 -3,21628 0,0038 Giong*Nhietdo oC 0,032917 0,00982177 3,35144 0,0028 Nhietdo oC 6,08469 2,30179 2,64346 0,0145 Nhietdo oC*Nhietd -0,0925382 0,0356214 -2,59782 0,0161 - T R Analysis of Variance Source Sum of Squares Df Mean Square F-Ratio P-Value Model 14,9409 2,13442 3,63 0,0088 Residual 13,5128 23 0,587515 Total (Corr.) 28,4538 30 R-squared = 62,5095 percent R-squared (adjusted for d.f.) = 38,0558 percent Standard Error of Est = 0,766495 Mean absolute error = 0,535716 Durbin-Watson statistic = 1,7807 The output shows the results of fitting a multiple linear regression model to describe the relationship between ethanol and independent variables The equation of the fitted model is ethanol = -114,128 + 3,98148*Bx - 0,0492188*Bx*Nhietdo oC 0,0666007*Bx*Bx - 0,0772509*Giong*Giong + 0,032917*Giong*Nhietdo oC + 6,08469*Nhietdo oC - 0,0925382*Nhietdo oC*Nhietdo oC Since the P-value in the ANOVA table is less than 0.01, there is a statistically significant relationship between the variables at the 99% confidence level K UQ >3 H = JZ &$ >7 >7 ]m^ H4 > J K H \ "# N R R N H H >T G N J4 G N # T Y n SST = ( yi − y )2 [ 2Y 45 J R>3 i =1 n SS E = ( yi − yˆ i )2 [ H H 2Y 45 i =1 n SS R = ( yˆ i − y )2 = SST − SS E [ 2Y i =1 R2 = SS R = SST [ [ 2Y 2Y 45 45 Q J R>3 45 Q \Z \ 2Y ] > ^ ?U ?l G N >T E T + 0U@D A+B :M N< "# H H & -Y ! 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