1 / 6

Applied Statistics

Exercise 1 Week 2 Tenderness of Pork Mai Stahl Madsen Helene Drejer Grejsen Mathilde Guene Mihaela Frincu. Applied Statistics. Data. pH Low High Pork's 12 12 Chilling 12 tunnel 12 fast 12 tunnel 12 fast Tenderness all 48 pieces

wynona
Download Presentation

Applied Statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Exercise 1 Week 2 Tenderness of Pork Mai Stahl Madsen Helene Drejer Grejsen Mathilde Guene Mihaela Frincu Applied Statistics

  2. Data pH Low High Pork's 12 12 Chilling12 tunnel 12 fast 12 tunnel 12 fast Tenderness all 48 pieces Scope: Investigate the impact of pH and chilling method on the tenderness of pork.

  3. Relations to Example 2 As in the example 2 in the course, the variables are paired because we experiment chilling methods on pieces of meat which come from a same pork. We will only use the linear mixed model because it will bring more information than simply a one-way paired ANOVA

  4. Calculations > library(nlme) >tender.model<-lme(tender~ph*chilling,random=~1|factor(pork), data=tenderness) > summary(tender.model) Linear mixed-effects model fit by REML Data: tenderness AIC BIC logLik 153.8353 164.5405 -70.91766 Random effects: Formula: ~1 | factor(pork) (Intercept) Residual StdDev: 1.118207 0.6813447 Fixed effects: tender ~ ph * chilling Value Std.Error DF t-value p-value (Intercept) 7.010000 0.3780010 22 18.544922 0.0000 phlow -1.545833 0.5345742 22 -2.891710 0.0085 chillingtunnel 0.212500 0.2781578 22 0.763955 0.4530 phlow:chillingtunnel 0.165000 0.3933745 22 0.419448 0.6790 Correlation: (Intr) phlow chllng phlow -0.707 chillingtunnel -0.368 0.260 phlow:chillingtunnel 0.260 -0.368 -0.707 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.6328124 -0.3070833 -0.0451965 0.4136859 1.9149072 Number of Observations: 48 Number of Groups: 24

  5. Calculations >tender.model2<-lme(tender~ph+chilling,random=~1|factor(pork), data=tenderness) > summary(tender.model2) Linear mixed-effects model fit by REML Data: tenderness AIC BIC logLik 151.9680 161.0013 -70.98401 Random effects: Formula: ~1 | factor(pork) (Intercept) Residual StdDev: 1.121918 0.6690282 Fixed effects: tender ~ ph + chilling Value Std.Error DF t-value p-value (Intercept) 6.968750 0.3645086 23 19.118202 0.0000 phlow -1.463333 0.4970746 22 -2.943891 0.0075 chillingtunnel 0.295000 0.1931318 23 1.527454 0.1403 Correlation: (Intr) phlow phlow -0.682 chillingtunnel -0.265 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.72124802 -0.32631449 -0.02872637 0.42284245 2.00333113 Number of Observations: 48 Number of Groups: 24

  6. Results The combined effect of pH and chilling method on tenderness of pork was modelled by a linear mixed model. The linear mixed model was used to include the random variation from pork. The reported p-values correspond to t-tests and were evaluated at a 5% significance level. All analyses were made in R version 2.14.0 (www.r-project.org). Results: There was no effect modification of chilling method on tenderness due to pH (p=0.679). There was no significant effect of chilling method (p=0.14), but the influence of the pH was significant (p=0.0075)

More Related