Food and Nutrition: Customs and CultureNelson Thornes |
Contents
FOOD AND CULTURE | 1 |
Table 14 Impact of cultural subsystems on food values | 16 |
Self | 21 |
CHAPTER 2 | 30 |
CHAPTER 3 | 51 |
Social functions of food | 78 |
was burned on cremation pyres food was also put | 100 |
SEXUAL DIVISION OF LABOUR | 106 |
Density | 53 |
Brazilian | 60 |
0000 | 76 |
3 | 105 |
AM | 139 |
2 | 140 |
3 | 168 |
4 | 175 |
CHAPTER 6 | 120 |
important way of transmitting culturally valued knowledge from genera | 140 |
CHAPTER 7 | 150 |
do humans and that they are not merely means to | 154 |
Whole grains | 161 |
CHAPTER 8 | 165 |
CHAPTER 9 | 184 |
CHAPTER 10 | 206 |
8 | 221 |
References | 234 |
249 | |
火 | 256 |
262 | |
Preface | 265 |
Contents | 265 |
UNIVARIATE EXPLORATORY | 3 |
2 | 49 |
K | 206 |
5 | 239 |
00 | 249 |
5fDD | 294 |
6 | 311 |
7 | 353 |
Simulated GARCH11 with same parameters as in | 369 |
3 | 389 |
429 | |
433 | |
435 | |
437 | |
441 | |
445 | |
Springer Texts in Statistics continued from page ii | |
Common terms and phrases
absolute deviations regression algorithm analysis assume auto-correlation function bandwidth behaviour bivariate BLRet called Cauchy distribution chapter choice coefficients column components compute cooking copula correlation cultural data frame data set defined density estimation diet dietary discussion eaten eating Ekiben ENRON equation example explanatory variables fact fast fast-food Figure filtering food habits formula GARCH Gaussian given gives histogram illustrate implied volatility interest rate kernel regression least absolute deviations least squares linear models linear regression meal mean meat methods multivariate nonlinear nonparametric normal distribution notation Nutrition observations option parameters polynomial prediction problem produce projection pursuit Q-Q plot quantiles random variables reason residuals S-Plus S-Plus function sample scatterplot simulation social society splines stationary statistical stochastic Subsection taboos tails tion tsplot values variance vector volatility white noise women Wt}t yield curve zero