E jurnal Matematika

38-44

Authors: ANAK AGUNG ISTRI AGUNG CANDRA ISWARI, I WAYAN SUMARJAYA, I GUSTI AYU MADE SRINADI

The aim of this study is to apply Bayesian simple linear regression using noninformative prior. The data used in this study is 30 observational data with error generated from normal distribution. The noninformative prior was formed using Jeffreys’ rule. Computation was done using the Gibbs Sampler algorithm with 10.000 iteration. We obtain the following estimates for the parameters, α_0=1,698045 with 95% Bayesian confidence interval (0,775775; 2,626025), β=2,999468 with 95% Bayesian confidence interval (2,948; 3,052), and σ^2=0,697669 with 95% Bayesian confidence interval (0,375295; 1,114). These values are not very different compared to the actual value of the parameters, which are α_0=2 and β=3 Keywords: Bayesian regression, noninformative prior, Jeffreys’rule, the Gibbs Sampler algorithm

53-63

Authors: AGUSTINUS ANGELAUS ETE, NI LUH PUTU SUCIPTAWATI, DESAK PUTU EKA NILAKUSMAWATI

At this time, almost everyone once to consume instant noodles. The high interest of public on the instant noodles should be balanced with enough knowledge about the noodles and its nutritional content, either on it’s instant noodles which have similar nutrient content and nutrient content that become identifier of each this group of noodles. The method can be used to obtain information on several brands of instant noodles that have similar nutrient content and nutrient content type that become identifier of each group of instant noodles is biplot analysis. Biplot analysis can show mie and nutrient content types simultaneously in a two-dimension plot. So that from a plot shows noodles and nutritional content types simultaneously, so that obtain information about the instant noodle that have similar nutrient content and nutrient content types into identifier of each group of instant noodles. This study was used 33 brands of instant noodles as observed objects with the type of nutrient content were observed there were nine. This study aims to find out some instant noodles that have similar nutrient content and nutrient content type that become identifier of each group of instant noodles. From the biplot analysis, obtained six groups of instant noodles with different identifier variables. Keywords: Biplot Analysis, Similarity, Type of Nutrition

75-85

Authors: I PUTU JERYANA, I PUTU EKA NILA KENCANA, G.K. GANDHIADI

Regression analysis is used to study the relationship between dependent (response) variable with one or more independent (causal) variables. While response data were censored, then Tobit regression model could be applied. According to Greene (2003), censored data were data with incomplete observation or the dependent variable has a value of zero, while for the other observations have particular value. This research aimed to model dairy milk’s consumption from households at Bali Province. By using data from Survey Sosial Ekonomi Nasional (SUSENAS) or Social Economy’s National Survey (SENS) for year 2012, 615 households were selected as sampling unit using simple random sampling technique, and found 123 households who consumed dairy milk. The independent variables in our model were last education level completed by head of household’s (X1), head of household’s work (X2), age of head of household’s (X3), amount of expenditure for food consumption’s (X4), number of household members (X5), and household income (X6), the response variable was budget for buying dairy milk (Y). From six independent variables, is found only last education level by head household and amount of expenditure for food consumption had siginficant effect on Y’s. The final Tobit regression model were obtained using AIC (Akaike Information Criterion) method is Y = -3314724 + 565429,7 X1 + 0,014278 X4 with pseudo R2 as much as 16.79 per cent. Keywords: Akaike Information Criterion, Censored Data, Pseudo R2, Tobit Regression