Publications by Laboratory Exercise No. 5
Stat 142 SARIMA Models
Introduction So far, we have restricted our attention to non-seasonal data and non-seasonal ARIMA models. However, ARIMA models are also capable of modelling a wide range of seasonal data. A seasonal ARIMA model is formed by including additional seasonal terms in the ARIMA models we have seen so far. It is written as follows: \[ SARIMA\;\;\;\u...
11777 sym Python (3769 sym/27 pcs) 16 img
Stat 142 ARIMA Models
Introduction In the previous lecture we learned how a regression model can capture trend and seasonality In this chapter we are going to learn how a regression model can be used to quantify the correlation between neighboring values in a time series (called autocorrelation) This type of model, called an autoregressive (AR) model, is useful for...
15296 sym R (3759 sym/29 pcs) 18 img
Stat 136 Lesson 2.3 (Bayesian Inference for the Poisson Mean)
Table of contents Review: The Poisson Distribution Bayesian Inference for the Poisson Mean Using Discrete Prior Bayesian Inference for the Poisson Mean Using Continuous Prior An Example Stat 136 (Bayesian Statistics) Lesson 2.3: Bayesian Inference for the Poisson Mean Author Norberto E. Milla, Jr. Published February 11, 2024 Review: The Po...
8837 sym 2 img 6 tbl
Stat 141 Analysis of Covariance
Quick review Nuisance factor- factor that may have an effect on the response variable but of not an interest to the researcher Unknown and uncontrollable- randomization balances out its impact across the experiment Known and controllable- blocking or grouping of the experimental units using the nuisance factor as grouping variable Known but u...
7926 sym 2 img 1 tbl
Stat 141 Split-Plot Experiments
Basic ideas a two-factor experiment wherein levels of one of the factors require large plot size for execution and also show large differences in their effects the experiment will consist of a set of large plots called main plots in which levels for the main plot factor are assigned each main plot is divided into subplots to which the second ...
6158 sym 4 img 2 tbl
Stat 141 Experiments with Nested Factors
Nested (hierarchical) factors Recall that in a factorial experiment, the levels of factor A are “crossed” with the levels of factor B In some two-factor experiments the level of one factor , say B, is not “cross” or “cross classified” with the other factor, say A, but is “NESTED” with it. Nesting may occur in the treatment struc...
3665 sym 3 img 3 tbl
Stat 141 Key Answer Lab Exercise 4
Problem No. 1 An experiment station conducted a randomized complete block experiment in order to make comparisons among three varieties of sweet potato. Six blocks were used. The yields (kg) are shown in the following table. Test if there exist significant differences in the mean yield among the three varieties. Test if blocking is effective....
4713 sym R (3501 sym/6 pcs) 3 img
Stat 141 Post hoc test for significant interaction
Remarks Recall that when the interaction effect is significant, the effect of one factor is dependent on the other factor When the interaction effect is significant, the main effects are no longer sensible Thus, in the presence of significant interaction effect, we do post hoc test of the levels of one factor at each (fixed) level of the other...
1253 sym 1 img 2 tbl
Stat 141 Factorial ANOVA Using R
Two-factor factorial ANOVA (Fixed Model) fact2.crd <- read.csv("twofactorial.crd.csv") head(fact2.crd) ## Pesticide Variety Yield ## 1 P1 V1 49 ## 2 P1 V1 39 ## 3 P1 V2 55 ## 4 P1 V2 41 ## 5 P1 V3 66 ## 6 P1 V3 68 out1 <- aov(Yield ~ Pesticide*Variety...
2846 sym R (5239 sym/23 pcs)
Stat 141 Expected Mean Squares
Steps in Deriving Expected Mean Squares Prepare a two way table with the terms of the model as the row labels. Write the subscripts in the model as column headings. Over each subscript, write the number of levels of the factors associated with that subscript and whether the factor is fixed (F) or random (R). Replicates are always treated as ra...
3096 sym 1 img 2 tbl