Publications by Laboratory Exercise No. 5
Stat 142 Lesson 2.1 (Time Series Decomposition)
Table of contents 1 Review of time series components 2 More on moving averages 3 Classical decomposition 3.1 Additive decomposition 3.2 Multiplicative decomposition 3.3 Comments on classical decomposition 4 X11 decomposition 5 SEATS decomposition 6 STL decomposition Stat 142 (Time Series Analysis) Time Series Decomposition Author Nor...
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Stat 142 Lesson 1.3 (Overview of Forecasting Strategies)
1 Introduction The ultimate end goal of time series is forecasting. Forecasting refers to predicting what will happen in the future by taking into consideration the past and present events. Forecasting methods can be generally classified into two: Quantitative and Qualitative. Quantitative forecasting methods include, among others, naive method...
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Stat 136 Lesson 2.2 (Bayesian inference for a proportion using continuous prior)
1 Motivation A limitation of specifying a discrete prior for \(p\) is when a plausible value is not specified in the prior distribution (e.g. \(p = 0.2\)), it will be assigned a \(0\) probability in the posterior distribution Ideally, we want a distribution that allows p to be any value in [0, 1] Two possible distributions come into mind: Co...
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Stat 136 Lesson 2.2 (Bayesian inference for a proportion using continuous prior)
class: center, middle, inverse, title-slide .title[ # Lesson 2.2: Bayesian Inference for p ] .subtitle[ ## (Continuous Prior) ] .author[ ### Norberto E. Milla, Jr. ] .institute[ ### Department of Statistics, VSU ] .date[ ### 2023-03-08 ] --- ## Motivation - A limitation of specifying a discrete prior for `\(p\)` is when a plau...
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Stat 122- Problem Set No. 2
INSTRUCTION: Present neat and detailed solutions. Problem No. 1 The proportion of impurities in certain water samples is a random variable \(Y\) with a density function given by \[ f_Y(y) = \begin{cases} \frac{3}{2} y^2 + y, \; \text{if} \; 0\leq y \leq 1 \\ 0, \; \text{elsewhere} \end{cases} \] Find the probability density function of \(U...
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Econ 115s Lesson 1.4 (Properties of OLS Estimators)
1 Statistical Properties of OLS Estimators What are the properties of the distributions of \(\beta_0\) and \(\beta_1\) over different random samples from the population? What are the expected values and variances of OLS estimators? We will first examine finite sample properties: unbiasedness and efficiency. These are valid for any sample size ...
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Econ 115s Lesson 1.3 (Incorporating Nonlinearities in the SLR Model)
1 Incorporating Nonlinearities in Simple Regression Linear relationships may not be appropriate in some cases. By appropriately redefining variables we can easily incorporate nonlinearities into the simple regression. Our model will still be linear in parameters. We do not use nonlinear transformations of parameters. In practice natural logari...
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Econ 115s Lesson 1.2 (The Simple Linear Regression Model)
1 Definition of the Simple Regression Model \[ y = \beta_0 +\beta_1x + u \] \(y\): the dependent variable (explained variable, response variable, predicted variable, regressand, outcome variable) \(x\): the explanatory variable (independent variable, cotrol variable, predictor variable, regressor, input variable) Also called “bivariate li...
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Stat 142 Lab Exercise 1
INSTRUCTIONS: Document your work by creating and compiling an R Markdown document in HTML format. Place heading for each section of your analysis. Upload your document to Rpubs and send the link in the GC. Open the livestock.xlsx file and study it. Save it in CSV format and read the file into R/RStudio. Convert the data for Cattle, Hog, and Goa...
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Stat 136 Lab Exercise No. 1
Problem 1 Let \(Y_1\) be the number of successes in \(n = 10\) independent trials where each trial results in a success or failure, and p, the probability of success, remains constant over all trials. Suppose the 4 possible values of \(p\) are 0.20, 0.40, 0.60, and 0.80. We do not wish to favor any value over the others so we make them equall...
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