Publications by Kelsey Blackstone
stat 207 q21
Consider the data available as “birthweight” form the package “LearnBayes”. Fit a linear regression that considers age and gender as explanatory variables for birth weight. Describe the posterior distribution of the regression parameters using a sample-based approach. Explore the predictive posterior distribution for the birth weight of c...
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temp hw3 stat 208
Question 1 Compute and plot the residuals for a fit of the model: \[Y_{t} = \mu + \alpha t + \gamma t^{2} + S_{t} + \epsilon_{t}\] to the Mauna Loa carbon dioxide series. Solution: Looking at a period of 120 months of the \(CO_{2}\) data collected, we see that there is a periodic trend to the data. We account for this trend by adding a quadrati...
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STAT 208 - Homework 2
Question 3 Increases in atmopsheric \(CO_{2}\) levels over time is a well-known occurance. In order to better understand the trend and future of \(CO_{2}\), we would like to build a model that fits the data we have observed and, additionally, can make predictions for future unobserved years. In this question, we fit two models. The first model ac...
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Take Home Final (Part I) - KJB
Problem 1 Consider \(y_{1}, ..., y_{n} \overset{\text{iid}} \sim{}Gamma(\nu, \theta)\), where E(\(y_{i}\)) = \(\nu/\theta\). Assign \(\nu\) \(\overset{\text{iid}} \sim{}\) Gamma(3, 1) and \(\theta\overset{\text{iid}} \sim{}\)Gamma(2, 2). Part (i) Develop a Metropolis-within-Gibbs algorithm to sample from \(\rho(\nu, \theta | y_{1},...,y_{n})\). ...
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Homework 3
## Loading required package: ggplot2 ## ## Attaching package: 'plotly' ## The following object is masked from 'package:ggplot2': ## ## last_plot ## The following object is masked from 'package:stats': ## ## filter ## The following object is masked from 'package:graphics': ## ## layout Problem 6: Confidence Intervals Confidence I...
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STAT 208 - Homework 1
Question #1: Simple Linear Regression Fit a Simple Linear Regression Model to the Colorado snowfall data. # Create design matrix x1 <- c(1,1,1,1,1,1) elev <- c(5.280, 5.328, 7.522, 9.60, 6.732, 7.406) #elev <- c(5280, 5328, 7522, 960, 6732, 7406) x_design <- as.matrix(cbind(x1,elev)) # y vector avg_sno <- matrix(data = c(64,70,90,225,180,175)) ...
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