MY-ASSIGNMENTEXPERT™可以为您提供online STAT501 Linear RegressioN线性回归的代写代考和辅导服务！
这是宾夕法尼亚州立大学线性回归课程的代写成功案例。
STAT501 课程简介
This graduate level course offers an introduction into regression analysis. A researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis.
STAT 501 is an applied linear regression course that emphasizes data analysis and interpretation. Generally, statistical regression is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).
Prerequisites
This graduate level course covers the following topics:
- Understanding the context for simple linear regression.
- How to evaluate simple linear regression models
- How a simple linear regression model is used to estimate and predict likely values
- Understanding the assumptions that need to be met for a simple linear regression model to be valid
- How multiple predictors can be included into a regression model
- Understanding the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid
- How a multiple linear regression model is used to estimate and predict likely values
- Understanding how categorical predictors can be included into a regression model
- How to transform data in order to deal with problems identified in the regression model
- Strategies for building regression models
- Distinguishing between outliers and influential data points and how to deal with these
- Handling problems typically encountered in regression contexts
- Alternative methods for estimating a regression line besides using ordinary least squares
- Understanding regression models in time dependent contexts
- Understanding regression models in non-linear contexts
STAT501 Linear Regression HELP（EXAM HELP， ONLINE TUTOR）
[15 points] For the random variables $X$ and $Y$, derive the following values
(a) $\operatorname{Pr}(X=1)$
(b) $\operatorname{Pr}(X=2 \cap Y=1)$
(c) $\operatorname{Pr}(X=3 \mid Y=2)$
\begin{tabular}{c||c|c|c}
& $X=1$ & $X=2$ & $X=3$ \
\hline \hline$Y=1$ & 0.1 & 0.05 & 0.2 \
\hline$Y=2$ & 0.05 & 0.25 & 0.35
\end{tabular}
[20 points] Consider rolling two fair die $D_1$ and $D_2$; each has a probability space of $\Omega=$ ${1,2,3,4,5,6}$ which each value equally likely. What is the probability that $D_1$ has a larger value than $D_2$ ? What is the expected value of the sum of the two die?
[10 points] Let $X$ be a random variable with a uniform distribution over $[0,2]$; its pdf is described
$$
f(X=x)= \begin{cases}1 / 2 & \text { if } x \in[0,2] \ 0 & \text { if } x \notin[0,2] .\end{cases}
$$
What is the probability that $f(X=1)$ ?
[30 points] Consider a data set $D$ with three data points ${-1,7,4}$. We want to find a model for $M$ from a restricted sample space $\Omega={1,3,5}$. Assume the data has Laplacian noise defined, so from a model $m$ a data point’s probability distribution is described $f(x)=$ $\frac{1}{6} \exp (-|m-x| / 3)$. Also assume we have an assumption on the models so that $\operatorname{Pr}(M=1)=$ $0.4, \operatorname{Pr}(M=3)=0.3$, and $\operatorname{Pr}(M=5)=0.3$. Assuming all data points in $D$ are independent, which model is most likely.
[25 points] Use python to plot the pdf and cdf of the Laplace distribution $(f(x)=$ $\left.\frac{1}{2} \exp (-|x|)\right)$ for values of $x$ in the range $[-3,3]$. The function scipy.stats.laplace may be useful.
MY-ASSIGNMENTEXPERT™可以为您提供ONLINE STAT501 LINEAR REGRESSION线性回归的代写代考和辅导服务！
