本次作业代写主要内容是R语言统计分析相关
Winter Term 2021
2021年冬季学期
广义线性模型MATH 523作业2
A3 R练习
从MASS库加载数据Boston：
该数据集具有波士顿附近郊区506个社区的自有住房中位数（medv）的多个预测因子。为简单起见，只关注最后253个观察值，并仅考虑以下输入：犯罪率（按城镇划分的人均犯罪率），氮氧化物（氮氧化物的浓度（每千万人口的份数）），年龄（在此之前建造的自有住房的比例） 1940年）和chas（查尔斯河虚拟变量（如果区域限制河流，则为1；否则为0））。
这是您可以进行子设置的方法：
lm（medv〜crim + nox + chas + crim：nox + crim：chas，data =波士顿[254：506，]）
（a）使用带有日志链接的Gamma GLM分析这些数据。描述使用偏差分析的模型构建过程。
（b）在（a）部分中选择的模型与使用其他选择的标准（例如AIC或BIC）选择的模型相比如何（请查看RClass6-HousingData.Rmd以了解如何使用R完成此操作） ？
（c）根据您在（a）和（b）部分中的工作，选择您喜欢的模型。证明您的选择。
（d）解释在（c）部分中选择的模型。
（e）显示（c）部分中所选模型的残差图并评论拟合质量。
（f）拟合线性回归模型，其预测因子与（c）部分中的模型相同。你更倾向哪个？您可以查看AIC，BIC诊断图等-由您决定！
Generalized Linear Models MATH 523 Assignment 2
A3 R Exercise
Load the data Boston from the MASS library:
This data set has several predictors of the median value of owner-occupied homes (medv), for 506 neighbourhoods in the suburbs near Boston. For simplicity, focus on the last 253 observations and consider only the following inputs: crim (per capita crime rate by town), nox (nitrogen oxides concentration (parts per 10 million)), age (proportion of owner-occupied units built prior to 1940), and chas (Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)).
Here is how you can do the subsetting:
lm(medv ~ crim + nox + chas + crim:nox + crim:chas,data=Boston[254:506,])
- (a) Analyze these data with a Gamma GLM with the log link. Describe a model building process using the analysis of deviance.
- (b) How does the model selected in part (a) compare to a model selected using an- other criterion of your choice, such as AIC or BIC (look at RClass6-HousingData.Rmd to see how this can be done with R)?
- (c) Based on your work in parts (a) and (b), select your favourite model. Justify your choice.
- (d) Interpret the model selected in part (c).
- (e) Display a residual plot for the model selected in part (c) and comment on the quality of the fit.
- (f) Fit a linear regression model with the same predictors as your model in part (c). Which one do you prefer? You can look at AIC, BIC diagnostic plots etc – up to you!