【latex版】水贴

查看data_2.sav数据集，这是一个关于癌症病人的数据集，数据的标签对每个变量做了详细的说明。考虑下列问题：

1） 分析癌症病人了男女的抽烟比例是否一致？（10分）
2） 已知methodA和methodB两个变量是对癌症扩散的两种检验方式，请问这两种检验方式是否一致？（10分）
3） 不同组别（group）的出血量（blood）是否有差异？（10分）
4） 不同手术方式的出血量是否有差异？（10分）
5） 尝试对从年龄（age）、性别（sex）、手术方式（location）、是否吸烟（smoking）、最大肿瘤直径（grtd）这些因素甄别出出血量的危险因素。（10分）

1 Introduction
2 Binary Response
3 Binomial and Proportion Responses
4 Variations on Logistic Regression
5 Count Regression
6 Contingency Tables
7 Multinomial Data
8 Generalized Linear Models
9 Other GLMs
10 Random Effects
11 Repeated Measures and Longitudinal Data
12 Bayesian Mixed Effect Models
13 Mixed Effect Models for Nonnormal Responses
14 Nonparametric Regression
15 Additive Models
16 Trees
17 Neural Networks

1 Introduction
2 Binary Response
3 Binomial and Proportion Responses
4 Variations on Logistic Regression
5 Count Regression
6 Contingency Tables
7 Multinomial Data
8 Generalized Linear Models
9 Other GLMs
10 Random Effects
11 Repeated Measures and Longitudinal Data
12 Bayesian Mixed Effect Models
13 Mixed Effect Models for Nonnormal Responses
14 Nonparametric Regression
15 Additive Models
16 Trees
17 Neural Networks

Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes.

Python offers a variety of packages for plotting data. This tutorial will use the following packages to demonstrate Python's plotting capabilities:

Matplotlib
Seaborn

Clinical trials have documented numerous clinical features, social characteristics, and biomarkers that are “prescriptive” predictors of depression treatment response, that is, predictors of which types of treatments are best for which patients. On the basis of these results, research is actively under way to develop multivariate prescriptive prediction models to guide precision depression treatment planning. However, the sample size requirements for such models have not been analyzed. We present such an analysis here. Simulations using realistic parameter values and a state-of-the-art cross-validated targeted minimum loss-based prescription treatment response estimator show that at least 300 patients per treatment arm are needed to have adequate statistical power to detect clinically significant underlying marginal improvements in treatment response because of precision treatment selection. This is a considerably larger sample size than in most existing studies. We close with a discussion of practical study design options to address the need for larger sample sizes in future studies.

Major depressive disorder (MDD) is a commonly occurring
(Ferrari et al., 2013), seriously impairing (Kessler,
2012), and woefully undertreated (Thornicroft et al., 2017)
disorder that is rated by the Global Burden of Disease
(GBD) Study as one of the more burdensome diseases in
the world (GBD 2015 Disease and Injury Incidence and
Prevalence Collaborators, 2016). Less than one-third of
MDD patients in clinical trials remit in response to a first
full course of treatment of either antidepressant medication
(ADM) or psychological therapy (Cuijpers, van
Straten, van Oppen, & Andersson, 2008). The remission
rate is even lower in routine care (e.g., Garrison, Angstman,
O’Connor, Williams, & Lineberry, 2016; Sacks, Greene,
Hibbard, & Overton, 2014; Vuorilehto, Melartin, Riihimaki,
& Isometsa, 2016). Such therapeutic failures are intolerable
to many patients, especially those struggling with
hopelessness, as indicated by the fact that fully half of the
121 daily suicides in the United States occur among
individuals who were treated for a mental disorder in the
prior 12 months (Ahmedani et al., 2014) and the fact that
MDD was the most common treated mental disorder
among these suicide decedents (Bertolote, Fleischmann,
De Leo, & Wasserman, 2004).

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np

In the above code chunk, we import the Matplotliib library with the PyPlot module as plt This is to make it easier to execute commmands as we will see later on in the tutorial. PyPlot contains a range of commands required to create and edit plots. %matplotlib inline is run so that the plot will show underneath the code chunk automatically when it is executed. Otherwise the user will need to type plt.show() everytime a new plot is created. This functionality is exclusive to Jupyter Notebook/IPython. Matplotlib's highly customizable code structure makes it a great guide to other plotting libraries. Lets see how we can generate a scatter plot from matplotlib.

In the above code chunk, we import the Matplotliib library with the PyPlot module as plt This is to make it easier to execute commmands as we will see later on in the tutorial. PyPlot contains a range of commands required to create and edit plots. %matplotlib inline is run so that the plot will show underneath the code chunk automatically when it is executed. Otherwise the user will need to type plt.show() everytime a new plot is created. This functionality is exclusive to Jupyter Notebook/IPython. Matplotlib's highly customizable code structure makes it a great guide to other plotting libraries. Lets see how we can generate a scatter plot from matplotlib.