# MATH 6627 2010-11 Practicum in Statistical Consulting/Assignment Teams/Gray

### From Wiki1

Line 173: | Line 173: | ||

;Discussion: | ;Discussion: | ||

+ | Andy: Yes, maybe high degree of pain causes solicitousness, instead of solicitousness causing towards more pain. I don't think there exits a confounding factor. If there is, I believe it is LOVE, because a man's LOVE wins a highly attentive spouse and doesn't work as a narcotic as a woman feels. And with that kind of LOVE, he may move his body, more likely, which may cause more hurt. And then his physical function may recover faster. That is a sweat answer to any woman, but tooth- hurting felt by any healthy man. | ||

+ | Also, LOVE could be the mediating factor too. | ||

+ | |||

====3==== | ====3==== | ||

;Question: Have any confounding factors been accounted for in the analysis? | ;Question: Have any confounding factors been accounted for in the analysis? |

## Revision as of 22:08, 23 January 2011

## Contents |

## Assignment 1

### 1. Simpson's Paradox

Example: My friend and I play a basketball game and each shoot 20 shots. Who is the better shooter?

But, who is the better shooter if you control for the distance of the shot? Who would you rather have on your team?

This is question of Simpson's Paradox.

We can see from this figure, the relationship changed from negative to positive when we took the distance to our consideration. Black line linked probability of we two made. Red line is linked our performance when far, but blue when close.

Simpson’s paradox arises from one simple mathematical truth. Given eight real numbers: a, b, c, d, A, B, C, D with the following properties:, then it is not necessarily true that. In fact, it may be true that:.

This is an obvious math reality, yet it has significant ramifications in Bayesian analysis, medical research, science and engineering studies, and societal statistical analysis. It is of concern for any statistical activity involving the calculation and analysis of ratios of two measurements.

Exmaple 2 (Real Income tax example)

### 2. Graphics to visualize data

Gray: rgl and p3d (include the use of the 'groups' parameter to produce trajectories

### Introduction

File:Grey rgl p3d 1.pdf File:Grey rgl p3d 2.pdf

*rgl* is a library of functions that offers 3D real-time visualization functionality to the R programming environment (Adler & Murdoch, 2010), providing OpenGL implemention for R.

*p3d* is a library of functions which employs functions from RGL to help visualize statistical models expressed as a function of 2 independent variables with the possible addition of a categorical variable (Monette, 2009).

### Package *rgl*

With *rgl* we create a ‘device’ , which is simply a window, within which a ‘world’ is created where we can create 3 dimensional shapes and through which we can navigate.

Functions within the rgl package can be divided into 6 categories: (1) Device management functions (open and close devices, control active device) (2) Scene management functions (option to remove certain or all objects from the scene) (3) Export functions (creating image files) (4) Shape functions - essential plotting tools primitives (points, lines, triangles, quads) as well as higher level functions (text, spheres, surfaces).

(5) Environment functions - modify the viewpoint, background and bounding box, adding light sources (6) Appearance function rgl.material(…).

Using shapes and surfaces within an *rgl* device, statistical data can be represented in 3 dimensions. Some advanced examples are available as demos or provided on the rgl website.

A few of the functions from *rgl* are useful for manipulating 3D models created using *p3d*, since *p3d* contains many functions that inherit from *rgl* but taylor them to statistical methods. Thus all but a few are unnecessary for our purposes unless you would like to contribute functionality to *p3d*!

### Package p3d

In this section I will focus on example code you may use to familiarize yourself with the capabilities of this package. You will require the tuition.Rdata (source:) and USIndicesIndustrialProd.Rdata (source:) data sets. Note that a few of the commands employed in sample code are from rgl, but these will likely be superceded by *p3d* functions as the package matures.

In this section I will focus on example code you may use to familiarize yourself with the capabilities of this package. You will require the File:Gray p3d ex Tuition.txt and File:Gray p3d ex USIndicesIndustrialProd.txt data sets. Note that a few of the commands employed in sample code are from rgl, but these will likely be superceded by *p3d* functions as the package matures.

Initialization code:

library( lattice )

library( nlme )

library( car )

library( spida )

library( rgl )

library( p3d )

tuit = read.table('tuition.Rdata',header=TRUE)

head(tuit)

prod = read.table('USIndicesIndustrialProd.Rdata',header=TRUE)

head(prod)

For the tuition data we will begin by plotting the annual cost of tuition from a sample of American Universities against the rates of faculty compensation and proportion of students who graduate.

Using mouse keys you can change the field of view and zoom in and out. *Plot3d* creates the 3D plot as shown on the right.

We can remove elements from the device using the function *Pop3d()*. This function removes elements starting with the most recently added item. Multiple items can be removed addition an numeric argument, ie.*Pop3d(4)*

Init3d(cex = .8)

Plot3d(tuition ~ fac_comp + graduat, col = c("blue"), data = tuit)

Next we will subdivide the data by category, in this case whether the school is private (red) or public (blue) (variable name public.private).

Init3d(cex = .8)

Plot3d( tuition ~ fac_comp + graduat|public.private, col = c("blue", "red"), data = tuit)

Next we will add regression planes for private(red) and public(blue) schools using the lm() function to determine the fit, and Fit3d() to insert the plane in the graph. Axes and labels are added using Axes3d() and title3d().

fitpub = lm(tuition ~ fac_comp + graduat,subset=(public.private==0),data = tuit)

Fit3d( fitpub, col = c("blue"))

fitpri = lm(tuition ~ fac_comp + graduat,subset=(public.private==1),data = tuit)

Fit3d( fitpri, col = c("red"))

Axes3d()

title3d(main='Tuition predicted by grad rates and faculty salary -private (red) and public(blue) institutions')

Data ellipses are useful for understanding our data.

Ell3d()

We can change the view point of our graph using function *view3d(theta,phi,fov,zoom)*, which takes polar coordinates. Note that *view3d(0,0,0)* will rotate the image to to face the x-z plane (y into the screen) and *view3d(270,0,0)* will rotate the image to to face the y-z plane (x into the screen). Function *snap()* will capture a still image of the current view. Note that to use *movie3d()* you must have ImageMagick installed to automatically convert png's to gif, otherwise you must use external software.

view3d(0,0,0)

snap()

spin(theta = 0, phi = 0)

spins(inc.theta = 1/4, inc.phi = 0, theta = NULL, phi = NULL)

movie3d( spin3d(axis=c(0,1,0), rpm=20), duration=2, dir='movie' )

Here is an additional example, using data on the US indices of industrial products, plotting Mining production (MIN) over months and years. Adding the argument ‘groups=YR’ to *Plot3d* connects the months in a given year to produce trajectories.

open3d(windowRect=c(100,100,800,800),cex = .8)

prod = read.table('USIndicesIndustrialProd.Rdata',header=TRUE)

head(prod)

Plot3d(MIN ~ YR+MONTH,data=prod,groups=YR)

Axes3d()

title3d(main='Industrial Production Mining (1947-1993)')

view3d(215,0,45)

## Assignment 2

Statistics in the News: "Spousal support a royal pain?"

#### 1

- Question
- whether the article suggest a causal relationship between two variables? If so which? Are the data observational or experimental?

- Discussion

Andy: Yes, it did suggest. One variable is the spousal solicitousness. Another is the degree of pain. The data are observational, because there is no human intervention when collecting the data.

#### 2

- Question
- Can you think of alternative explanations to causality? Confounding factors? Or explanations consistent with causality? Mediating factors?

- Discussion

Andy: Yes, maybe high degree of pain causes solicitousness, instead of solicitousness causing towards more pain. I don't think there exits a confounding factor. If there is, I believe it is LOVE, because a man's LOVE wins a highly attentive spouse and doesn't work as a narcotic as a woman feels. And with that kind of LOVE, he may move his body, more likely, which may cause more hurt. And then his physical function may recover faster. That is a sweat answer to any woman, but tooth- hurting felt by any healthy man.

Also, LOVE could be the mediating factor too.

#### 3

- Question
- Have any confounding factors been accounted for in the analysis?

- Discussion

#### 4

- Question
- Have any mediating factors been controlled for in a way that vitiates a causal interpretation of the relationship?

- Discussion

#### 5

- Question
- What is your personal assessment of the evidence for causality in the study that is the subject of the article?

- Discussion

### Paradoxes and Fallacies

#### 2.

- Question
- You are studying observational data on the relationship between Health and Coffee (measured in grams of caffeine consumed per day). Suppose you want to control for a possible confounding factor 'Stress'. In this kind of study it is more important to make sure that you measure coffee consumption accurately than it is to make sure that you measure 'stress' accurately.

- Discussion

#### 5.

- Question
- In a multiple regression of Y on three predictors, X1, X2 and X3, if the coefficients of both X2 and X3, are not significant, it is safe to drop these two variable and perform a regression on X1 alone.

- Discussion

#### 8.

- Question
- In a multiple regression, if you drop a predictor whose effect is not significant, the p-values of the other predictors should not change very much.

- Discussion

#### 11.

- Question
- In a model to assess the effect of a number of treatments on some outcome, we can estimate the difference between the best treatment and the worse treatment by using the difference in the mean outcomes.

- Discussion

#### 14.

- Question
- If two variables have a strong interaction, this implies a strong correlation.

- Discussion