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AMS 315 Data Analysis
创建日期：2024-08-13 17:38:25
所属分类：经济Economics
标签： AMS 315
Data Analysis

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AMS 315


Data Analysis

First Computing Assignment

One Predictor Linear Regression

Introduction

This assignment is due on Thursday, April 4.  This report is worth 100 points. Please

remember that there is a second project coming, so that you should finish the first project as soon as possible. Please submit your project on the Class Brightspace as instructed below. Please submit your report of Project 1 (both parts) in one pdf file. Each student has one chance to resubmit the report before the deadline. Detailed submission information is given below.

Project 1 has two parts. There are three files for this project. Two are for part A, and one is for part B. The files are labeled with the last six digits of your Stony Brook ID number.

Part A

Part A is worth 40 points. The model for the Part A assignment is a first data and

statistical processing task that a newly hired statistician might be given. Your report should address the issues that your future supervisor would want to know about: how many observations, fraction of missing data in independent variable and dependent variable, and imputation of missing data.

The two files for part A each contain a column for subject ID and a column for either the  dependent variable value or the independent variable value. Your first task is to sort the two files by subject ID and merge them. You should not just use “cut and paste” to merge your data.

Second, you are expected to deal with missing data. Your report should contain the count of the number of subject IDs that had at least one independent variable value or dependent variable value. It should also include the count of the number of subject IDs that had an independent variable value, the count of the number of subject IDs that had a dependent variable value, the  count of the number of subject IDs that had both an independent and dependent variable value, and the count of the number of subject IDs that had at least one independent variable value or dependent variable value.

Your second task is to impute the missing values. There are many of missing data

procedures. Often a statistical package has imputation algorithms in the software. For example, R has a package called MICE that has several options. You may not choose listwise deletion or mean imputation (or its equivalent median imputation). Specify your choice in your report.

Often, the choice of imputation method has little effect on the results if the fraction of missing data is 30% or less.

Part B

Part B is worth 60 points. The data file for part B contains one line for each subject ID.

The line will contain the subject ID, the value of the independent variable, and the value of the dependent variable. A transformation of either IV or DV or both maybe required. You should read the textbook (Chapter 11.1) for suggestions on fitting a model. An approximate lack offit (LOF) test should be applied. It is your responsibility to find repeated (or near repeated) independent variable values. That is, you will have very few exact repeats of an independent variable value. You should bin near repeated data into one level. For example, suppose that x1   = 1.01, x2  = 1.02, x3  = 1.03  and y1  = 2, y2  = 3, y3   = 4. While there are not exactly repeated x   values, you could bin these points into one group of nearly repeated points. That is, choose the average x-value as the value of x after binning. Then your binned data would bex1   = 1.02, x2   = 1.02, x3  = 1.02  and  y1  = 2, y2  = 3, y3  = 4. Now performa LOF test on the data set after binning all near repeated values. There is software in R that performs an approximate lack offit test. Often a transformation does not improve the apparent extent to which the data satisfies the assumptions of Chapter 11. Please check the r-squared of the data as given tother-squared of the data after you transform it. Also, please check the residual plot of the data. It maybe helpful to apply these checks to the data in part A.

Report

You must submit a one-page report on Problem A and a one-page report on Problem B, both parts in one single pdf format file. Each report should have four sections.

1.  Introduction. The introduction should contain a statement of the problem and the

objective of the paper. Some of the questions that you should answer are: What is the objective of your effort? What are your research questions? What is the background of this work? The introduction is easy: your problem is to recover the function that was used to generate the dependent variable value based on the value of the independent variable.

2.  Methods. The second section should describe your methodology. Specifically, how were the files were merged? What was the program used to perform. the statistical analysis?

What were the statistical techniques used? Did you use linear regression Did you use  additional procedures such as an approximate lack of fittest? How much missing data was present in the data? What procedure did you use to deal with missing data.

3.  Results. The third section should contain your results: What fraction of the variation of the dependent variable was explained? What was the analysis of variance table? What   was the fitted function? What was the confidence interval for the slope? What was the  conclusion to the test of the null hypothesis that the slope was zero.

4.   Conclusions and Discussion. The fourth section should be conclusions and discussion. This section should focus on “big picture” issues. Was there an association between the variables? How important was it? That is, what was the r-squared value. What is your  fitted function? You may submit a longer appendix of computer work and programs.

You are allowed an appendix to your report and there is no page limit on the appendix. If you  include a table or figure, you must discuss it. Tables and figures should be numbered and titled.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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