Applied Linear Statistical Models Michael H Kutner Pdf Files
Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. Vhr stock car setups for nascar cars. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and 'Comments' to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding.
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Class Times: Monday, Wednesday and Friday 11:15 AM-12:20 PM
Class Room: 004, Kemeny Hall
Instructor: Nishant Mallik, Office: 310 Kemeny Hall, Phone: 603-646-9020, Email : Nishant.Malik@dartmouth.edu
Office Hours: Monday, Wednesday and Friday 1:30 PM - 2:30 PM [or by appointment].
X-hours: Tuesday 12:00 PM -12:50 PM [Will be used intermittently at instructor's discretion for Python sessions or for review of course material etc. Do not schedule anything regular in this X-hr].
Title: Applied Linear Regression Models
Edition: 4th
Authors: Michael H. Kutner, Christopher J. Nachtsheim and John Neter
Publisher: McGraw Hill/Irwin
Click to access data sets used in the book
The linear regression model and its extension, the generalized linear model, are the most popular and powerful data analysis technique for studying statistical relationships. The course will present the theoretical background for linear models and their statistical properties, demonstrate how various problems and models reduce to the linear case, and explore the assumptions and limitations of linear models through derivation and simulation.
Roughly following topics will be covered during the course:
- Simple linear regression
- Multiple regression
- Analysis of variance
- Statistical model building strategies
- Regression diagnostics
- Analysis of complex data sets
MATH 10, another elementary statistics course, or permission of the instructor.
1. Two in CLASS EXAMS (1 hour long) 15 % each i.e., these two tests will account for 30% of the total grade.
2. HOMEWORK accounts for 20% of the total grade.
3. End of the course PROJECT for 15% of the total grade.
4. Final exam (3 hour long) accounts for the remaining 35%.
1. First in class exam : October 7, 2015.
2. Second in class exam : November 2, 2015.
3. Project submission deadline: November 16, 2015.
4. Final Exam : November 20, 2015 (8AM)
- Reference books:
- Statistical Models by A C Davison (Cambridge University Press, 2003). Excellent text with very modern treatment of the subject material.
- Linear Models with R by Julian J. Faraway (Chapman & Hall/CRC, 2015, 2nd Edition). Great text though we will not be using R in the course. Data sets:
- Miscellaneous Datasets page of Larry Winner, Department of Statistics, University of Florida. LINK
- A collection of data sets accompanying the book 'Understandable Statistics' by Charles Henry Brase and Corrinne Pellillo Brase (Cengage Learning,7th Edition) LINK
- Rdatasets is a collection of data sets that is distributed with R,these datasets can be accessed in Python using statsmodels functionalities in python. The html listing of thesedata sets is available on this page .
Homework will be assigned once a week on Fridays and will be due the following Friday, unless otherwise explicitly specified by the instructor. Submit homework to the instructor after the class or during the office hours. Homework sheets will be uploaded periodically onto this page. Homework problems marked with an asterisk (*), should be solved using ipython notebook (jupyter) and the resulting pythonnotebook should be submitted in html format to the instructor byuploading it at . In case you are not able to uploadthe homework files to this website then please contact the instructor.Password for the DROPITTOME website will be provided inthe class. A homework file should be named as hw < homework sheetnumber > < students full name > with no spaces orspecial characters.
Posted on: 09/18/2015 Due on: 09/25/2015
Solutions
Posted on: 09/25/2015 Due on: 10/02/2015 Solutions
Posted on: 10/02/2015 Due on: 10/09/2015 Solutions
Posted on: 10/09/2015 Due on: 10/16/2015 Solutions
Posted on: 10/16/2015 Due on: 10/23/2015 Solutions
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Posted on: 10/23/2015 Due on: 10/30/2015 Solutions
Posted on: 10/30/2015 Due on: 11/11/2015*
At the end of the course each student has to submit a research project based onthe material learned during the course. Students can choose either towork on a project individually or in a team of 2 to 3 students. Themain criteria for grading a project will be the originality of the idea/problem andcomplexity of methods, concepts and techniques used. Project document shouldbe submitted in a pdf format generated using latex or html generatedusing ipython notebook (jupyter). Students are highly encouraged touse the ipython notebook (jupyter) option for submission and to include interactive graphics in their submission. Please check out tools like mpld3 or Bokeh for creating interactive plots in ipython notebook. Students are alsoexpected to give a brief presentation to the class about theirproject.
Phyton will be the programming language for the course. No priorknowledge of Python is expected.
Python is among the most popular high level programming languages of our times, its application areas are wide and extensive and includes scientific and numericalcomputation. It has a large community of developers and contributors,hence it is very well supported. In recent years it has gained popularityamong data scientists with the inclusion of highly capable statistics anddata analysis toolboxes.
Student are highly recommended to install Anconda Pythondistribution , it is free and very easy to install on mostcomputers. It comes with all the packages we will needduring this course. Another way to install Python and all therequired packages is to install Canopy Express (free Enthought PythonDistribution). Students with no prior exposure to Python arediscouraged to attempt manual installation of Python or its packages,instead should install either Anconda Pythondistribution or Canopy Express (free Enthought PythonDistribution). Students that encounter problems installing Python,should contact the Instructor.
- Basic Python tutorials/books/notes/guides:
- A byte of Python by Swaroop CH. This book is one of the best tutorials for beginners.
- Introductionto Python for Econometrics, Statistics and Data Analysis by Kevin Sheppard. Useful notes for the course. Tutorials for the packages we will be using in the course:
- NumPy Tutorial and Tentative NumPy Tutorial (in pdf)
- NumPy User Guide and NumPy reference
- IPython notebook tutorial , Jupyter documentation and Notebook Gallery
1. BOX PLOT 2. SCATTER PLOT 3. LEASTSQUARES 4. DISTRIBUTIONS 5. INFERENCE 6. FUNCTIONS
7. CONFIDENCE BAND 8. CORRELATION 9. DIAGNOSTICS AND REMEDIAL MEASURES 10. TRANSFORMATIONS
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11. LOWESS 12. BONFERRONI INTERVALS 13. LACK OF FIT
Students with diagnosed learning disability are encouraged to discusswith the instructor any appropriate accommodations that might behelpful. All discussions will remain confidential, although the Student Accessibility Services office may be consulted.
You are encouraged to work together on homework. However, the final writeup should be your own. On exams, all work should be entirely your own; no consultation of other persons, printed works, or online sources is allowed without the instructor's explicit permission.