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14 Pages

### lecture4

Course: STAT 201, Fall 2008
School: Texas A&M
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Word Count: 1072

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Distributions Sometimes Normal the overall pattern of a large number of observations is so regular that it can be described by a smooth curve Four examples of histograms which demonstrate a symmetric, bell-shaped pattern. 1 Normal distributions can take on many different means and standard deviations. Only the general bell shape must remain the same. Properties of the Normal Distribution: 1. Symmetric,...

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Texas A&M - STAT - 201
Least-Squares RegressionRecall that correlation measures the direction and strength of the linear (straight line) relationship between two variables. If a scatterplot shows a linear relationship, we would like to draw a line on the plot to summarize
Texas A&M - STAT - 201
Solution to Quiz 2September 24, 2008Problem 1NO. Correlation does not imply a cause and effect relationship. So size of the hospital does not affect the length of stay of a patient. As a result the length of stay cannot be shortened by merely cho
Texas A&M - STAT - 201
CorrelationRecall that a scatterplot displays the form, direction and strength of the relationship between two quantitative variables. Linear relationships are particularly important because of their simple pattern that is quite common. A linear re
Texas A&M - STAT - 201
STAT 201 Section 501 Elementary Statistical InferenceVincent LeMoine Texas A&amp;M University Department of Statistics March 18, 20041Chapter 4 Section 4.1 : Randomness You cannot predict the results of tossing a coin or choosing an SRS, but there i
Texas A&M - STAT - 201
Syllabus for STAT 201, Section 501 Elementary Statistical Inference Fall 2004 Course: Lecture MW 4:105:25 PM, HECC 200 Instructor: Vincent LeMoine Oce: Phone: Email: Web Page: Oce Hours: Grader: Yi Qian Oce: Phone: Email: Oce Hours: Blocker 405F 845
Texas A&M - STAT - 303
Syllabus Statistics 303 Section 203, Summer 2008MWF 12:00 - 1:35pm BLOC 150, TR 12:00 - 1:35pm BLOC 161 Instructor: Vincent LeMoine, e-mail: vlemoine@stat.tamu.edu Office Hours: Yet to be announced, Office: 405-E Stat Dept. Office: Blocker 447, 8-5p
Texas A&M - STAT - 201
University Rules: Incompletes and Missed ExamsThe following university rules describe the procedures by which Incompletes should be given and the procedures to follow in deciding if a make-up exam should be given and the rules to follow in administe
Texas A&M - STAT - 303
STAT303Practice Questions Sp 06 jhc D. The assumption of linearity has been violated. E. All of the assumptions for simple linear regression are valid.Source | SS df MS -+-Model | 47.5154184 1 47.5154184 Residual | 8.0485833 8 1.00607291 -+-Total
Texas A&M - STAT - 201
STAT 201 Section 501 Elementary Statistical InferenceVincent LeMoine Texas A&amp;M University Department of Statistics January 21, 20041Chapter 1: Denitions: Individuals are objects described by a set of data. Examples are people, animals, or things
Texas A&M - STAT - 201
STAT 201 Section 501 Elementary Statistical InferenceVincent LeMoine Texas A&amp;M University Department of Statistics April 6, 20041Chapter 6 Introduction The purpose of statistical inference is to draw conclusions from data. We have examined data
Texas A&M - STAT - 201
Quiz 1 Solutions1. (a) Construct a five-number summary based on the stemplot below: (30 points) Stem = 1 1 2 3 4 5 6 |6889 |12237999 |122 |256 |2 |1(b) What is the shape of the distribution? (10 points) 2. List the two properties of a valid densit
Texas A&M - STAT - 201
STAT 201 Section 501 Elementary Statistical InferenceVincent LeMoine Texas A&amp;M University Department of Statistics April 17, 20041Chapter 7 Section 7.2 : Comparing Two Means Twosample problems are among the most common sitations encountered in s
Texas A&M - STAT - 303
S ta tis tic s 303Chapter 4 ProbabilityProbability modelWhen we study certain random phenomenon (ex. tossing a coin, choosing a digit at random ), we will describe it by: A list of all possible outcomes (called sample space, denoted by S)Ex.1.
Texas A&M - TEACHING - 651
Texas A&M - TEACHING - 651
Texas A&M - TEACHING - 651
Data Analysis and Statistical Methods Statistics 651http:/www.stat.tamu.edu/~suhasini/teaching.htmlLecture 6 (MWF)Suhasini Subba RaoLecture 6 (MWF)Review of previous lecture Every random variable has a probability `distribution' associated
Texas A&M - TEACHING - 651
Texas A&M - TEACHING - 673
BibliographyHong-Zhi An, Zhao-Guo. Chen, and E.J. Hannan. Autocorrelation, autoregression and autoregressive approximation. Ann. Statist., 10:926936, 1982. T. W. Anderson. An Introduction to Multivariate Analysis. Wiley, New Jersey, 2003. T. W. Ande
Texas A&M - TEACHING - 651
Data Analysis and Statistical Methods Statistics 651http:/www.stat.tamu.edu/~suhasini/teaching.htmlLecture 11 (MWF)Suhasini Subba RaoLecture 11 (MWF)Review of previous lecture Up until the previous lecture we were calculating probabilities
Texas A&M - TEACHING - 673
Chapter 7MixingalesIn this section we prove some of the results stated in the previous sections using mixingales. We rst dene a mixingale, noting that the denition we give is not the most general denition. Denition 7.0.1 (Mixingale) Let Ft = (Xt ,
Texas A&M - TEACHING - 651
Data Analysis and Statistical Methods Statistics 651http:/www.stat.tamu.edu/~suhasini/teaching.htmlLecture 8 (MWF)Suhasini Subba RaoLecture 8 (MWF)The binomial: mean and varianceRecall that the number of successes out of n, denoted by Sn is
Texas A&M - TEACHING - 651
11.1Commands in SPSSDowloading data from the web The data I post on my webpage will be either in a zipped directory containing a few les or just in one le containing data. Please learn how to unzip zipped documents. Saving data on my website t
Texas A&M - TEACHING - 673
Chapter 1Introduction: Why do time series?A time series is a series of observations xt , each observed at the time t. Typically the observations can be over an entire interval, randomly sampled on an interval or at xed time points. Dierent types o
Texas A&M - TEACHING - 673
STATISTICS 673 Autumn Semester, 2008 INSTRUCTOR: Dr. Suhasini Subba Rao OFFICE: BLOCKER 435 OFFICE HOURS: Monday and Wednesday 11:00 12:00 or by appointment. email address: suhasini.subbarao@stat.tamu.edu WWW: www.stat.tamu.edu/suhasini/teaching.htm
Texas A&M - TEACHING - 651
STATISTICS 651 Spring Semester, 2009 INSTRUCTOR: Dr. Suhasini Subba Rao OFFICE: BLOCKER 435 OFFICE HOURS: Monday and Wednesday 10:20 11:20 or by appointment. email address: suhasini.subbarao@stat.tamu.edu T.A.: Qing Chang, Room BLOCKER 409, email:qc
Texas A&M - TEACHING - 673
Chapter 5Almost sure convergence, convergence in probability and asymptotic normalityIn the previous chapter we considered estimator of several dierent parameters. The hope is that as the sample size increases the estimator should get closer to th
Texas A&M - TEACHING - 651
STAT 651 Midterm Test (50 minutes)NAME:Total number of Marks:Section:There are 7 questions in this paper, do not be deterred, they are all straightforward. Read each question carefully. The number of marks for each question are given in brack
Texas A&M - TEACHING - 651
Data Analysis and Statistical Methods Statistics 651http:/www.stat.tamu.edu/~suhasini/teaching.htmlLecture 15 (MWF)Suhasini Subba RaoLecture 14 (MWF)The underlying idea of statistical testing So far we have considered confidence intervals,
Texas A&M - TEACHING - 651
Texas A&M - TEACHING - 651
STAT 651 Midterm Test (50 minutes)October, 2008NAME:Total number of Marks:/30There are 6 questions in this paper, do not be deterred, they are all straightforward. Read each question carefully. There are questions on both side of the page.
Texas A&M - TEACHING - 673
Appendix AAppendixA.1 Background: some definition and inequalitiesThe norm of an object, is a postive numbers which measure the `magnitude' of that object. Suppose x = (x1 , . . . , xn ) Rn , then we define x 1 = n |xj | and x 2 = ( n |x2 )1/2 j