Part A 1. Create and print a SAS dataset or R dataframe named Flour.
2. Use SAS or R to find the simple linear regression model for predicting NBags from Weight. nbags= 5.86436+0.02180*weight
3. Use proc means to compute the means and standard deviations
PART A 1. Read in the data from "paper.txt", print it with R. Everything was input correctly.
2. Added cat statement using R
3. White paper Sample mean : 0.09565517 Sample standard deviation : 0.02963863 Sample median : 0.098 Sample IQR: 0.006 Values for
Technical Report for Final Project
Group Member: Keng Zhang, Shuxiang Xu
Introduction
This project is designed for building a model for predicting the price of
2005 GM Chevrolet used cars.
The variables in original dataset are price, mileage, make, model,
InClass Exercise
1. Identify the correct plot that shows constant variance
Residuals show
increasing trend
Residuals show
decreasing trend
Residuals show
randomly scattered
around zero line
InClass Exercise
2. Identify the correct plot that shows indepe
Multiple Regression
Influential points
Outliers are data points which lie outside the general linear pattern of which the midline
is the regression line. A rule of thumb is that outliers are points whose studentized
residual is greater than 2.0. The remov
Influential Observations and Inference in Accounting Research
Andrew J. Leone
University Miami
[email protected]
Miguel MinuttiMeza
University of Miami
[email protected]
Charles Wasley
University of Rochester
[email protected]
August 2013
(
CSC425 Time series analysis and forecasting
Homework 1
Due on Wednesday 1/18/2017 before midnight
Total points: 22
Reading assignment (pdf documents posted under week 1 and 2)
o TSAR: Chapters 1 and 2: sections 2.12.4. Ljung box test on page 184, Chapter
CSC425 Time series analysis and forecasting
Homework 4  Due on Wednesday February 15th, 2017
Total Points: 16
Reading assignment:

TSAR: Chapter 10
IAFR: Read Section 2.8, 2.9, 2.12 in the Intro to Analysis of Financial Time Series, by
Ruey S. Tsay.
IAF
CSC425 Time series analysis and forecasting
Homework 3 answers
Due by Wednesday February 1st, 2017 before 11:59pm
Total points: 20
PROBLEMS
Problem 1 [not to be graded  but a similar problem may be in the exam]
Consider the following MA(3) time series p
CSC425 Time series analysis and forecasting
Homework 2
Due by Wednesday January 25th, 2017 before 11:59pm
Total points: 20
Reading assignment
o TSAR: Chapter 4: sections 4.14.3, Chapter 6: sections 6.1 and 6.2. Forecasting in
Chapter 9: sections 9.1 and
CSC425 Time series analysis and forecasting
Homework 5  Due on Wednesday March 1st, 2017
Total Points: 24
Reading assignment:
1.
2.
3.
4.
5.
6.
7.
TSAR: Chapter 10 on seasonality
TSAR: Time series regression in Chapter 11, sections 11.311.5; GARCH Model
Overview of Statistical Hypothesis Testing
Hypothesis testing is the use of statistics to determine the probability that a given hypothesis is true.
The usual process of hypothesis testing consists of four steps.
STEP 1: Formulate the null hypothesis H 0
CSC423: DATA ANALYSIS AND REGRESSION / CSC 324: DATA ANALYSIS & STATISTICAL SOFTWARE II
Reading Week4
Textbook: A Second Course in Statistics: Regression Analysis, 7th ed., William Mendenhall, Terry L.
Sincich, Prentice Hall, 2010 (ISBN: 9780321691699)
OneWay Analysis of Variance: A Guide to
Testing Differences Between Multiple Groups
In analysis of variance, the main research question is whether the sample means are from different populations. The
assumptions upon which the tests and estimation proced
1. The difference between F test and t test are:
Fvalues are all nonnegative.
The distribution is nonsymmetric.
The mean is approximately 1.
There are two independent degrees of freedom, one for the numerator, and one for the denominator.
There are man
Part A 1. Create a SAS or R dataset and print it.
2. Create a regression model for predicting hours from type and rpm. Use a dummy variable for type. The regression model is: Hours = 76.65886 0.06421 * rpm 22.00468 * dummy_var
3. Create a scatterplot of
Nandhini Gulasingam
CSC423  Assignment Four
February 6, 2017
PROBLEM 1 [16 pts] to be answered by everyone
Create scatterplots to visualize the associations between bank balance and the other five
variables. Discuss the patterns displayed by the scatterp
StatNews #83
Interpreting Coefficients in Regression with
LogTransformed Variables1
June 2012
Log transformations are one of the most commonly used transformations, but interpreting results of
an analysis with log transformed data may be challenging. Thi
CSC423 Data Analysis and Regression
SAS Procedures for Exploratory Data Analysis (EDA)
PROC MEANS
The PROC Means provides data summarization tools to compute descriptive statistics for variables
across all observations and within groups of observations. F
CSC423: DATA ANALYSIS AND REGRESSION / CSC 324: DATA ANALYSIS & STATISTICAL SOFTWARE II
Reading Week1
Textbook: A Second Course in Statistics: Regression Analysis, 7th ed., William Mendenhall, Terry L.
Sincich, Prentice Hall, 2010 (ISBN: 9780321691699)
CSC 423 Data Analysis and Regression
An Introduction to SAS
SAS is a sophisticated computer package containing many components. The capabilities
of the entire package extend far beyond the scope of this course. We will limit our
discussions only to those
17  Transformations in Simple Linear Regression
Example  Polychlorinated Biphenyl (PCB) Concentration and Age of
Rainbow Trout in Lake Cayuga (New York).
Data File: PCBtrout.JMP
In this experiment we are studying the relationship between age of trout an
Handout 1: Confidence Intervals and Hypothesis Testing
Problem 1:
In order to ensure efficient usage of a server, it is necessary to estimate the number of
concurrent users. According to records, the average number of concurrent users at 100
randomly sele
J Real Estate Finan Econ (2007) 35:161180
DOI 10.1007/s111460079032z
The Impact of Railway Stations on Residential
and Commercial Property Value: A Metaanalysis
Ghebreegziabiher Debrezion & Eric Pels &
Piet Rietveld
Published online: 19 June 2007
# Sp
Class 4. Leverage, residuals and influence
1
Todays material
An in depth look at
Residuals
Leverage
Influence
Jackknife
Masking
2
Residuals
Residuals are vital to regression because they establish the credibility of
the analysis. Never accept a regression
Handout 1: Confidence Intervals and Hypothesis Testing
Problem 1:
In order to ensure efficient usage of a server, it is necessary to estimate the number of
concurrent users. According to records, the average number of concurrent users at 100
randomly sele
PART A 1. Use scan function to create an R dataset.
2. Use cat and other function to add explanations.
3. Create a normal plot for nusers.
4. Compute a 95% confidence interval with R. Xbar: 17.954 Se: 0.447 Df: 49 Confidence interval: (17.057, 18.851)
5.