QUESTIONS FROM: Applied Regression Analysis and Other Multivariate
Methods (4th ed) by Kleinbaum, Kupper and Muller
KEY OF HW#3
Dear TA,
Students need to use JMP to ANSWER the related questions asked in Q8.7
-Summary of Fit
RSquare
RSquare Adj
Root Mean S
Question 1 [True-or-false Questions: 8 marks]
1-1.
When the standard deviation of a statistic is estimated from data, the result is called the
. True
Nonlinear relationships between two quantitative variables can sometimes
be changed into linear relations
Statistics 302
Dr. Alexandre Bouchard-Ct
o e
Fall 2014
Midterm
10/22/2014
Family name:
Given name:
Student ID:
Important information about the midterm
Write your student number on each page.
The exam is closed book, with 5 questions on 13 pages (includi
Statistics 302 Midterm 2
Calculators and one 8.511 inch formula sheet are a allowed. Please write your answers
directly on the exam paper. If you run out of space you can write on the back of the page but
clearly indicate that you have done so. You may wr
Statistics 302 Final Exam
Name:
Student Number:
Instructions
Fill in the information requested above.
Calculators and one 8.511 inch formula sheet are allowed. Write your answers
directly on the exam paper. If you run out of space, you may write your answ
Stat 302, Assignment 1, Due Thursday January 28, 2016 at 4:30pm
(This due date overrides the syllabus due date of Jan 26).
There are six multi-part questions and a set of questions based on 11 pages of reading.
Please see the Stats Workshop for help, or s
Agenda for Week 1, Hr 3 (Thursday, Jan 7)
- Binomial and F-Distribution distributions
- Hypothesis Testing
- Confidence Intervals
The normal and T-distributions are continuous in the sense
that Z-scores and T-scores, and the means can take on any
value; t
Stat 302, Assignment 2, Due Thursday February 18, 2016 at 4:30pm
There are _ multi-part questions and a set of questions based on 5 pages of reading.
Please see the Stats Workshop for help, or see me in office hours (Tue 1-2, Thur 3:30-4:30), or e-mail
me
Today's Agenda
Hour 1
Correlation vs association,
Pearsons R,
non-linearity,
Spearman rank correlation,
Hour 2
(Pearson) Correlation and regression.
Hypothesis testing for correlation and regression
Correlation and regression
Regression is used to further
Week 1 Tuesday Hr 2 (Review 1)
- Samples and Populations
- Descriptive and Inferential Statistics
- Normal and T distributions
One of the primary goals of statistics is to make statistical
inferences on of a population. A population is a blanket term
for
Today's Agenda
Hour 1
Correlation vs association,
Pearsons R,
non-linearity,
Spearman rank correlation,
Hour 2
Hypothesis testing for correlation
(Pearson) Correlation and regression.
Correlation vs association
Association refers to any sort of trend betw
Agenda for Week 3, Hr 2 (Tuesday, Jan 21)
Hour 2: lm (regression),
plot (scatterplots),
cooks.distance and resid (diagnostics)
Stat 302, Winter 2016 SFU, Week 3, Hour 2, Page 1
The basic form of the regression formula is lm(y ~ x)
I'm referring to it as t
Agenda for Week 3, Hr 1 (Tuesday, Jan 19)
Hour 1:
- Installing R and inputting data.
- Different tools for R: Notepad+ and RStudio.
- Basic commands: ?, ?, mean(), sd(), t.test(), lm(), plot()
- t.test() and its output.
- cor() , cor.test() and their outp
Agenda for Week 3, Hr 3 (Thursday, Jan 21)
- Confidence intervals for a given x
- Prediction intervals for a given x
- Causality
A regression equation gives you the slope and intercept of the
line that best describes Y as a function of X.
Since the regres
Agenda for Week 4, Hour 3
Another one-way ANOVA example
Introduction to two-way ANOVA
Today's dataset: farms.csv
Stat 302 Notes. Week 4, Hour 3, Page 1 / 37
Correction from Tuesday (Week 4 Hour 1,2):
When testing for equal variance, there are two differen
Today's Agenda
r2, the coefficient of determination
The bivariate normal assumption
Diagnostic plots: Residuals and Cook's Distance
R output (moved to week 3),
Syllabus note: We are ahead of schedule in regression, so we're
taking the time to add more exa
Statistics 302 Midterm Exam 1 Solutions
1. (1 marks) Briey dene the sampling distribution of a statistic.
Solution: Something like The distribution of the statistic over repeated samples
of data.
2. (1 mark) What theorem tells us that the sampling distrib
Statistics 302 Practice Midterm Exam 1A
Calculators and one 8.511 inch formula sheet are a allowed. Please write your
answers in an exam booklet. You may write in pen or pencil, but must write in pen if
you wish to challenge your grading after the exam is
2016/11/15
Assignment #7
Assignment #7
Qingqing Liu
November 15, 2016
Growthdata = read.csv("DataGrowth.csv")
# Remove "Malta from the dataset and run a regression of Growth on TradeShare.
newdata = subset(Growthdata,country_name != "Malta")
Question 1:
#
ClassSTR-2.R
minibi6031
Sat Nov 05 15:33:30 2016
#Set the directory where the data is located
setwd("C:/Users/minibi6031/Teaching/buec333/R files")
#Load additional packages
require(sandwich)
# Loading required package: sandwich
# Warning: package 'sandwi
Stat 302, Assignment 2, Due Thursday February 18, 2016 at 4:30pm
There are _ multi-part questions and a set of questions based on 5 pages of reading.
Please see the Stats Workshop for help, or see me in office hours (Tue 1-2, Thur 3:30-4:30), or e-mail
me
Nonlinear Regression Functions
(SW Chapter 8)
Outline
1. Nonlinear regression functions general comments
2. Nonlinear functions of one variable
3. Nonlinear functions of two variables: interactions
4. Application to the California Test Score data set
BUEC
Documentation for Earnings_and_Height
These data are taken from the US National Health Interview Survey for 1994. They are a
subset of the data used in Anne Case and Christina Paxsons paper Stature and Status:
Height, Ability, and Labor Market Outcomes, J
Chapter 1: Simple Linear Regression
Brad McNeney
2017-01-16
Simple Linear Regression
Simple linear regression models
I
The basic model is of the form
Data = Model + Error
I
We are interested in quantitative response variable Y
I
I
For now we will also ass
Statistics 302: Analysis of Experimental and
Observational Data
Review of Introductory Statistics
Instructor: Brad McNeney
Statistics and Actuarial Science, Simon Fraser University
Observational Units and Data Types
I
The observational unit is the people,
Statistics 302 Practice Midterm Exam 1B Solutions
1. (1 mark) True or false: Statistical signicance is the same as practical signicance.
Solution: False
2. (1 mark) Briey, what is a p-value?
Solution: Something like: The chance of seeing a statistic as or
Filipinos are using social media platforms 53 hours a week.
Thats a whole 11 hours more than the global average of 42
hours!
In a global study called Wave7, Filipinos are using social media
to primarily connect with their families living overseas. As of
t
THE
HUMAN
VOICE
A presentation by:
Jenevieve Lontoc
BSA-IV
Contents:
I. Overview of the Report
II. Introduction to Human Voice
III.Process of human
sound/voice
IV.Physiology of Human Voice
V. Terminologies
VI.Voice Disorders
The human voice is composed of
Statistics 302 Homework 2 Solutions
Chapter 8, #6 (6 marks)
a) (4 marks) The sum of squares model is SSY-SSE=1835.93 (1 mark) on 3
degrees of freedom (1 mark). The ANOVA table is as follows (1 mark each for
correct MSM, MSE and F):
Source
df Sum of Square
-title: "Chapter 1: Simple Linear Regression"
author: "Brad McNeney"
date: '2017-01-16'
output: beamer_presentation
-# Simple Linear Regression
# Simple linear regression models
- The basic model is of the form
$Data = Model + Error$
- We are interested i
Chapter 1: Simple Linear Regression, part II
Brad McNeney
2017-01-16
Load packages for these notes
library(Stat2Data); library(ggplot2)
library(dplyr); library(broom)
Transformations
Data transformations
I
The linear model is sometimes more appropriate fo