Estimation
Of Statistical Linear Models
Dr Douglas Jones
1
Outline of Lecture
Least Squares
Estimate of
Coefficients
Estimate of Error
Variance
Dr Douglas Jones
R-Square, goodness of
model fit
Wilkinson-Rogers
notation for models in
R
2
Least Squares

Answer key at the end of each Lesson
Lesson 6
True/False
1. False
2. False
3. False
4. False
5. True
6. False
7. True
8. True
Short Answer Questions
1. d, m, y, h
2. If cell C4=100, it would show No; otherwise it will show 101
3. NOT function
4. TRUE or F

Basic Scoring
Rubric for
Presentation
Dr. Douglas Jones
1
Description
The following is matrix of presentation
characteristics with ratings on each key
performance feature
Prepare your presentation to achieve the highest
score
Dr. Douglas Jones
2
Item
1

Inference: Hypothesis
Testing
Outline of Lecture
Hypothesis Tests to Compare Models
Test of all the predictors
Testing just one predictor
Testing a pair of predictors
Testing a subspace
Dr. Douglas Jones
2
Statistical Inference
Sir Ronald Aylmer Fis

Course Overview
Introduction to Statistical Linear Models (aka Data
Analysis for Decision Making)
Dr. Douglas H Jones
1
Software for this Course
This course will not emphasize formulas or their
derivation
However, you will be expected to run a software

3.x.x Example Presentation Based on Rubric 4
DH Jones
Monday, June 23, 2014
Data for Example
We will use the mtcars data from R: Motor Trend Car Road Tests (1974). Load the data and look at the rst
six rows of the dataframe.
data(mtcars)
head(mtcars)
#
#

O vervi of St i i
ew
at st cal
D at Types i R
a
n
Dr Douglas H Jones
N um eri and Cat
cal
egori Vari es
cal
abl
First, lets understand the types of data values we
encounter in everyday life
The standard labeling of data types are:
Categorical, e.g., bl

SYSTEMS ANALYSIS AND DESIGN
10TH EDITION
Chapter 1
INTRODUCTION TO SYSTEMS ANALYSIS AND DESIGN
Introduction
Companies use information
as a weapon in the battle to
increase productivity,
deliver quality products and
services, maintain customer
loyalty, an

200.0 Project - Exploratory Data Analysis and
Preliminary Model Fitting
DH Jones
Friday, September 19, 2014
Abstract
BACKGROUND:
The National Institute of Diabetes and Digestive and Kidney Diseases conducted a study on 768 adult female
Pima Indians living

I er
nf ence: H ypot
hesi
s
Test ng
i
O ut i of Lect e
lne
ur
Hypothesis Tests to Compare Models
Test of all the predictors
Testing just one predictor
Testing a pair of predictors
Testing a subspace
Dr. Douglas Jones
2
St i i I er
at st cal nf ence

04.0.0.0_Hypothesis_Testing_Summary_R_Program.R
dhjones
Mon Jun 29 20:25:16 2015
#Hypothesis Testing: Summary R Program
# Load Packages
library("faraway")
# Set Options
options(show.signif.stars=FALSE)
# In Fisher's honor
# Load Data
data(savings)
head(sa

Basic Scoring
Rubric for
Presentation
Data Analysis for Decision Making
Dr. Douglas Jones
1
Description
The following is matrix of presentation
characteristics with ratings on each key
performance feature
Prepare your presentation to achieve the highest

Automated
Variable
Selection and
Validation
Lecture Outline
Backward stepwise regression testing based
Stepwise regression information criterion based
Model Cross-validation for predictive utility
Kitchen Sink Example
Dr Douglas H Jones
2
Variable Sel

Condence
Intervals and
Prediction
Intervals
DH Jones
Condence
Intervals for
the
Coecents in
a Statistical
Model
Prediction and
Condence
Intervals for
New and
Average
Response
Values
Bonferroni
Correction
Condence Intervals and Prediction Intervals
Data An

I er
nf ence:
Confi ence
d
I erval
nt
s
Dr. Douglas H. Jones
1
Lect e O ut i
ur
lne
Confidence Intervals for Beta Parameters
One-at-a-time CI for Beta Parameters
Joint CI for two Beta Parameters
Confidence Intervals for Individual
Predictions
Confide

300.0 Project - Preliminary Model Fitting
DH Jones
Friday, September 19, 2014
Abstract
BACKGROUND:
The National Institute of Diabetes and Digestive and Kidney Diseases conducted a study on 768 adult female
Pima Indians living near Phoenix.
AIMS:
Use the d

Inference:
Confidence
Intervals
Dr. Douglas H. Jones
1
Lecture Outline
Confidence Intervals for Beta Parameters
One-at-a-time CI for Beta Parameters
Joint CI for two Beta Parameters
Confidence Intervals for Individual Predictions
Confidence Intervals

Basic Scoring
Rubric for
Presentation
Data Analysis for Decision Making
Dr. Douglas Jones
1
Description
The following is matrix of presentation
characteristics with ratings on each key
performance feature
Prepare your presentation to achieve the highest

Basi Scori
c
ng
Rubri f
c or
Pr
esent i
at on
Dr. Douglas Jones
1
D escri i
pt on
The following is matrix of presentation
characteristics with ratings on each key
performance feature
Prepare your presentation to achieve the
highest score
Dr. Douglas Jon

To Hiring Manager,
I am writing to express my strong interest in joining Longchamp USA for the IT Deskside Support
Specialist position in New York, New York. I am a recent college graduate from Rutgers University having
majored in Management Information S

PART 1: EXCEL Spreadsheet DUE Wednesday, April 1, 6 pm UPLOAD in Moodle
A.
Prepare the INPUT Sheet in Appendix A using financial statement information for your company.
Obtain the most recent 5 years of annual financial statements.
Note the Year-End dates

1. Answer the following questions concerning chapter 1:
1.1 The application layer in Host A sends a message to Host B. Assuming no errors, the
application layer in Host B receives
The exact same message
the same message, along with headers and trailers

The quiz is worth 100 points. There are 11 multiple choice or true/false questions worth 3
points each. There are 11 calculation or short answer questions worth 6 points each. You will
get 1 point for putting your name on the paper. Many of the problems a

Pre-school LLL Relocation Question:
Little Learner at Livingston (LLL) is a prestigious preschool in the town. Experiencing the recent pleasant township developm
But this plan is determined by the availability of a land space availability next to the town

JAVA CS 113
CHAPTER 4
Public outside the class, visibility modifier
Private not accessible outside the class
To support the public method we can use private in class.
We can have 0 or more constructor
Anatomy/ structure of a class- Main method and have ot

Mehul Rana
Topic 2
Title: Wearable Sensors Could Translate Sign Language Into English
MLA : Dodgson, Lindsay. "Wearable Sensors Could Translate Sign Language Into English." LiveScience.
TechMedia Network, 15 Oct. 2015. Web. 17 Dec. 2015.
Engineers at Texa

Answer key at the end of each Lesson
Lesson 1
True/False
1. False
2. True
3. False
4. False
5. True
6. False
7. True
8. False
Short Answer Questions
1. The active cell
2. A2, A3, A11, etc.
3. The Ribbon
4. Alignment, Borders, Fill
5. Ctrl + Home
6. Chart

1. Badoo.com
High-level objective: Finding nearby partners to date or casual meet ups.
Description of claim: Search for potential dating partners or friends where
each person has a description of their profile and rankings.
List of apps that exemplify thi

Team member contributions and grade distribution.
Note: All members of the group has contributed equally to the project. Below is an estimation of distribution.
Points: are distributed from 100 among 6 members
Wierzbicki - (Points: 16.6)
Problem & Activit

Team Rocket
5 October 2016
Claims Analysis
Approach Title: Reviews about Professors!
Website is built to help college students to choose the best professor for the class they
have to take during the semester.
Relevant apps/websites:
Ratemyprofessors.com

Problem Scenario
Name: Ashley Lee
I want to unite with my friends who are all bookworms
Age: 19
Gender: Female
Education: Architecture (Freshman)
Location: Parsipanny, NJ (On
Campus)
Goals:
To make new friends on
campus
Be more active in campus
life/event

Stakeholders list
Target Demographic & 1 Persona
Stakeholders list:
Key: L=low, M=Medium, H=High
Stakeholders
Key Interests
Influence on
App(L,M,H)
Participation
(L,M,H)
NJIT Students
To be involved
with events and
be friends with
other students.
High
Hig