Using Linear Regression for
Model Building
Simple Linear Regression
1
Reading Assignment
Read Chapter 11
Section 11.1: Introduction to Linear Regression
Section 11.2: Simple Linear Regression (SLR)
STAT-4706
Homework # 5
Summer 2013
You must show all of your work for each problem in order to receive full credit. Minitab should
be used when specified. HW is due 08/15/2013. MLR
1. The data in the
Using Linear Regression for
Model Building
Chapter 11
Scatterplots & Correlation
1
Reading Assignment
Read Chapter 11
Section 11.12: Correlation
2
Multivariate Data
In engineering studies involving
STAT-4706
Homework # 1
Summer 2013
You must show all of your work for each problem in order to receive full credit. Minitab
should be used when specified. HW Due 07/12/2013.
1. (25) The reaction times
STAT-4706
Homework # 1 Solutions
Fall 2014
You must show all of your work for each problem in order to receive full credit. Minitab should be
used when specified. HW Due 09/11/2014.
1. (30) For the fo
STAT-4706
Solutions Homework # 2
Fall 2014
You must show all of your work for each problem in order to receive full credit. HW Due
09/25/2014.
1. (16) A random sample of 12 graduates of a certain secr
STAT-4706
Solutions Homework # 3
Fall 2014
You must show all of your work for each problem in order to receive full credit. HW is due
09/02/2014.
1. (11) It is claimed that automobiles are driven on a
Lecture 1-5
Descriptive Statistics:
Variability or Dispersion
1
Measures of Variability/Dispersion
Measures of central tendency give information
only about the typical value.
Real data exhibit varia
Lecture 1-6
Descriptive Statistics:
Data Displays
1
Important Data Displays
Stem-and-Leaf Plots
Histograms
Boxplots
Time Plots
Normal Probabiltiy Plots
Q-Q Plots
2
Stem-and-Leaf Plot
Displays the sha
Lecture 8-2
Sampling Distributions
Sampling Distributions
Statistics can be viewed as random variables
from a probability distribution.
Suppose we draw multiple samples from a
population.
Each samp
Lecture 1-4
Descriptive Statistics
Central Tendency
1
Descriptive Statistics
Two important characteristics of a population
Center: measures of central tendency
Behavior around the center: measures
Lecture 1-3
Review of Populations, Parameters,
and Statistics
Populations
Population: set of all possible observations of
interest to the problem at hand.
In theory, often infinite
In practice, usu
Lecture 8-1
Review of the Normal Distribution
Normal Distribution
Most widely used probability model.
Major reason: The Central Limit Theorem
Defined by two parameters
E(X) =
variance of V(X) =
Lecture 9-1
Introduction to Estimation
Overview of Estimation
2
Estimators
An unbiased estimator of an unknown
parameter is one whose expected value is
equal to the parameter of interest.
Thus, we
Lecture 8-5
and F Distributions
- Distribution
The - Distribution describes the random
behavior of sample variances.
Simplest case involves , sample variance.
Assumptions
Random sample
Normal dis
Lecture 10-5
Hypothesis Tests for Difference of
Two Means, Paired Data
Paired Data
Basic idea discussed in Chapter 9.
Classic example: Octane study
Take a set of gasoline blends
Split each blend i
Lecture 10-3
Hypothesis Tests for a Single Mean,
More on Power
Concept of Power
Power is the probability
We reject the nominal claim,
When the alternative claim, , is true
Power depends upon , the
Lecture 10-5
Hypothesis Tests for Difference of
Two Means, Variances Unknown
Basics: Independent Samples
X 11 , X 12 ,., X 1n is a random sample of size n1
from population 1.
X 21, X 22 ,., X 2 n is
Lecture 10-1
Overview of Hypothesis Tests
Basic Framework
The way we use data to answer questions about
parameters is very similar to how juries evaluate
evidence about a defendant.
We start with a
Lecture 9-6
Confidence Intervals for Proportions
Basics
Recall the binomial distribution
is the number of successes
is the size of the random sample
is the probability of a success
Can approximat
Lecture 9-4
Confidence Intervals for
Difference of Two
Independent Means
Variances Known
is a random sample of size n1 from population
1.
is a random sample of size n2 from population
2.
The two po
Lecture 8-3
Central Limit Theorem
Distributions of Sample Means
Much of classical statistical analysis uses
sample means.
Critical question: What is the distribution?
For normal population: sample
Lecture 9-2
Confidence
Intervals for
Confidence Intervals, 2 Known
2
Confidence Intervals, Known
2
With some algebra:
)
Thus, our interval is
Note: is not random!
The limits are random!
3
Confi
Lecture 9-5
Confidence Intervals for
Paired Data
Motivation
Before and after.
See if there are changes in the subject.
Initial reading is given, an experiment is
performed, then a second reading is
Lecture 9-3
Other Intervals for
Prediction Intervals
Confidence intervals provide good information
about the unknown parameter .
Prediction intervals, estimate the possible
value of a future observa
Lecture 8-4
t Distribution
What If the Variance Is Unknown?
In real-life, the variance is rarely known.
What is a reasonable strategy?
2
What If the Variance Is Unknown?
Consider
X
s/ n
Important
Lecture 10-4
Hypothesis Tests for a Single Mean,
Variance Unknown
Basics
Let X1, X2, , Xn is a random sample from a well
behaved distribution.
Estimate the population variance by the
sample variance
Lecture 1-2
Collecting Data
Collecting Data
We are interested in taking samples from
some population.
The most popular ways of doing this are:
A retrospective study.
An observational study.
A des
STAT 4706
Lecture 1-1
Scientific/Engineering Method
Scientific/Engineering Method
The heart of sound engineering practice is the
engineering method
systematic approach for problem solving.
constant
23,2k Factorial and Fractional
Factorial Experiments
1
Lecture 15-4
Fractional Factorial Design
2
Even for a moderate number of factors,
the 2k factorial can require an excessive
number of runs.
A p
STAT-4706
Homework # 1 Solutions
Fall 2017
You must show all of your work for each problem in order to receive full credit. Minitab should be
used when specified. HW Due 09/14/2017.
1. (35) The reacti
STAT-4706
Homework # 6 Solutions
Fall 2017
You must show all of your work for each problem in order to receive full credit. Minitab should be used when
specified. HW is due 11/07/2017
1. (16)11.5 Book
STAT-4706
Homework # 4 Solutions
Fall 2017
You must show all of your work for each problem in order to receive full credit. HW is due 10/05/2017.
1. (10) A textile fiber manufacturer is investigating
Stat 4706
HW # 7 Solutions
HW is due.
1. (20)An article in the AT&T Technical Journal describes the application of two level factorial designs to
integrated circuit manufacturing. A basic processing s
STAT-4706
Homework # 5 Solutions
Fall 2017
You must show all of your work for each problem in order to receive full credit. Minitab should be used when
specified.
1. (25)The data in the excel file rel