Chapter 4
Continuous Probability Distributions
Solved Problems (con)
4.3 The Normal Distribution
9.
Let Z be a standard normal random variable and calculate the following probabilities:
a.
b.
c.
d.
e.
f.
g.
h.
i.
j.
ANSWER:
a.
b.
=
c.
=
d.
e.
=
=
f.
=
g.
SDS 302 Estimating the Mean
Confidence levels
How can we estimate the population mean from a sample?
What is a confidence interval?
We can compute a _ for which believe the _
_falls between
The interval is called _
How confident we want to be that we capt
SDS 302: Center and Spread
Vocabulary
1. bin
2. histogram
3. standard deviation
4. unimodal
_ a graphical display of quantitative data, with data grouped in bins
_ a distribution with two mounds
_ a distribution with one mound
_ a graphical display for ca
SDS 302: Data
Vocabulary
1. data
2. descriptive statistics
3. quantitative
4. categorical
5. nominal
6. inferential statistics
7. population
8. sample
9. discrete
10. continuous
11. ordinal
_ qualitative data that cannot be ordered or ranked
_ a type of v
SDS 302 Z-scores
Vocabulary
1. standard deviation
2. z-score
3. 68-95-99.7
4. nearly Normal condition
5. Normal percentile
6. outlier
7. symmetric
8. left-skewed
9. right-skewed
_ the area under the Normal curve to the left of a z-score
_ is the distribut
SDS 302 Sampling Distribution
Vocabulary
1. population
2.
3.
4.
5.
6.
7.
8.
9.
sample
parameter
statistic
statistical inference
sampling distribution
central limit theorem
precision
standard error (SE)
_ states no matter the shape of the population distri
SDS 302 Normal Distributions
Percentiles
What does percentile mean?
What z-score marks of the 5th percentile?
Example: WHR
1. What is WHR?
2. What are the parameters of the WHR distribution for females?
Draw a normal curve and label where the raw
scores f
Question 6 Part C
Class III vs. California Mailbox
$22.00
$20.00
$18.00
f(x) = 1.18x - 1.56
R = 0.93
Class III $16.00
$14.00
$12.00
$10.00
$8.00 $9.00 $10.00 $11.00 $12.00 $13.00 $14.00 $15.00 $16.00 $17.00 $18.00
California Mailbox
From the equation, I c
6.3 Ley de Coulomb y coeficientes de friccin
Las leyes de la friccin seca de Coulomb se pueden ejemplificar mediante el siguiente
experimento: un bloque de peso W se coloca sobre una superficie horizontal plana:
Las fuer zas que actan sobre el bloque son
Lecture #3- The Fall of Rome and Rise of Byzantium Pg. 1
Slide 1 and 2
Hello and welcome to the third content lecture of the course.
This lecture will be split into two parts.
o First, we will look at Rome. It is important to look at the
Roman Empire beca
Lecture #1- Legacy of the Ancient and Classical World Pg. 1
Slide 1
Hello and welcome to the first content PowerPoint of the semester!
First we will be looking at the Legacy of the Ancient and Classical
World. Just a quick note before we get into the actu
Lecture #2- The Rise, Spread, and Influence of Christianity and Islam Pg. 1
Slide 1 and 2
Hello and welcome to the second content lecture of the course. Before I
begin this lecture I do just want to make a short disclaimer.
We will be discussing the histo
Lecture #11- The End of the Renaissance, the Counter-Reformation, and the Baroque Pg. 1
Slide 1 and 2
Hello and welcome to the eleventh content lecture of the course.
This lecture will be on the end of the Renaissance, the CounterReformation, and the Baro
Lecture #7- Medieval China Pg. 1
Slide 1 and 2
Hello and welcome to the seventh content
lecture of the course. This lecture will be
on Medieval China.
First, I want to make a quick note on
organizationAlthough the main topic of this lecture is
Medieval Ch
Lecture #10- The Renaissance Pg. 1
Slide 1 and 2
Hello and welcome to the tenth content lecture of
the course. This lecture will be on the Renaissance,
the period between 1300 and 1600 CE.
So, for this lecture we will be continuing from
where we left off
Lecture #8- The Late Middle Ages and the Rise of the Renaissance Pg. 1
Slide 1 and 2
Hello and welcome to the eighth content lecture of the course. This
lecture will be on the Late Middle Ages and the rise of the
Renaissance, the period between 1300 and 1
Lecture #5- The High Middle Ages Pg. 1
Slide 1 and 2
Hello and welcome to the fifth content
lecture of the course. This lecture will be
on the High Middle Ages, the period
between 1000 and 1300 CE.
So, for this lecture we will be continuing
from where we
Lecture #4- The Early Middle Ages Pg. 1
Slide 1 and 2
Hello and welcome to the fourth content
lecture of the course. This lecture will be
on the Early Middle Ages, the period
between 476 and 1000 CE.
So, for this lecture we will be moving
back west from t
Chapter 18: Engineering
Fundamentals
Section 18.1 Statics
Statics: Concerned with equilibrium of
bodies subjected to forces
1
Forces and Moments: two entities of most
interest in Statics
Force: the manifestation of the action of
one body upon another
Typ
2-44.
In the design of an electromechanical product, 12 components are to be stacked into a cylindrical casing in a manner
that minimizes the impact of shocks. One end of the casing is designated as the bottom and the other end is the top.
(a) If all comp
Normal
Error Model
No matter what distribution the error terms i have (and hence the Yi),
the least squares method provides unbiased point estimates of 0 and 1.
These estimates have minimum variance among all unbiased linear
estimators.
However, to do con
Regression Models
A regression model is a formal means of expressing two essential ingredients of a statistical
relationship:
1)
A tendency of the response variable Y to vary with the predictor variable X in a systematic
fashion.
2)
A scattering of points
Estimation of the
Survival Function
We now wish to estimate the survival function. Note, unless stated
otherwise, we assume recorded values of time are continuous and
subject only to right censoring.
Assume we have a sample of n independent observations o
SDS 380D Statstcal Methods II
UTC 4.112 TTH 12:30-2:00 Spring 2016
Professor:
Matt Hersh
matt.hersh@austn.utexas.edu
Office: GDC 7.508F
Office Hours: Th 2:00-3:00
Teaching Assistant:
Teaching Assistant:
Kejin Lee
Kejin53@gmail.com
Office: PAR 210
O hours:
Mixed Models
So far, we have been using a cell means model for the single-factor random
effects model. We can also use a random factor effects model. The two
models are equivalent.
To do so, we express each factor level mean i as a deviation from its
expe
Generalized Linear Models
Generalized linear models, GLMs, describe patterns of association and
interaction.
The models help us evaluate which explanatory variables affect the
response, while controlling for effects of possible confounding variables.
For
Logistic Regression Models
The odds of success is the probability of success divided by the
probability of failure:
1
And
log
1
x
exp( x ) exp( ) exp( x )
1
Logistic Regression Models
An odds ratio is the ratio of two odds. Here we look at the o