Quiz 4 Study Guide
User Orientation: Understand the 10 user centered design mentors
1. Interviews and observations of end users
2. Personas -> what a potential customer would be like
3. Scenarios -> Situations
4. Storyboards -> Workflow on how things woul
Lecture 7
David Armstrong
STATS 67 - UCI
1
Continuous Random Variables
Now we extend to continuous random variables.
A continuous random variable can be an uncountable innite possible
values.
Example:
X can be any number in the interval [0,1]. Thus SX
Lecture 5
David Armstrong
STATS 67 - UCI
1
Discrete Random Variables: Expectation
Review of what we has been covered with random variables.
f (x) is the probability mass function of a random variable X.
The input is x, which is a specied value of X from
Lecture 9
David Armstrong
STATS 67 - UCI
1
Recall: Binomial Distribution
Suppose we are interested in
X,
where
independent trials.
Fixed number of trials
n
trials
Fixed probability of success
p
Fixed probability of failure
q =1p
Trials are independent
X
Lecture 1
David Armstrong
STAT 67 - UCI
1
Statistics
Statistics is the mathematical science of learning from data, and of
measuring, controlling, and communicating uncertainty.
It is concerned with developing methods for collecting and analyzing
empiric
Lecture 4
David Armstrong
STATS 67 - UCI
1
Discrete Random Variables
Variable :
A quantity that may take dierent values.
Random variable :
A variable that may assume dierent values with
certain probabilities.
One way to think of it as a function that ass
Lecture 2
David Armstrong
STATS 67 - UCI
1
Probability
Remember that the events A1 , A2 , ., AM form a partition of the sample
space S when A1 A2 . AM = S .
The law of total probability states that if A1 , A2 , ., AM form a partition
M
P
then P (B) = P
#(A)
#(S)
P (A) =
A
#(A)
#(S)
S = cfw_HHH, HT H, HHT, T HH, T T H, T HT, HT T, T T T
A
A
r
Pr,n = n (n
n (n
(n
1) (n
(n
r)!
1) . (n
r + 1) =
n!
(n
n
2) . 2 1
r)! = (n
n!
r) (n
= n (n
n
(r
n! =
1) . 2 1
1) . (n
r + 1)
r
n
r
n
n
n
n
(r
1
1) = n
r
r+1
r)!
SX = cfw_0, 1, 2, 3, .
SX = cfw_1, 2, 3, .
X
X = cfw_1, 2, ., 1
X
p
X
SX = cfw_1, 2, 3, .
X
p
P (X = 1) = p
P (X = 2) = (1
p)p
P (X = 3) = (1
p)2 p
X Geometric(p)
f (x) = P (X = x) = (1
p)x 1 p
x
(X) = P (X x) = 1
X
X
1
p
1
p
p2
(1
p)n
1
x
|r| < 1
g(r)
Distributions
Distribution
Bernoulli
Binomial
Geometric
Poisson
Exponential
Uniform
Normal
pdf/pmf
p
x=1
f (x) =
1p x=0
n x
f (x) =
p (1 p)nx for x = 0, 1,
x
f (x) = (1 p)x1 p for x = 1, 2, 3,
f (x) =
x e
for x = 0, 1, 2,
x!
ex for x > 0
1
b
a
f (x
STAT 67, Winter 2017
Homework 5
3-07-2017
1. Say you have n = 100 many Xi s, where the Xi s are independent and identically distributed
Bernoulli random variables with p = 0.5 (E(X)=p and var(X)=p(1-p).
n
P
a. What distribution does
Xi follow exactly? Sta
3/15/2017
University of California, Irvine
Sevan Koko Gulesserian
Lecture 11
STAT 67
1 / 28
The union of the null and alternative will cover the entire
sample space.
The alternative hypothesis is what we are motivated to show.
The null hypothesis is the p
Stats 7 Winter 2017 - Homework 7
11.3 a. Procedure for two independent samples.
b. Procedure for paired data.
c. Procedure for two independent samples.
11.29 a. s.e. (!) = s/n = 6/9 = 2. Roughly, this is an approximation of the average difference between
Stats 7 Winter 2017 - Homework 8
13.4 a. One population mean.
b. The difference between the means of two populations.
13.8 a. H0: = 25 where is the mean speed of all cars that drive through the area.
b. H0: 1 2 = 0 (or : 1 = 2) where 1 2 is the difference
Stats 7 Winter 2017 - Homework 6
9.24 a. The mean of the sampling distribution of the sample mean is the population mean .
b. One value from the sampling distribution of the sample mean is one sample mean, denoted by !.
9.25 a. The mean of the sampling di
Stats 7 Winter 2017 - Homework 9
16.1 a. Appropriate. The response variable is quantitative and this is a comparison of independent groups.
b. Not appropriate. It's not a comparison of independent groups. There was only one group and all individuals
liste
STAT 67
Lecture 9
Sevan Koko Gulesserian
University of California, Irvine
2/23/2017
1 / 65
Normal Distribution
Up to now, we covered several discrete and continuos random
variables.
Now, wee come to what is knows as the normal or Gaussian
distribution.
Th
STAT 67, Winter 2017
Formula Sheet
February 15, 2017
Set Theory:
Compliment of a compliment is the original set. (AC )C = A.
(A B)C = AC B C .
(A B)C = AC B C .
Probability:
P (A) = 1 P (AC ) where AC is the compliment of A.
Note that A B is A or B. A B i
Informatics 43 - Summer 2016
Lecture 2 (June 21, 2016)
Mustafa Ibrahim
What is Software Engineering?
Software
Engineering
What is Software Engineering?
Software
Code
Documentation, user manuals
Designs, specifications
Test cases
Plans and schedule
Informatics 43 - Summer 2016
Discussion 1 (June 28, 2016)
Mustafa Ibrahim
Use Cases
A use case diagram using UML:
Unified Modeling Language is a common way for engineers to identify and show the
use cases for a particular system. The above is a simple us
Informatics 43 - Summer 2016
Lecture 1 (July 5, 2016)
Mustafa Ibrahim
Quality
What is quality?
the standard of something as measured against other
things of a similar kind; the degree of excellence of
something.
Quality
What do we want to measure in so
Informatics 43 - Summer 2016
Lecture 1 (June 28, 2016)
Mustafa Ibrahim
Software Requirements
High Level vs Detailed Requirements
Most High Level Requirements are of 2 types:
Functional requirements
Non-functional requirements
Detailed Requirements
In
Informatics 43 - Summer 2016
Lecture 1 (July 12, 2016)
Mustafa Ibrahim
Architecture
What is architecture?
the complex or carefully designed structure of
something.
Architecture
Architecture
Architecture
Architecture
Software Architecture
What is softwa
Informatics 43 - Summer 2016
Lecture 1 (June 21, 2016)
Mustafa Ibrahim
Todays Agenda
Course Info
Course Schedule
Grading
Academic Honesty
What is Software Engineering?
Course Info
My Office Hours
Tuesday and Thursday 9:20pm to 10:20pm
TAs and their Off
Informatics 43 - Summer 2016
Lecture 1 (July 19, 2016)
Mustafa Ibrahim
Exam #1 Grading Updates
The development lead decided the optimal programming
language to use when solving the problem is Java. What
category does this fall into? (1 pt)
Design Constr
Informatics 43 - Summer 2016
Lecture 1 (July 20, 2016)
Mustafa Ibrahim
Reminders
EEE course evaluation Extra Credit
Must complete it prior to Sunday Jul 24, 2016 at 11:45pm to received the
extra credit (no exceptions since I have no control over this de
Informatics 43 - Summer 2016
Lecture 1 (July 14, 2016)
Mustafa Ibrahim
Homework #2
Additional Info:
The teacher's UCID which is a set 7 alpha-numeric characters that uniquely identifies the teacher.
The teacher's ClassSched four digit PIN.
The class name
Informatics 43 - Summer 2016
Lecture 1 (July 7, 2016)
Mustafa Ibrahim
Testing
Typical progression of the different testing types:
Unit Tests
Integration
Test
Acceptance
Testing
Regression
Testing
System
Testing
Stress Testing
Testing
Unit Testing
Unit
Informatics 43 - Summer 2016
Lecture 2 (June 23, 2016)
Mustafa Ibrahim
Software Requirements
What are software requirements?
Thesoftware requirementsare description of features
and functionalities of a target software system.
Requirementsconvey the expe