The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
EXAM 3 REVIEW ATTENDANCE
QUESTIONS
ATTENDANCE1
Which of the following is not a correct way to state a null hypothesis?
A. Ho: d = 0
B. Ho: 1 2 = 0
C. Ho : p 1 p 2
D. Ho: p = .5
ATTENDANCE2
If = .01 for a twotailed hypothesis test using the z test, the cr
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
EXAM 2 REVIEW ATTENDANCE
QUESTIONS
QUESTION1
Suppose y = number of broken eggs in a randomly selected carton of one dozen
eggs. The probability distribution is as follows:
Y
0
1
2
3
p(y)
0.65
0.20
0.10
0.04
4
What is the probability that the carton contai
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
REVIEW BINOMIAL DISTRIBUTION
ATTENDANCE1
Identify the variable, x, for a binomial distribution.
A. x is the probability of a success
B. x is the number of trials performed
C. x is the number of successes
D. x is the number of failures
ATTENDANCE2
Which tw
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Chapter 17 Analysis of Variance (section 17.1 only)
Topics.
1. SingleFactor Analysis of Variance (ANOVA)
2. Sources of Variation
3. ANOVA Table
Textbook Problems.
Page 17 (pdf on blackboard) #17.11
Formulas.
Treatment Sum of Squares (SSTr)
k
SSTr ni ( xi
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Chapter 8
Definition (review)
A parameter is a numerical measurement describing some characteristics of a population.
Any quantity computed from values in a sample is called a statistic.
The observed value of a st
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Home work
STA 1053
Sudip Roy
Total points 30 (Each question 5 points)
(Due date 27th June Monday)
Write your answer on separate page and submit it stapled with the question paper.
Q.1
The Nielsen Company conducts surveys each year about the use of various
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Chapter 5
Probability
1
Introduction to Probability
A probability is a numerical value that indicates the chance,
or the likelihood, of a specific event occurring.
Weather forecaster commonly use probability when
referring to the chance of rain the foll
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Chapter 2 (part 2)
Graphical Methods for
Describing Data
Distributions
Displaying Categorical
Data
Bar Charts
Bar Chart
When to Use: Univariate, Categorical data
To comply with new standards from the U. S. Department of Transportation,
helmets should reac
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Chapter 3
Numerical Methods
for Describing Data
Distribution
Suppose that you have just received your
score on an exam in one of your classes.
What would you want to know about the
distribution of scores for this exam?
Measures of center describe were the
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Chapter 4
(Section 4.1)
Describing Bivariate
Numerical Data
Correlation
Pearsons Sample Correlation
Coefficient
Properties of r
Does it look like there is a relationship between the two variables?
If so, is the relationshipYes
linear?
Yes
Does it look lik
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
BASIC STATISTICS
STA 1053
Sudip Roy (ROY)
Chapter 1
Collecting Data in
Reasonable Ways
Data and conclusions based on data are everywhere
Newspapers
Magazines
Online reports
Professional publications
Population the entire collection of
individuals or
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Chapter 2 (Part 1)
Graphical Methods for
Describing Data
Distributions
Variable
any characteristic whose value may change from one
individual to another
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The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
Lecture 9
Analysis of Variance (ANOVA)
The statistical methodology for comparing several means is called
analysis of variance, or ANOVA. We will consider two ANOVA
techniques:
Oneway ANOVA:
 is used when there is only one way to classify the populations
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
ChiSquare Test for Goodness of Fit
(after Applied Statistics by Hinkle/Wiersma/Jurs)
Scientists will often use the Chisquare ( 2 ) test to determine the goodness of fit between theoretical
and experimental data. In this test, we compare observed values
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
Name:
10/10/13
STA 1053.1 Handout #11
Hypothesis Testing using Proportion (p)
Multiple Choice:
1. You are interested in determining whether there is strong evidence in support of the
claim that less than 40% of retired adults have a parttime job. To ans
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
Analysis of Variance (ANOVA) Single Factor
In the past, when we needed to compare population means, we were restricted to determining the
existence of a significant difference using only two data sets. Lets suppose that you, the
statistician, needs to con
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
SI Information
SI Leader: Laura
Email: [email protected]
Twitter: si_statistics
Office Hour: 1111:50am 4th Floor of JPL (use the elevators)
Session times:
Monday: 33:50pm *Meet outside classroom*
Thursday: 1212:50pm JPL (2nd Floor) Study Room #6
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
ChiSquareChapter 11, Sections 11.111.2
Your text discusses three types of chisquare tests:
1. GoodnessofFit
2. Independence
3. Homogeneity of Proportions
We use the Greek symbol 2 (pronounced k i square to rhyme with skysquare) to
represent values o
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
THE CHISQUARED GOODNESSOFFIT TEST
Example. Ten years ago the distribution of fish at Simple Lake was as follows:
Type of Fish:
Proportion:
Bass
.10
Catfish
.25
Goldfish
.30
Trout
.35
Recently, a random sample of fish from Simple Lake provided the follo
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
Chapter 11
Hypothesis Testing Using the
OneWay Analysis of Variance
ANOVA
The analysis of variance (abbreviated ANOVA) is a
powerful hypothesis testing procedure that extends
the capability of ttests beyond just two samples.
There are many types of ANO
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
EXTRA CORRELATION PROBLEMS
It is hypothesized that there are fluctuations in norepinephrine (NE) levels which accompany
fluctuations in affect with bipolar affective disorder (manicdepressive illness). Thus, during
depressive states, NE levels drop; duri
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
Correlation
When two sets of data are strongly linked together we say they have a High Correlation.
The word Correlation is made of Co (meaning "together"), and Relation
Correlation is Positive when the values increase together, and
Correlation is Negati
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
ANOVA Simple Linear Regression
Example 181
x2 41222.14 592.62 2 6104.66
592.6
2 x
2
lxx x
lxx x
41222.14 10 6104.66
nn
10
2
y2 220360.47 1428.702 2 16242.10
1428.70
2 y
2
l yy yy
l yy
n 220360.47 10 16242.10
10
n
592.6 1428.70
x y
lxy xy x y 918
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
Coefficient of Determination Formula
Coefficient of Determination is one of the most important tools in statistics which is widely used
in data analysis in economics, physics, chemistry and many more fields. Coefficient of
determination allows us to forec
The University of Texas at San Antonio San Antonio
STAT 1053

Spring 2016
Chisquared test for nominal (categorical) data
The c2 test is used to determine whether an association (or relationship) between 2 categorical
variables in a sample is likely to reflect a real association between these 2 variables in the
population.
Note
The University of Texas at San Antonio San Antonio
STAT 1053

Fall 2012
Quiz 2 :STAT 1053 001(Fall 2012):
1. The pie chart below summarizes the results of a survey of 300 randomly selected students
at a particular high school. The investigators asked about soft drink preferences at a local
high school.
SoftDrinkPreference
Co