Chapter 5
Measures of Variability
Measuring Variability
Central Tendency
Numbers that describe what is typical or
average (central) in a distribution
Measures of Variability
Numbers that describe diversity or variability in
the distribution
Measuring V
Measures of
Central
Tendency
Whats Central Tendency?
Quick way to summarize data
Category Value That Is Most
Usual, Common, Typical
Learning Objectives
Measures
Mode
Median
Mean Distribution
Images of Frequency
Normal
Skewed (Positive/ Negative)
Location
Chapter 7
Sampling and Sampling Distributions
Sampling
Population
A group that includes all the cases in which the
researcher is interested
Individuals, objects, or groups
Sample
A relatively small subset from a population
Notation
Table 7.1 Sample a
Chapter 2
The Organization of Information:
Frequency Distributions
Frequency Distribution
A table reporting the number of observations
falling into each category of the variable
Frequency Distribution
Proportions
A relative frequency obtained by dividin
Chapter 6
The Normal Distribution
Normal Distribution
A bell-shaped and symmetrical theoretical
distribution, with the mean, the median, and
the mode all coinciding at its peak and with
frequencies gradually decreasing at both ends
of the curve
Normal Di
Standard Deviation
Average Distance from the Mean
On Average
How Far Is Each Score from Mean?
Tells you how dispersed your data is
Interpretation
Lowest Value = 0
No Variation in Values
Highest Value = Infinity
More Variation as Standard Deviation Increa
Chapter 9
Testing Hypotheses
Statistical Hypothesis Testing
A procedure that allows us to evaluate
hypotheses about population parameters
based on sample statistics
Statistical Hypothesis Testing
Hypothesis
The average price of gas in California is hig
Chapter 10
Cross-Tabulation Relationships
Between Variables
Introduction
Bivariate Analysis
A statistical method designed to detect and
describe the relationship between two variables.
Cross-Tabulation
A technique for analyzing the relationship
betwee
Measures of Dispersion
Learning Objectives:
1. Concept of Dispersion
2. Measures of Dispersion
- How to Calculate
- How to Interpret
- How to Select Measure
Central Tendency Versus Dispersion
Mode=7
Distribution of Ambulance
Response Times
Mode=7
X 7
Md 7
Chapter 1
The What and the Why of Statistics
The Research Process
Asking the
Research
Question
Contribute
new evidence
to literature
and begin
again
Evaluating the
Hypotheses
Examine a social
relationship, study the
relevant literature
THEORY
Analyzing
Da
Have your textbook handy,
you will need it for reference
to equations and the standard
normal table. Be sure to bring
your textbook to every class
hereafter.
The Normal Curve
Learning Objectives
1. Define Normal Distribution
2. Locate Observed Score on No
ConsiderthecrabsdatasetwithinRpackageMASS
library(MASS) library(reshape2)
# Warning: package 'reshape2' was built under R version 3.2.3
summary(crabs)
# sp
sex
index
FL
RW
# B:100 F:100 Min. : 1.0
Min. : 7.20 Min. : 6.50 # O:100 M:100 1st Qu.:13.0 1st Qu.
Alexandria Costa
Stat 2522, Homework 1
2.2
If a forester wants to estimate the total number of trees with diameters exceeding 12
inches and a map of the farm is available, since this is a tree farm, most trees are planted in rows
with a fixed amount of sp
Good Evening Students! Please Mute Mic's when not speaking; Turn On your WEBCams please, thanks!
TONITE IS THE LAST LIVE WEBEX CLASS
TONITE'S LEARNING OBJECTIVES
UNDERSTAND HOW TO USE MULTIPLE REGRESSION MODEL
UNDERSTAND HOW TO EVALUATE THE ACCURACY OF RE
Hi Students! Please Mute Mic's when not speaking; Turn On your WEBCams please, thanks!
TONITE IS THE LAST LIVE WEBEX CLASS
TONITE'S LEARNING OBJECTIVES
UNDERSTAND HOW TO USE MULTIPLE REGRESSION MODEL
UNDERSTAND HOW TO EVALUATE THE ACCURACY OF REGRESSION M
Exam
Name_
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Evaluate the expression.
1) - -12 - -16 - 9
A) 13
B) 37
Simplify. Leave answer with exponent.
2) (2y)4 (2y)9
A) 4y13
B) (2y)13
Add or subtrac
Exam 2 Practice Questions-Duc Anh Le Dao
11/4/15, 9:43 PM
Instructor: Prince Afriyie
Course: Quantitative Methods for
Business II, Fall 2015
Student: Duc Anh Le Dao
Date: 11/4/15
Assignment: Exam 2 Practice
Questions
Find the relative extreme points of th
Exam 2 Practice Questions-Duc Anh Le Dao
11/5/15, 12:40 AM
Instructor: Prince Afriyie
Course: Quantitative Methods for
Business II, Fall 2015
Student: Duc Anh Le Dao
Date: 11/5/15
Assignment: Exam 2 Practice
Questions
Find the relative extreme points of t
Exam 2 Practice Questions-Duc Anh Le Dao
11/5/15, 12:37 AM
Instructor: Prince Afriyie
Course: Quantitative Methods for
Business II, Fall 2015
Student: Duc Anh Le Dao
Date: 11/5/15
Assignment: Exam 2 Practice
Questions
Find the relative extreme points of t
Fractional Factorials
One of the disadvantages of factorial experiments is that they can get large very quickly
with several levels each of several factors. One technique for reducing the size of the factorial to
more manageable levels is fractional repli
> tmp <- data.frame(y=sample(1:8), a=factor(letters[c(1:4,1:4)])
> tmp
y a
1 6 a
2 5 b
3 7 c
4 8 d
5 2 a
6 3 b
7 4 c
8 1 d
>
> tmp.lm <- lm(y ~ a, data=tmp)
> tmp.aov <- aov(y ~ a, data=tmp)
>
> all.equal(tmp.lm, tmp.aov)
[1] "Attributes: < Component 1: L
Statistics 8003, First Exam, October 14, 2009
Name:
1
Statistics 8003
First Exam, October 14, 2009
Open Book, Calculator Needed
This example is taken from Kellers book. Please read the problem specication on the attached
sheet.
1. Look at the scatterplot
315
_._—————————-—
7 A word of warning. In the example we ﬁtted a multiple regression of Y on
four variables X1, X2, X3, X4. The methods by which the regression coefﬁcients
b,— were computed apply only if the X,- are mutually orthogonal, as is the case in
An Intermediate Course in Statistical Computing
July 21, 2010
Richard M. Heiberger
1
An Intermediate Course in Statistical Computing
Richard M. Heiberger
Temple University
I have just completed teaching the latest incarnation of a course on Statistical Co
Fractional Factorials
One of the disadvantages of factorial experiments is that they can get large very quickly
with several levels each of several factors. One technique for reducing the size of the factorial to
more manageable levels is fractional repli
> tmp <- data.frame(y=sample(1:8), a=factor(letters[c(1:4,1:4)])
> tmp
y a
1 6 a
2 5 b
3 7 c
4 8 d
5 2 a
6 3 b
7 4 c
8 1 d
>
> tmp.lm <- lm(y ~ a, data=tmp)
> tmp.aov <- aov(y ~ a, data=tmp)
>
> all.equal(tmp.lm, tmp.aov)
[1] "Attributes: < Component 1: L
Chapter 3
Richard M. Heiberger and Burt Holland
Statistical Analysis
and Data Display
An Intermediate Course
with Examples in R
Statistics Concepts
In this chapter we discuss selected topics on probability. We dene and graph several
basic probability dist