STAT 101 - Agresti
Homework 3 Solutions
9/27/10
Chapter 4
4.27. (a) The sampling distribution of the sample proportion of heads for flipping a balanced coin once is
p
0
1
Probabilit 0.5
0.50
y
0
(b) T
11. Multiple Regression
y response variable
x1, x2 , , xk - a set of explanatory variables
In this chapter, all variables assumed to be quantitative.
Multiple regression equation (population):
E(y) =
10. Introduction to Multivariate
Relationships
Bivariate analyses are informative, but we usually need
to take into account many variables.
Many explanatory variables have an influence on any
particu
9. Linear Regression and Correlation
Data: y: a quantitative response variable
x: a quantitative explanatory variable
(Chap. 8: Recall that both variables were categorical)
For example (Wagner et al.,
8. Association between
Categorical Variables
Suppose both response and explanatory variables are
categorical. (For comparing means in Chap. 7,
response variable is quantitative, explanatory variable
7. Comparing Two Groups
Goal: Use CI and/or significance test to compare
means (quantitative variable)
proportions (categorical variable)
Group 1
Population mean
Population proportion
1
1
Group 2
2
2
6. Statistical Inference:
Significance Tests
Goal: Use statistical methods to test hypotheses such
as
Mental health tends to be better at higher levels of
socioeconomic status (SES) (i.e., there is an
5. Statistical Inference: Estimation
Goal: Use sample data to estimate values of
population parameters
Point estimate: A single statistic value that is the
best guess for the parameter value
Interval
4. Probability Distributions
Probability: With random sampling or a
randomized experiment, the probability an
observation takes a particular value is the
proportion of times that outcome would occur i
12. Comparing Groups: Analysis of
Variance (ANOVA) Methods
Response y
Categorical
Explanatory x vars
Method
Categorical
Contingency tables
Quantitative
Quantitative
Regression and correlation
Quantita
What Is Culture?
T he Conceptual Question
wag.» ;. "
The anthropologist sees culture as the shared ideas and behavrors of a group of people.
These Trobriand island women are assembling yams they hav
Formula Card Exam 2 STA3123
Steps for constructing the Condence Interval for the True Difference between the Population Means
(large, independent samples]:
2
o 02
Step 1 Gather Data from Problem, Cal
7.5 - Confidence Intervals for Means
psu.edu
Previously we considered confidence intervals for 1-proportion and
our multiplier in our interval used a z-value. But what if our variable
of interest is a
8.2 - Hypothesis Testing for a
Proportion
psu.edu
Here we will be using hypothesis tests to compare a proportion in one
group to a specified population proportion.
Examples: Research Questions
The fol
8.4 - Hypothesis Testing for a Mean
psu.edu
Hypothesis testing for one mean will use the same five steps with a
few small changes. This is procedure is known as a one sample mean
t test.
Five Step Hyp
7.2 - Confidence Intervals for
Proportions
psu.edu
Lets begin by constructing a confidence interval for a population proportion. For the following procedures, the assumption is that both
np 10np10 and
3. Descriptive Statistics
Describing data with tables and graphs
(quantitative or categorical variables)
Numerical descriptions of center,
variability, position (quantitative variables)
Bivariate d
2. Sampling and Measurement
Variable a characteristic that can vary in
value among subjects in a sample or a
population.
Types of variables
Categorical (also called qualitative)
Quantitative
Catego
STAT 101 - Agresti
Homework 2 Solutions
9/17/10
Chapter 3
3.33. The mean, standard deviation, maximum, and range all increase, because the observation for D.C.
was a high outlier. Note that these stat
STAT 101 - Agresti
Homework 1 Solutions (including optional exercises)
9/2/10
1.2. (a) Population was all 7 million voters, and sample was 2705 voters in exit poll. (b) A statistic is the
56.5% who vo
Statistics 101: Formulas Exam 2
t=
z=
se = s/ n
y 0
,
se
0
0 (1 0 )
n
= se0 =
Binomial P (x) =
n!
x (1 )nx ,
x!(n x)!
(2 1 ) z (se), se =
t = (y 2 y 1 )/se, (y 2 y 1 ) t(se),
2 =
x = 0, 1, 2, . . .
Statistics 101: Formulas Final Exam
y=
yi
n
s2 =
y
se = s/ n
y =
n
z=
y t(se)
t=
y 0
se
z=
0
=
0 (1 0 )
n
z (se) se =
n = (1 )
n = 2
z
M
(2 1 ) z (se), se =
t = (y 2 y 1 )/se,
2 =
(y y )2
n1
z
M
11. Multiple Regression
y response variable
x1, x2 , , xk - a set of explanatory variables
In this chapter, all variables are assumed to be
quantitative. Chapters 12-14 show how to incorporate
catego
10. Introduction to Multivariate Relationships
Bivariate analyses are informative, but we usually
need to take into account many variables.
Many explanatory variables have an influence on any
partic
4. Probability Distributions
Probability: With random sampling or a
randomized experiment, the probability an
observation takes a particular value is the
proportion of times that outcome would occur i