Chapter 2.10
Four steps to organizing a statistical problem:
* State: What is the practical question, in the context of the real world setting?
* Plan: What specific Statistical operations does this problem call for?
* Solve: Make the graphs and carry out
Unit 1 Lesson 3: Producing Data with Experiments Chapter 9
* The individuals studied in an experiment are often called subjects
* The explanatory variables in an experiment are often called factors
* A treatment is any specific experimental condition appl
Unit 1 Lesson 4: producing data with experiments part 2
* Randomized comparative experiments are designed to give good evidence that
differences in the treatments actually cause the differences we see in the response.
* Random assignment of subjects forms
Unit 1 Lesson 5: Picturing Distributions with Graphs
* Organize our thinking about data is the first step in dealing with flood data
* Individuals are the objects described by a set of data. Individuals may be people, but they
may also be animals or thing
Unit 1: Lesson 7: Describing Distributions with Numbers part 2
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
The interquartile range IQR, is the distance between the first and third quartiles
IQR = Q3-Q1
No single numerical measure of spread, such as IQR, is very use
Unit 1 Lesson 6: Describing Distributions with Numbers Part 1
* Measure of centers: the location of the data on the number line (mean, median)
* Measure of spread: the dispersion of data around the center: range, interquartile range,
standard deviation (s
Linear Combinations and
Linear
Multiple Comparisons of
Means
Means
(more ANOVA stuff)
Chapter 6
1
Outline
Case Studies
Inferences About Linear Combinations of
Inferences
Group Means
Group
Simultaneous Inferences
Some Multiple Comparison Procedures
Some
Re
Comparisons Among Several
Samples
Stat 511 Chap 5
Stat 511 Chap 5
1
Purpose of this chapter
s
s
s
s
In the previous chapters we compared two groups
In chapter 5 we compare 3 or more groups.
Concepts are similar, but there are things to take
advantage of,
Model Checking and
Refinement
Refinement
Chapter11
identify questions of interest, review
design of study and scope of inference
explore data graphically
data analysis
strategy
fit model
check model
carry out inferences
communicate results
Good Quote
Good
Inferential Tools for Multiple
Regression
Stat 511
Chapter 10
1
Profound Quote - Page 267
Data
analysis involves finding a good-fitting
model whose parameters relate to the
questions of interest.
2
Outline
Case
Study 1
Case Study 2
Standard Errors
Ex
Multiple Regression
an Introduction
Stat 511
Chap 9
1
case studies
meadowfoam flowers
brain size of mammals
2
case study 1: meadowfoam
flowering
designed experiment carried out in a growth
chamber
general goal of research: understand the
biology of meado
A Closer Look at Assumptions
for Simple Linear Regression
Chapter 8
Stat 511 Chap 8
1
Case Study 1: Island Area and
Number of Species
l observational study on an important issue in
biogeography and conservation biology the relationship
between island size
Simple Linear Regression: A
Model for the Mean
Chapter 7
1
An Intermediate Model
(if the groups are defined by values of a numeric variable)
Separate Means Model
Means fall on a straight line
function of the group values
Equal Means Model
2
Meaning of the
Alternatives to the t-Tools
Stat 511 Chap 4
Stat 511 Chap 4
1
Case Study 1: O-Ring Failures
Stat 511 Chap 4
2
Case Study 1: O-Ring Failures
Stat 511 Chap 4
10
5
0
1
2
3
2
1
1
2
C o u nt
15
3
4
3
Co u nt
observational study
s highly unbalanced
s far from n
A Closer Look at Assumptions
Stat 511 Chapter 3
Cloud Seeding Experiment
Case Study 1
Cloud seeding to increase rainfall
(randomized experiment)
52 cumulus clouds
At random 26 were seeded and 26 were
controls
Experimenter and pilot were blind to the
Inference Using t-Distributions
Chapter 2
511 Chap 2
1
Bumpus Data - Case Study I
Humerus length of adult sparrows in 1898
s 2 groups: perished, survived (after a storm)
s surviving sparrows were stressed
s unequal group sizes (24 and 35)
s observational
Drawing Statistical
Conclusions
Chapter 1
Stat 511 Chap 1
1
Statistical Sleuthing
carefully examining data to answer questions
of interest (Ramsey and Schafer)
l estimating the unobservable things of
science
l What is an example of an unobservable
thing
STATISTICS 370- Probability and Distribution Theory
Fall 2011
Instructor:
Dennis Tolley
Office Hours 11:00 AM- 12:00 PM, MWF
206 TMCB 422-6668
email: tolley@byu.edu
Text: Introduction to Probability and Mathematical Statistics 2nd Ed. By Bain and Engelhar
ECON 380
Sample Consumer Theory Exam Questions
The following are questions taken from some of my previous exams.
1. Demonstrate that indifference curves cannot cross by appealing to the preference
axioms.
2. If you know that Marshallian demand for x1 ( P1
Consumer Theory Pretest Helps
(by no means is this inclusive)
Be able to walk around the below circle of life in any direction:
U=V and E=I
Dont walk into the test without these equations (dont worry about names)
General Elasticity
Equation
Own Price Slut
ECON 380
Sample Producer Theory Exam Questions
The following are questions taken from some of my previous exams.
1. For a production function, q = f ( K , L) , derive the relationship between the elasticity
of q with respect to K and the measures MPK and
Producer Theory Review:
By no means is this all inclusive.
Important things to know:
Marginal Cost
Average Cost
Marginal Product of Labor
Average Product of Labor
Marginal Revenue Product
of Labor
Marginal Expense of Labor
General Elasticity Equation
RTS
Problem Set #8
Profit Maximization
1) Calculate the profit maximizing amount to produce given the following:
The firms inverse demand function:
p = 360 8q
The firms cost function:
TC = 160 + 4q
Verify the second order conditions for maximization.
2) Cal