Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
ST 352
Midterm 100 points
Spring 2007
Form 1
PRINT Name
SIGN Name
Lecture Time
Last 3 digits of student ID
Part I: Multiple Choice (57 points 2 points each unless otherwise stated). Darken t

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
January 13, 2010
Lab Assignment #1
Problem I: The Taste Test
1. The random variable is how many participants in the lab correctly identified the cup that
contained the different brand of bev

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
February 10, 2011
Lab Assignment 3
Problem 1
1. A legitimate population that Dans father is interested in would be all students and
Oregon State University.
2. The value of the best estimate

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
February 17, 2011
Lab Assignment 4
A
1.
2. The relationship between and hand span and foot length is linear and positive. The points
on the graph are not very tightly packed, showing that th

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
March 10, 2011
Lab Assignment 6
Problem 1
1.
a. We need to check for high correlation between the explanatory variables because
if two explanatory variables are highly correlated, they are e

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
ST 352
Midterm 87 points
Spring 2008
Form 1
PRINT Name
SIGN Name
Circle Lecture Time: 8 AM
1 PM
Last 3 digits of student ID number:
Part I: Multiple Choice (54 points 2 points each, except w

Lesson 18: Inference using Simple Linear Regression
Motivation: Assessing conditions is an important step in doing a simple linear regression analysis. If the
conditions arent met, conclusions about a population based on the analysis of the sample data ma

Lesson 20: The Analysis of Variance Table in the Regression Setting
Motivation: We briefly referred to this table that appears at the bottom of the output from a simple linear regression
analysis. What is this table? Its called the Analysis of Variance Ta

Lesson 22: Assessing conditions in a Multiple Linear Regression analysis
Motivation: In Lesson 21, we learned how to do a multiple linear regression analysis. Any conclusions from
that analysis to a population of interest (all countries) will only be vali

Lesson 21: Multiple Linear Regression
Motivation: Weve spent a lot of time discussing simple linear regression. In many situations, there is more
than one explanatory variable that helps explain the response variable. When we have more than one
explanator

Lesson 23: Regression with a categorical explanatory variable
Motivation: So far, all of our variables in regression have been quantitative. But, we could have categorical
explanatory variables in regression. (And even a categorical response variable, but

Lesson 17: Inference using Simple Linear Regression: the SLR model and assessing
the conditions of the model
Motivation: Simple linear regression refers to an analysis procedure when there is a quantitative response
variable and one explanatory variable.

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
January 13, 2010
Lab Assignment #1
Problem I: The Taste Test
1. The random variable is how many participants in the lab correctly identified the cup that
contained the different brand of bev

Lesson 16: Transforming (or re-expressing) data
Motivation: In order to use the results of a simple linear regression to make accurate statements about
what is happening, certain conditions must exist. One such condition is that there are no influential o

Lesson 14: Residuals and Residual Plots
Motivation: In Lesson 13, we learned how to describe the linear relationship between the response and
explanatory variable with an equation. The least-squares regression line fits between the points, but not all of

Lesson 15: A strategy for dealing with Outliers
Motivation: Outliers (unusual data points) can have a strong influence on analysis in terms of affecting the
slope and/or y-intercept in the regression equation. A single point can change the analysis in suc

Lesson 19: Simple Linear Regression Analysis Involving a Transformation
Motivation: Conclusions from a simple linear regression analysis are valid to a population of interest only if the
conditions listed on the tan sheet (Steps for doing a Simple Linear

Lesson 1: Inference for One-Sample problems for means or medians
Motivation: There are many different methods to use when a problem involves making an inference.
Which method is appropriate to use depends on the type of variable of interest (quantitative

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
February 24, 2011
Lab Assignment 5
Problem 1
Chapter 10, #12:
a.
This plot shows a positive direction and an association that has a little scatter but is not
straight, it is bent. The bend i

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
ST 352
Midterm solutions 100 points
Spring 2007
Form 1
Part I:
1. a
2. a
3. b
4. b
5. c
6.
7.
8.
9.
10.
d
b
c
a
c
11.
12.
13.
14.
15.
a
c
b
b
e
16.
17.
18.
19.
20.
d
b
e
b
e
21.
22.
23.
24.

Tegan Clark
ST 352
Statistical Methods
Professor Jeffrey Kollath
ST 352
Midterm solutions
Spring 2008
Form 1
Part I:
1. b
2. b
3. c
4. a
5. a
6.
7.
8.
9.
10.
b
d
d
b
d
11.
12.
13.
14.
15.
d
e
a
d
d
16.
17.
18.
19.
20.
b
a
b
a
d
21.
22.
23.
24.
25.
a
e
a
a

Lesson 7: Inference for comparing two-proportions: a randomization test and
the twoprop macro
Motivation: In Lessons 5 and 6, we discussed different inference methods when we had a categorical
variable of interest with two categories and one population. I

Lesson 6: Inference for a population proportion using the bootstrap methods
Motivation: In Lesson 5, we learned how to obtain the exact p-value for a one-proportion problem by using
the binomial formula. In this lesson, well discuss the bootstrap method t

Lesson 3: Inference for comparing means/medians from Paired data
Motivation: There are studies performed on cases where each case appears in the two comparison groups.
Such studies could be observational or experiments, but having the same case in the two

Lesson 4: Discrete Random Variables and the Binomial Distribution
Motivation: Until now, we have focused on quantitative variables of interest. We'll now turn our attention to
categorical variables of interest. In this lesson, we'll introduce another type

Lesson 5: Inference for a population proportion using technology
Motivation: When a variable of interest is categorical with two categories, we may be interested in the
proportion of cases in the population of interest in one of the two categories (whethe

Lesson 2: Inference for Two-Sample problems comparing means or medians
Motivation: If an inference problem involves a quantitative response variable and a categorical
explanatory variable with two categories, two-sample methods will be used.
What you need

Lesson 13: The Least-Square Regression Line and Equation
Motivation: In the past two lessons, weve mentioned fitting a line between the points. In this lesson, well
discuss how to best fit a line between the points if the relationship between the response