STA 4210 Exam 1 Fall 2012 PRINT Name _
For all significance tests, use = 0.05 significance level.
Q.1. A linear regression model is fit by a downtown ice cream vendor, relating Y (units of ice cream sold in a day) to X
(high temperature for the day). She
STA 4210 Exam 1 Fall 2012 PRINT Name _
For all significance tests, use = 0.05 significance level.
Q.1. A linear regression model is fit by a downtown ice cream vendor, relating Y (units of ice cream sold in a day) to X
(high temperature for the day). She
STA 4210 Exam 1 Fall 2013 PRINT Name _
For all significance tests, use = 0.05 significance level.
Q.1. A linear regression model is fit by a tropical beach seat vendor, relating Y (number of seats rented in a day) to X (noon
temperature for the day). She
STA 4210 Exam 1 Fall 2012 PRINT Name _
For all significance tests, use = 0.05 significance level.
Q.1. A linear regression model is fit by a downtown ice cream vendor, relating Y (units of ice cream sold in a day) to X
(high temperature for the day). She
1. (15 pts) Explain in your own words what is the NPV Investment Decision Rule, and why it makes sense, what it is
based on.
The NPV Decision Rule says: Maximize the NPV across all mutually exclusive available and feasible
investment alternatives, and ne
STA 4210 Exam 1 Fall 2012 PRINT Name _
For all significance tests, use = 0.05 significance level.
Q.1. A linear regression model is fit by a downtown ice cream vendor, relating Y (units of ice cream sold in a day) to X
(high temperature for the day). She
UNIDAD 1 Introduccin a la probabilidad
Edgar Vega Mondragn
Actividades/Tareas U1: Conceptos bsicos de probabilidad
Instrucciones:
Por medio de estos ejercicios reforzars importantes conceptos bsicos de
probabilidad, indispensables para profundizar en tema
Worksheet Simple Linear Regression
Bearing Capacity and Depth of Soft Soil in Paddy Fields
Goal: Describe the relationship between soil depth (X) and Bearing Capacity (Y) in Paddy fields, where:
bearing capacity is the capacity of soil to support the load
Take-Home Exam 1 Answer Sheet PRINT Name _
1)
^
Y1 = _ Y 1 = _ e1 = _
n
2)
2
^
SSE = Yi Y i = _
i =1
MSE =
SSE
= _
n2
3) 95% CI for 1: _
4) 95% CI for 0: _
5) 95% confidence interval for Ecfw_Y|X=35:_
6) 95% prediction interval for a new observation when
Influence Statistics, Outliers, and Collinearity Diagnostics
Studentized Residuals Residuals divided by their estimated standard errors (like tstatistics). Observations with values larger than 3 in absolute value are considered
outliers.
Leverage Values (
STA 4210 Homework #3 Due 11/15/14
A study obtained mortgage yields in n=18 U.S. metropolitan areas in the 1960s. The researcher obtained
the following variables and fit a linear regression model to see which factors (variables) were associated
with yield
STA 4210 Homework #4 Due 12/05/14
Part 1: Ballistic Tests on various layers of cloth panels
A study was conducted to measure the effect of the number of layers of panels in cloth fabric and the velocity
needed for half of a ballistic discharge to penetrat
Worksheet Blood Alcohol Elimination Rates in Men and Women
1-Sample and 2-Sample Inference for Means and Variances
Data: Measurements of Blood Alcohol Elimination Rate (grams/litre/hour)
n1
Males: n1 =32 Y1 ,., Yn1
Y=
Yi
i=
1
=_
n1
Z=
(Y - Y )
2
i
=_
s12
Regression Model Building Diagnostics
KNNL Chapter 10
Model Adequacy for Predictors Added Variable Plot
Graphical way to determine partial relation between
response and a given predictor, after controlling for other
predictors shows form of relation betw
Probability, Probability Distributions, and Comparison of 2 Populations
John Snow, Siskel&Ebert, Exotic Pet Species Glucose Levels
Part 1: Basic Probability from a Cross-tabulation (Contingency Table)
John Snow conducted a census of London households in t
1
Introduction to Matrices
In this section, important denitions and results from matrix algebra that are useful in regression
analysis are introduced. While all statements below regarding the columns of matrices can also be
said of rows, in regression app
Case Study: Intrinsically Linear Models
Cobb-Douglas Production Function
Source: C.W. Cobb and P.H. Douglas (1928). A Theory of Production, American Economic Review Vol. 18 (Supplement) pp. 139-165.
Theoretical Model of Production (Constant Returns to Sca
4)
A Create a data frame with the two variables; call them x and Y . Attach the data frame.
>
>
>
>
>
View(x)
STA.HW3 <- read.csv("~/Desktop/STA HW3.csv")
View(STA.HW3)
names(STA.HW3) <- c("x","Y")
attach(STA.HW3)
B. Obtain the scatterplot of Y vs. x. Fit
Model Building III Remedial
Measures
KNNL Chapter 11
Unequal (Independent) Error Variances
Weighted Least Squares (WLS)
Case 1 Error Variances known exactly (VERY
rare)
Case 2 Error Variances known up to a constant
Occasionally information known regar
Model Selection and Validation
KNNL Chapter 9
Data Collection Strategies
Controlled Experiments Subjects (Experimental Units)
assigned to X-levels by Experimenter
Purely Controlled Experiments Researcher only uses
predictors that were assigned to units
Chapter 5 Matrix Approach to Simple Linear Regression
Definition: A matrix is a rectangular array of numbers or symbolic elements
In many applications, the rows of a matrix will represent individuals cases (people, items, plants,
animals,.) and columns wi
Autocorrelation in Time Series Regression Atlantic City Casino
Profits
Data: Years 1978-2012 (n=35)
Bets ($1B)
Y=Gross Operating Profit ($100M) X 1 = Slots Bets ($1B) X2 = Table
Testing for Autocorrelation among error terms:
Estimate of based on Cochrane-
STA 4210 Homework #2 Due Oct. 17, 2014
1) Download the Explosives dataset. Fit a simple linear regression, relating the
deflection of galvonometer (Y) to the area of the wires on the coupling (X). Complete
the following parts. Conduct all tests at =0.05 s
STA 4210 Homework #1 Due Sept. 17, 2014
1)
Using the Grade Point Average data, complete problems 1.19, 1.23, 2.4 (95%CI, and level of significance .
05), 2.13 (Xh=30 in all parts), 2.23 (=0.05). Use R for all parts of analysis (construct the confidence an
Estimated Weighted Least
Squares
Profits and Market Structure for
Highly Advertising Companies
J.M. Vernon and R.E.M. Nourse (1973). Profit Rates and Market Structure of Advertising
Intensive Firms, The Journal of Industrial Economics, Vol. 22, #1, pp. 1-
Nonlinear Regression
KNNL Chapter 13
Nonlinear Relations wrt X Linear wrt s
1) Polynomial Models: E cfw_ Yi = 0 +1 X i + 2 X i2
E cfw_ Yi
=
Xi
E cfw_ Yi
0 +1 X i + 2 X i2 0 +1 +2 2 X i =h ( X i )
=
Xi
=1
0
E cfw_ Yi
1
E cfw_ Yi
=X i
2
2) Tran