Franz Rivero de la Guarda
Math 279-104
Chales L.Silber
02/10/2016
Engineering Statistic Homework 3:
a)P(X 3)
f(1)+f(2)+f(3)=0.326+0.088+0.019=0.1396
b)P(3<X<5.1)
f(4)+f(5)=0.251+0.158=0.409
c)P(X<4.5)
f(1)+f(2)+f(3)+f(4)=0.326+0.088+0.019+0.251=0.684
d)Me
Franz Rivero de la Guarda
Math 279-104
Chales L.Silber
02/03/2016
Engineering Statistic Homework 2:
a) 1-0.3=0.7
b) 1-0.25=0.75
c) 1-0.60=0.4
d) No
e) No
a) 0.40+0.45=0.85
b) 0.40+0.15=0.55
a) 1-0.45=0.55
b) 1-0.05=0.95
c)0.55-(1-0.95)=0.5
Franz Rivero de
Franz Rivero de la Guarda
Math 279-104
Chales L.Silber
02/24/2016
Engineering Statistics Homework 5
a)
P ( x >0.5 )= 3 e3 x dx=e3 ( 0.5 )=0.22
0.5
1/ 6
b)
3 x
P ( x <1/ 6 )= 3 e
3
dx=1e
( 16 )
=0.39
0
2
c)
P (1< x <2 )= 3 e3 x dx=e3 (2) e3 (1 )=0.047
d)
1
So 'Lurrod
ST 505/697R: Fall 2012 . MIDTERM EXAM: PART I, CLOSED BOOK (52 pts)
READ THE QUESTIONS CAREFULLY!
1. A shipment of biological material from a company is sent in a carton containing 1000 ampules. Data
was collected on a number of shipments whe
Review for Midterm Exam 1
Chapter 1: Linear Regression with One Predictor Variable
1. Understand the dierence between a functional relationship and a statistical
relationship among variables. Regression utilizes a statistical relationship to
predict one v
Math 644, Fall 2012
Homework 7 Due: Friday, 12/7/2012
1. Suppose Y has 5 covariates X1 , X2 , X3 , X4 , X5 , denote any model Y = 0 + i Xi +
j Xj + k Xk + by (ijk). All the models can be listed as (0), (1), (2), (3),
(4), (5), (12), (13), (14), (15), (23)
Math 644, Fall 2012
Homework 2 Due: Friday, 10/5/2012
1. (Grade Point average): Refer to the Grade Point average problem in HW1.
(a) Obtain 99% condence interval for 1 . Does it include 0? Why are we interested
in whether the interval include 0?
(b) Test,
Math 644, Fall 2012
Homework 1 Due: Friday, 09/21/2012
1. Consider the regression model Yi = 0 + 1 Xi + i , i = 1, . . . , n.
Show that Yi Y = 1 (Xi X) + i , where i = i and
= n n i .
1 i=1
2. Show that the least squares estimator b1 of 1 can be express
Math 644, Fall 2012
Review for Final Exam
The final exam will be mainly based on our lecture notes covered in the class. You can use our
textbook [KNN] as a reference and read the following relevant sections.
Chapter 1
Chapter 2 (excluding Sections 2.6 an
10/9/2014
https:/onlinecourses.science.psu.edu/stat501/print/book/export/html/26
Published on STAT 501 - Regression Methods
(https:/onlinecourses.science.psu.edu/stat501)
Home > The Analysis of Variance (ANOVA) table and the F-test
The Analysis of Varianc
Math 644, Fall 2012
Exercise 3 Due: Tuesday, 10/16/2012
1. At the website
"http:/lib.stat.cmu.edu/datasets/Plasma_Retinol"
we nd a data set with 315 observations of 14 variables, including personal characteristics, nutritional intake information, and meas
Math 3260
HW # 5
Q1) This Minitab data set contains n=120 students (columns C1 and C2 of hw5data.mtw)
selected at random from a new freshmen class at a small college. The context of this study is to
determine whether or not a students grade point average
More Regression Inferences in R
Again, assume you have the data set named Data from Problem 1.19, with explanatory
variable named ACT and response variable named GPA. Assume further that you have fit
a linear model to the data, and that the model is named
Regression Inferences in R
Assume you have the data set previously named Data from Problem 1.19, with
explanatory variable named ACT and response variable named GPA. Assume further that
you have fit a linear model to the data, and that the model is named
Stat 704
Solution #1
1.
(a).
since then
(b).
(c).
2.
(a).
(b).
3.
The 95% confidence interval is
Because the sample size is large enough, the t CI procedure is robust even if the sample is not
normally distributed.
4.
(a).
7.04
7.00
Sample Quantiles
Norma
Chapter 9 Correlation and Regression
Sections 9.1 and 9.2
In sections 9.1 and 9.2 we show how the least
square method can be used to develop a linear
equation relating two variables. The variable
that is being predicted is called the dependent
variable an
10/9/2014
ANOVA
Welcome to
R
A T
egression
nalysis
eaching
S
ite
ANOVA
The analysis of variance approach to regression analysis, also called ANOVA is useful
for more complex regression models and for other types of statistical models. To
understand this a
ECO391 Lecture Handout for 15.5, 15.6, and 15.7 Hypothesis Testing and Confidence Intervals
Several Brief Exercises to understand the computer output
Consider the following example from the old ECO 391 survey data: Lets try to build a simple model for
stu
Box Jenkins Method
Figure (1)
Since the Figure (1) shows that the Autocorrelation of original Turbidity decays very slowly at the Non-seasonal level, it is Non-stationary. And the Autocorrelation has spikes at lags 2, 4, 6. The numbers of lags are multipl