Trident University
John T. Eastman
Module 1 Case Assignment
MAT 201: Basic Statistics
Dr. Raman Lall
01 May 2013
1. In a poll, respondents were asked whether they had ever been in a car accident. 177
respondents indicated that they had been in a car accid
TUI University
Alain labbe
ITM434 Module 2 Case
Should Yahoo have been forced to turn over Justin Ellsworth's email to his parents? And
should the same rules be applied to you? Whose job is it to enforce them? Why?
I will also explain the utilitarian and
Trident University
Xxx xxxxx
27 Nov 2015
MAT201 Case 2
Intro to Probability
1. Describe the measures of central tendency. Under what condition(s)
should each one be used?
A measure of central tendency is a single value that attempts to describe a
set of d
Trident University
Alain Labb
Module 1 SLP
MAT201 - Basic Statistics
Feb 13 feb14 feb15 feb16 feb17
25 m
25m
35m
25m
25m
Feb20 feb21 feb22 feb23 feb24
25m
40m
30m
25m
30m
For Module 1 SLP I decide to track the time it take for me to drive to work. Most da
Trident University
John T. Eastman
Module 5 Case Assignment
MAT 201: Basic Statistics
Dr. Raman Lall
29 Apr 2013
1. Find the equation of the regression line for the given data.
X: -7, -2, 5, 1, -1, -2, 0, 2, 3, -3
Y: -12, -8, 9, 1, -5, -6, -1, 4, 7, -8
An
Rodney Buckmire
MAT201 Module 1 Case Assignment
Dr. Choi
Part I:
1.
2.
3.
4.
5.
57.3%
0.7%
13.2%
4%
10.3%
Part 2:
1.
2.
3.
4.
5.
58.3 yrs
201 newspapers
There is not a mode.
5.32
255
Trident University International
LaVeeta Dinkins
ITM 442: Knowledge Management Business Intelligence
Module 3 Case Assignment
November 20, 2014
Enterprise resource planning or (ERP) is a company-wide computer software system
employed to control and organi
Rodney Buckmire
MAT201- Module SLP
Professor Choi
Part I
The type of quantitative data I am going to collect is the amount of time I spend at work in one
week.
Day 1- 11 hours
Day 2- 10 hours
Day 3- 8 hours
Day 4- 7 hours
Day 5- 11 hours
Day 6- 12 hours
D
Rodney Buckmire
MAT201- Module 3 Case
Professor Choi
1. -2.03+1.45x
2. Regression Equation(y) = bx + a
Slope(b) = (NXY - (X)(Y) / (NX2 - (X)2)
Intercept(a) = (Y - b(X) / N
The regression line of y = 4.475 x + 57.506
3. y ^ = 3.53x + 37.92
4. 3.68
5. (a) H
Trident University
John T. Eastman
Module 4 Case Assignment
MAT 201: Basic Statistics
Dr. Raman Lall
29 Apr 2013
1. 49, 34, and 48 students are selected from the Sophomore, Junior, and Senior classes with 496,
348, and 481 students respectively. Identify
Trident University
Alain Labb
Module 6
MAT201
This has been a very difficult class for me, not just because of the subject but because of
things happening in my life. I do wish I had not been so busy and managed my time more
efficiently, I would have enjo
MODULE 1: INTRODUCTION TO PROBABILITY
Case 1
Introduction to Probability
Jessica Torre Yeates
MAT 201 Basic Statistics
Dr. Claude Superville
February 24, 2014
1
MODULE 1: INTRODUCTION TO PROBABILITY
2
1. In a poll, respondents were asked whether they had
MODULE 3: FREQUENCY DISTRIBUTIONS
Module 3 SLP
Frequency Distributions
Jessica Torre Yeates
MAT 201 Basic Statistics
Dr. Claude Superville
March 24, 2014
1
MODULE 3: FREQUENCY DISTRIBUTIONS
2
Frequency Distribution
Original
30
32
32
33
40
42
45
48
62
90
T
MODULE 3: FREQUENCY DISTRIBUTIONS
Case 3
Frequency Distributions
Jessica Torre Yeates
MAT 201 Basic Statistics
Dr. Claude Superville
March 24, 2014
1
MODULE 3: FREQUENCY DISTRIBUTIONS
2
1. To get the best deal on a CD player, Tom called eight appliance st
Case, Module 2
1
1. Describe the measures of central tendency. Under what condition(s) should each one be
used?
While there are many measures of central tendency, Im going to concentrate on the
most common measures which includes the median, mean and mode
Trident University
LaVeeta Dinkins
Module 4 Case Assignment
MAT 201: Basic Statistics
June 23, 2014
1. 49, 34, and 48 students are selected from the Sophomore, Junior, and Senior classes with 496,
348, and 481 students respectively. Identify which type of
Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem
The purpose of this lecture is more modest than the previous ones. It is to state
certain conditions under which we are guaranteed that limits of sequences converge.
Denition We say
Lecture 5 Innite Series
In todays lecture, we will restrict our attention to innite series, which we will view as
special kinds of sequences. We will bring what we learned about convergence of sequence
to bear on innite series.
An innite series is a forma
Lecture 3 Limits
In the previous lecture, we used the least upper bound property of the real numbers to
dene the basic arithmetic operations of addition and multiplication. In eect, this involved
nding sequences which converged to the sum and product. In
Lecture 10 Applications of the Mean Value theorem
Last time, we proved the mean value theorem:
Theorem Let f be a function continuous on the interval [a, b] and dierentiable at
every point of the interior (a, b). Then there is c (a, b) so that
f (c) =
f (
Lecture 1: Induction and the Natural numbers
Math 1a is a somewhat unusual course. It is a proof-based treatment of Calculus,
for all of you who have already demonstrated a strong grounding in Calculus at the high
school level. You may have heard complain
Lecture 14 The Riemann integral dened
Our goal for today is to begin work on integration. In particular, we would like to
b
dene a f (x)dx, the denite Riemann integral of a function f on the interval [a, b]. Here
f should be, at least, dened and bounded o
Lecture 12: Formal Taylor Series
For the last several lectures, we have been building up the notion of Taylor approximation. We proved
Theorem Let f, f , f , . . . , f (n2) be dened and continuous everywhere on a closed
interval I having c in the interior
Lecture 18: Taylors approximation revisited
Some time ago, we proved
Theorem Let f be a function on an interval [a, b] and c a point in the interior of the
interval. Suppose that f is n 2 times continuously dierentiable on [a, b], that the n 1st
derivativ
Lecture 15: Integrability and uniform continuity
Sorry for this abbreviated lecture. We didnt complete the proof of properties of the
Riemann integral from last time.
We could write the denition of continuity as follows: A function f is continuous at
x if
Lecture 11: Exponentiation
Todays lecture is going to focus on exponentiation, something you may consider one
of the basic operations of arithmetic. However there is subtle limiting process that takes
place when dening exponentiation which we need to full