4
Numerical Descriptive
Techniques
Recall from Chapter 1
Population : a collection of persons, objects, or items of interest
Parameter : is a descriptive measurement about a population
Sample
: a portion of the whole
Statistic : is a descriptive measureme

Chapter 6.1
6.1 a Relative frequency approach
b If the conditions today repeat themselves an infinite number of days rain will fall on 10% of the next days.
6.2 a Subjective approach
b If all the teams in major league baseball have exactly the same player

Chapter 4
4.40 First quartile: L25 (15 1)
Second quartile: L50 (15 1)
25
= (16)(.25) = 4; the fourth number is 3.
100
50
= (16)(.5) = 8; the eighth number is 5.
100
75
= (16)(.75) = 12; the twelfth number is 7.
100
Third quartile: L75 (15 1)
4.41 30th per

Chapter 3: Graphical Descriptive Techniques II
3.1 Describe a Set of Interval Data
Example 3.1: Analysis of Long-Distance Telephone Bills:
Following deregulation of telephone service, several new companies were created to compete
in the business of provid

Chapter 6.3
6.47
6.48
6.49
6.50
6.51
6.52
a P(R and R) = .81
b P(L and L) = .01
c P(R and L) + P(L and R) = .09 + .09 = .18
d P(Rand L) + P(L and R) + P(R and R) = .09 + .09 + .81 = .99
6.53 a & b
c
0 right-handers
1
1 right-hander
3
2 right-handers
3
3 r

Chapter 7
7.1 a 0, 1, 2,
b Yes, we can identify the first value (0), the second (1), and so on.
c It is finite, because the number of cars is finite.
d The variable is discrete because it is countable.
7.2 a any value between 0 and several hundred miles

Chapter 4: Numerical Descriptive Techniques
4.1 Measures of Central Location
Mean: the average of a group of numbers
Population Mean
Sample Mean
Example 4.1.1: A sample of 10 students from your business class was asked how many hours
they spent last week

Chapter 4
4.19 x
s
2
i
x) 2
n 1
2
x
i
n
(x
i
x) 2
n 1
4.21 x
s
i
n
(x
4.20 x
s
x
2
x
i
n
(x
i
x) 2
n 1
9 3 7 4 1 7 5 4 40
=
=5
8
8
[(9 5) 2 (3 5) 2 . (4 5) 2
46
=
= 6.57
7
8 1
4 5 3 6 5 6 5 6 40
=
=5
8
8
[( 4 5) 2 (5 5) 2 . (6 5) 2
8
= = 1.14
7
8

Chapter 1: What is Statistics?
Case 12.1 (Page 4) Pepsis Exclusivity Agreement with a University (see Chapter 12)
In the last few years, colleges and universities have signed exclusivity agreements with a variety of private
companies. These agreements bin

Key_QNM222 Homework#3 (Chapter 4)
1. (#43/p.136) Wendys Old Fashion Hamburgers offers eight different condiments (mustard,
ketchup, onion, mayonnaise, pickle, lettuce, tomato, and relish) on hamburgers. A store manager
collected the following information

Key_QNM222 Homework#2 (Chapter 3)
Part I. Multiple Choice:
1. The arithmetic mean is computed by:
A. Summing the values and dividing by the number of values.
B. Finding the middle observation and dividing by 2.
C. Finding the value that occurs most often.

Key_QNM222 Homework#1(Chapters 1 & 2)
Part I. Multiple Choice:
1. The collection of all possible individuals, objects, or measurements is called:
A. A sample.
B. A ratio measurement.
C. A population.
D. An inference.
2. Techniques used to organize, summar

QNM222 Assignment#2
Total: 25 marks
Question 1
7 marks
(75/135) A recent survey reported in BusinessWeek concerned the salaries of CEOs at large corporations, and whether company shareholders made
or lost money. If a company is randomly selected from the

Chapter 12
12.3 a x t / 2 s / n = 510 2.064(125/ 25 ) = 510 51.60; LCL = 458.40, UCL = 561.60
b x t / 2 s / n = 510 2.009(125/ 50 ) = 510 35.51; LCL = 474.49, UCL = 545.51
c x t / 2 s / n = 510 1.984(125/ 100 ) = 510 24.80; LCL = 485.20, UCL = 534.80
d. T

Chapter 11
11.1
H 0 : The drug is not safe and effective
H1 : The drug is safe and effective
11.2
H 0 : I will complete the Ph.D.
H1 : I will not be able to complete the Ph.D.
11.3
H 0 : The batter will hit one deep
H1 : The batter will not hit one deep
1

Chapter 2: Graphical Descriptive Techniques I
2.1 Types of Data and Information
Definitions:
A variable is some characteristic of a population or sample. (e.g. student grades) Typically denoted
with a capital letter: X, Y, Z The values of the variable ar

Chapter 6: Probability
Appendix B Introdution into Probability
Counting Rules for Multiple-Step Experiments: If an experiment can be described as a sequence of k
steps with n1 possible outcomes on the first step, n2 possible outcomes on the second step, a

Chapter 7: Random Variables &
Discrete Probability Distributions
7.1 Random Variables & Probability Distributions
Random Variable: is a function or rule that assigns a number to each outcome of an experiment
(ex. Instead of talking about the coin flipping

Chapter 9
P p
9.30a P( P > .60) = P
p(1 p) / n
= P(Z > 3.46) = 0
(.5)(1 .5) / 300
.60 .5
P p
.60 .55
= P(Z > 1.74) = 1 P(Z < 1.74)
b. P( P > .60) = P
p(1 p) / n
(.
55
)(
1
.
55
)
/
300
= 1 .9591 = .0409
P p
c. P( P > .60) = P
p(1 p) / n
= P(Z > 0)

Terminologies: (up to Ch12)
Population mean
Sample mean
Population standard deviation
Sample standard deviation
Population proportion
Sample proportion
Sample size
Error of estimation
Null hypothesis
Alternative hypothesis
Test Statistic: ( z critical &

Chapter 4: Numerical Descriptive Techniques
4.1 Measures of Central Location
Mean: the average of a group of numbers
Population Mean
=
=
+ +
Sample Mean
=
=
+ +
Example 4.1.1: A sample of 10 students from your business class was asked how many hours