Midterm
Statistics Business
1440264
FALL 2016/2017
Date: 17/11/2016
Time: 16:00 17:15
Semester: FALL 2016/17
University of Sharjah
College of Sciences
Department of
Mathematics
Midterm: Business Statistics
Name:
I. D. #:
Instructor: Dr. Samir
Section #:
Midterm Preparation
Question 1 [Out of 10 Marks]
A study was conducted to observe the climate changes. To this end, the temperature was recorded
during a specified week and at different places in the north of the United States.
The records were as follows
LECTURE NOTES
BUSINESS STATISTICS
SPRING SEMESTER OF 2015  2016
SECTION 2.3
DISCRETE RANDOM VARIABLES
DR. MOHAMED TAHIR, DEPARTMENT OF MATHEMATICS, UNIVERSITY OF SHARJAH
DEFINITION OF A RANDOM VARIABLE
A random variable is a function or a rule that assig
SECTION 2.2
CONDITIONAL PROBABILITY AND INDEPENDENCE
INTRODUCTION
Subsequent to the initial assignment to the
probability of an event, partial information
relevant to the outcome of the experiment may
become available. Such information may cause
us to rev
SECTION 2.4
THE NORMAL DISTRIBUTION
EXAMPLES OF A CONTINUOUS RANDOM VARIABLE
The October 2015 profit for a randomly selected
computer store.
The amount of money charged on a Visa card by a
randomly selected Visa cardholder during a given
month.
The yea
SECTION 2.1
PROBABILITY OF AN EVENT
INTRODUCTION
Probability is used in business to evaluate financial
and decisionmaking risks. Every decision made by
management carries some chance for failure.
Managers are often faced with answering questions
relate
INTRODUCTION
A test of hypotheses is a method for deciding
between two statistical hypotheses.
An hypothesis is a statement that describes a
belief, a theory, a guess or a claim.
For example,
the belief that the mean of the weekly sales made
by all salesp
SECTION 3.3
THE ONESAMPLE T TEST
INTRODUCTION
The onesample T test is used to test the mean of
a Normal population with unknown standard
deviation, using a small sample size (n < 30).
It can be used to test any of the following three
tests:
TEST #1: H0:
BUSINESS STATISTICS
SPRING SEMESTER OF 20152016
LECTURE NOTES
SECTION 3.1
A LARGESAMPLE CONFIDENCE INTERVAL
FOR A POPULATION MEAN
DR. MOHAMED TAHIR, DEPARTMENT OF MATHEMATICS , UNIVERSITY OF
SHARJAH
INTRODUCTION
Inferential Statistics is a body of meth
CHAPTER 4
DISCRETE PROBABILITY
DISTRIBUTION
DISCRETE PROBABILITY
DISTRIBUTION
Learning Objectives:
:
At the end of the lecture, you will be able to :
 select an appropriate discrete probability
distribution
* binomial distribution or
* poisson distributi
CHAPTER 2
DESCRIPTIVE STATISTICS
L2  Graphical display of Data
Learning Objectives:
At the end of the lesson, students should be able to:
Construct and interpret pictorial and tabular display of data
Pictorial & Tabular Methods
1. StemandLeaf Displays:
Chapter 1 Probability
(Week 12)
L1:
Sample Spaces and Events
Probability of Events
Counting Rule
L3: Permutations and
Combinations
L4: Conditional Probability
Independent
L2:
L5: Multiplication Rule
Total Prob. Teorem
Bayes Theorem
Learning Objectives:
A
Chapter 1 Probability
(Week 12)
Lecture 2:  Probability of Events
 Counting Rule
Learning Objectives
At the end of the session student should be able to:

Determine the probability of events
Interpret and understand the properties of probability
and u
Chapter 1 Probability
week 2
Lecture 5:
Multiplicative Law
Total Probability Theorem
Bayes Theorem
Jan 2009
1
Learning Objectives
Apply multiplicative law to fine probability of certain
events.
Apply a total probability rule to find the probability of an
Chapter 1 Probability
(week 12)
Lecture 3: Permutations and
Combinations
Jan 2009
Learning Objectives
After the lesson student should be able to
 Use the counting rule: permutation and
combination, to find the number of ways and
possible outcomes of a g
CHAPTER 2
DESCRIPTIVE STATISTICS
L1 Numerical Summary of Data
L2  Data Display and summary
1
Learning Objectives:
At the end of the lesson, students should be able to:
 Explain
the concepts of
 sample mean, population mean,
 sample variance, populati
Chapter 1 PROBABILITY
week 2
Lecture 4: Basic Probability Laws:
Conditional Probability;
Independent Events;
Jan 2009
1
Learning Objectives
Understand and apply probability properties to
calculate probabilities of specific events.
Calculate the conditiona