Stat 151: Introduction to Applied Statistics
Lecture 1
Instructor:
Brian Franczak
Section AS06
CCC 5-158
MacEwan University
September 8, 2016
Todays Lecture
We have quite a bit to do today!
We will proceed in the following order:
1. We will review the key
INTRODUCTION TO
APPLIED STATISTICS I
STAT 151
LECTURE NOTES
Dr. Greg M. Wagner
i
SECTION ONE: INTRODUCTION
1.1 Objectives of the course
Learning statistical methods and their application in research (practical approach)
Summarizing and describing data
o w
SECTION THREE: DESCRIPTIVE STATISTICS: NUMERICAL METHODS
[Chapter 3 in Weiss]
Three important aspects are required to describe a frequency distribution:
1. Shape
2. Center the middle of the distribution
3. Spread variation/dispersion of the distribution
3
SECTION 4: PROBABILITY CONCEPTS AND RULES
[Chapters 4 in Weiss]
Probability concepts
Help us understand inferential statistics
In inferential statistics, we are never 100% certain that we are right in drawing a certain
conclusion, there is always some unc
SECTION TWO: DESCRIPTIVE STATISTICS: GRAPHICAL METHODS
[Chapter 2 in Weiss]
Descriptive Statistics, both graphical or numerical methods, summarize the data and present
them in a way that can be understood at a glance
o Gives you the overall, bigger pictur
Stat 151 Practice Final - Lab Questions
The following two questions refer to the materials_that were used in the Lab 3 Instructions,
"lnferences for Categorical Data."
We obtained the following 95% confidence interval result from the Titanic data:
Two sam
Math 253 - Formulas
1
i
d
1 i
t dt
i (m) ( m)
1
d ( p) p
(1 i ) (1
)
(1
) e e 0
m
1 d
p
d iv 1 v
1 vn
an
i
Sn
(1 i ) n 1
a n (1 i ) n
i
n a n (1 i ) S S (1 i )
a
n
n
L (1 i ) n PS n X
Drop Payment
X
L(1 i ) n 1 PS
n
j
1 v nj
i (m)
where j 1
j
m
n
MATH 253 THEORY OF INTEREST
Sample Midterm
Name: _
ID Number:_
Instructions:
Time allotted is 70 minutes. Books and notes may NOT be used.
Show all of your work.
QUESTION
VALUE
1
2
2
3
3
4
4
4
5
4
6
4
7
4
TOTAL
25
MARK
Note: Calculations shown are abbrevi
THEORY OF INTEREST
MIDTERM 1
FEBRUARY 10, 2005
Name:_
Put name and id number on inside page.
No notes or books allowed.
CIRCLE ONLY ONE answer per question.
Name: _
ID Number _
1) Which one of the following statements is true?
(a) a (t ) k A(t )
I n A( n
Practice Problems Ch 14 to 15
Ch 12
1)
The probability of an event is a number between 0 and 1 that reports the likelihood of the event's
occurrence. It is also a long-run relative frequency.
Answer: E
2)
In order to use P(A and B) = P(A) P(B), event A an
Chapter 1: Introduction
We will discuss, in this chapter, some basic statistical
concepts such as:
Population, sample,
Parameter, Statistics
Descriptive statistics, Statistical inference,
1
Question: What is Statistics?
Answer: The information that we gat
Stat 151- Henryk Kolacz
CHAPTER 0: SAMPLING, STUDY DESIGNS
0.1 What will you learn in this class?
Learn how to carry out simple statistical analyses
Be able to follow basic statistical arguments
Develop a critical attitude toward quantitative claims
Get f
Stat 151: Introduction to Applied Statistics
Lecture 21
Instructor:
Brian Franczak
Section AS06
CCC 5-158
MacEwan University
November 29, 2016
Todays Lecture
Today, we will discuss the analysis of variance (ANOVA) procedure.
Specifically, we will:
1. disc
Stat 151: Introduction to Applied Statistics
Lecture 22
Instructor:
Brian Franczak
Section AS06
CCC 5-158
MacEwan University
November 30, 2016
Todays Lecture
Today, we will review the post-midterm material.
Specifically, I want to provide you with a map t
PAGE 1:
LAB ASSIGNMENT 4: (47 m) STAT 151
DUE DATE: SEE COURSE OUTLINE
Name: (1m)
Lovedeep Nahal
Lab Section: (1m)
10L
Student #: (1m) 1803007
Please answer one question per page, and then either print double sided, or staple the 2 answer pages together B
Stat 151
Assignment #3
Fall 2016
Assigned Questions: 4.22, 4.26, 4.72, 4.98, 4.110, 4.138, 4.186, 4.192, 4.258, 4.262
1
Stat 151
Assignment #3
Fall 2016
2
Stat 151
Assignment #3
Fall 2016
3
Stat 151
Assignment #3
Fall 2016
4
Stat 151
Assignment #2
Assigned Questions: 3.8, 3.20, 3.22, 3.32, 3.38, 3.74, 3.78, 3.172, 3.176, 3.178
Fall 2016
Stat 151
Assignment #2
Fall 2016
Stat 151
Assignment #2
Fall 2016
Stat 151
Assignment #2
Fall 2016
LECTURE #8
Binomial distribution is for
DISCRETE RANDOM
VARIABLES, similarly, the
Standard Normal
Distribution is for
CONTINUOUS RANDOM
VARIABLES.
DENSITY CURVE
A density curve is a smooth curve that
can be drawn over a histogram to
identify a shape.
It h
LECTURE #5 PART 2
Counting rules help to determine the # of
ways events can happen.
The Basic Counting Rule (BCR)
states the following:
R=actions to be performed. If there are m1
outcomes for the 1st r and m2 outcomes for
the 2nd r then there are:
m1 m2 m
LECTURE #9
A given z is an area of size a (alpha) to its
right.
For e.g. when given: Z0.4= this means that the
area to the right is 0.4.
In order to find the area to the left given area
to the right, recall that the total area of a
normal curve must be 1
LECTURE #11
Our interest is in making a statement
about characteristics of a population.
For each sample, we call the
computed estimate a point
estimate.
For each point estimate, we can
calculate a confidence interval
CONFIDENCE INTERVAL WHEN
STANDARD DEV
LECTURE #12
The length of a C.I is the difference
btwn upper & lower limit
C.I is always centered at sample
mean, x
za
( )
n=
Margin Error, E is a f(x) of the
standard deviation of the sampling
distribution & C.L when standard
deviation is known:
E=z a (
HOW DOES THE SHAPE OF DISTRIBUTION INFLUENCE MEASURES OF
CENTRAL TENDENCY?
LECTURE#3
3 MEASURES OF CENTRAL TENDENCY
Negatively skewed is also known as left
skewed. The mean is most affected by
extreme data and is pulled farthest away
(towards the left) fr
LECTURE #10
POPULATION
SAMPLE
MEASUREMENT
FROM SAMPLE
SAMPLNG
ERROR
SAMPLING
DISTRIBUTION
We usually collect a sample from a population because it is more convenient and cost efficient compared to collecting information from the
whole population. When we
LECTURE #6
R A N D O M VA R I A B L E
It is a function or rule that assigns a
numerical value to each simple event in a
sample space.
Reflects aspect of a random experiment
that is of interest for us
It is a quantitative var whose value
depends on chance