January 30 2005, Lecture 8:
CHAPTER 3: BIVARIATE AND
MULTIVARIATE DATA AND DISTRIBUTIONS
Recap on last class: We started Chapter 3 on Bivariate Data and
found scatter plots along with Pearsons Correlation Coefficient.
Example 3.3 (page 108): Soil producti
STAT2800: Lecture 16
CHAPTER 6: HYPOTHESIS TESTING
Large-Sample Tests for the Difference Between Two
Means (Section 6.5, page 423)
So far, weve only been looking at a single mean, its now time to
extend our knowledge of hypothesis tests on 2 means. Specif
STAT2800U: Lecture 17
CHAPTER 6: HYPOTHESIS TESTING
Small-Sample Tests for the Difference Between Two
Means Contd (Section 6.7, page 435)
When the Populations Have Equal Variances
Suppose that a srs of size n1 is drawn from a normal population with
unknow
STAT2800: Lecture 11
CHAPTER 4: COMMONLY USED DISTRIBUTIONS
The Central Limit Theorem (Section 4.11, page 289)
The Central Limit Theorem is by far the most important result in
statistics. Many commonly used statistical methods rely on this
theorem for the
1
1. (8 marks)
If x is a binomial random variable with parameters n and ,
n x
(1 ) n x [Note: this summation is not the formula for the
x
x=0
variance of the binomial distribution, but it was useful in proving part of the
variance.]
(6 marks)
(b) If x
STAT2800U: Lecture 20
SAS!
Lets first start off with some basic data input with SAS. Open your
SAS Stats program.
SAS CODE: (columns)
data one;
input id x;
cards;
1
2
3
4
5
6
7
8
9
10
run;
23
43
66
31
28
73
92
19
33
55
proc print;
run;
SAS OUTPUT:
Obs
1
2
STAT2800U: Lecture 19
CHAPTER 7: CORRELATION AND SIMPLE
LINEAR REGRESSION
The Least-Squares Line (Section 7.2, page 523)
Last class, we took a look at regression analysis which uses
information about x to draw some type of conclusion concerning y .
The li
STAT2800U: Lecture 23
CHAPTER 10: STATISTICAL QUALITY CONTROL
Control Charts for Variables Contd (Section 10.2,
page 764)
Recall: Control charts used for continuous variables are called
variables control charts. ( X chart, R chart, and the S chart.)
Contr
STAT2800: Midterm Review
MIDTERM REVIEW
1. Chapter 1 is pretty straight forward. Please make sure you
understand your simple summary statistics of a raw data set
(i.e., x, s 2 , ~ , quartiles, etc). Also, please understand the three
x
graphical summaries:
STAT2800: Lecture 15
CHAPTER 6: HYPOTHESIS TESTING
In Section 6.1, I described a method for testing a hypothesis about a
population mean, based on a large sample. Recall the null and
alternative hypothesis:
Null and alternative hypotheses:
A null hypothes
STAT2800: Lecture 14
CHAPTER 6: HYPOTHESIS TESTING
Example: Consider the population of weights (in kg) of all newborn
babies in Canada for a particular year. In this case, the Population
Mean is the average weight of all newborns in the population. An
inv
BUSI 1450: Statistics
Assignment # 1
Assignment # 1
1. d
2. a
3. d
4.
5. a
6. c
7. a
8. a
9. d
10.
11. a
12. b
13. a
14. b
15. c
16.
17.
18.
a)
b)
c)
d)
e)
f)
g.
Descriptive Statistics
Inferential Statistics
Inferential Statistics
Inferential Statistics
D
STAT 2800U
MIDTERM: MARCH 7, 2013
First Name
Last Name
Student Number
TAs Name (Ruth or David)
Tutorial Day/Time
INSTRUCTIONS:
Use a pen to fill in the front page. All midterm answers
should be written in ink. No white-out is allowed. If a
midterm is wri
1
1. (8 marks total)
i) A multiple-choice quiz has 200 questions, each with 4 possible answers of which only 1 is
correct. What is the probability that sheer guesswork yields from 25 to 30 correct answers for the
80 of the 200 problems about which the stu
Final Formula Sheet for STAT 2800
Lognormal Distribution:
Discrete Distribution:
Mean: x x p(x)
Variance: ( x ) p ( x)
Continuous Distribution:
Mean: x x f ( x)dx
2
x
2
where
x
(x ) f (x)dx
2
z
ln x
2
Mean: E (Y ) e 2
Variance: V (Y ) e 2 2 e 2
Variance
Midterm Formula Sheet for STAT 2800
Lognormal Distribution:
Discrete Distribution:
Mean: x x p(x)
Variance: ( x ) p ( x)
Continuous Distribution:
Mean: x x f ( x)dx
2
x
2
where
x
(x ) f (x)dx
2
z
ln x
2
Mean: E (Y ) e 2
Variance: V (Y ) e 2 2 e 2
Varian
STAT2800: Lecture 12
CHAPTER 5: CONFIDENCE INTERVALS
Large-Sample Confidence Intervals for a Population
Mean Contd (Section 5.1, page 323)
Recall: Last class, we took a look at the most common confidence
intervals:
Summary
Let X 1 ,., X n be a large ( n 3
STAT2800U: Lecture 21
MORE SAS
Example: A sample of 10 diesel trucks were run both hot and cold to
estimate the difference in fuel economy. The results, in mi/gal, are
presented in the following table. (From in-sue Emissions from
Heavy-Duty Diesel Vehicle
STAT2800U: Lecture 17
CHAPTER 6: HYPOTHESIS TESTING
Small-Sample Tests for the Difference Between Two
Means Contd (Section 6.7, page 435)
When the Populations Have Equal Variances
Suppose that a srs of size n1 is drawn from a normal population with
unknow
January 25 2006, Lecture 7:
CHAPTER 3: BIVARIATE AND
MULTIVARIATE DATA AND DISTRIBUTIONS
Scatter Plots (Section 3.1, page 100)
BIVARIATE DATA
All the data sets we have encountered up to now deal with
measurements of one variable on each of several individ
January 23 2006, Lecture 6:
CHAPTER 2: NUMERICAL SUMMARY
MEASURES
More Detailed Summary Quantities (Section 2.3,
page 79)
QUARTILES AND THE INTERQUARTILE RANGE
Definition
Separate the n ordered sample observations into a lower half and
an upper half; if n
January 18 2006, Lecture 5:
CHAPTER 2: NUMERICAL SUMMARY
MEASURES
Measures of Center (Section 2.1, page 60)
MEASURES OF CENTER FOR DATA
Definition
The sample mean of observations x1 ,., x n , denoted by x , is given
by
n
x + x2 + + xn
x= 1
=
n
The numerat