149
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 hypothesis is
52
Lecture 5:
CHAPTER 2: PROBABILITY
Random Variables (Section 2.4, page 88)
Definition:
A numerical characteristic whose value depends on the outcome of a
chance experiment is called a random variable. A random variable
is discrete if its possible values
163
Lecture 17
CHAPTER 6: HYPOTHESIS TESTING
Small-Sample Tests for the Difference Between
Two Means Contd (Section 6.7, page 430)
When the Populations Have Equal Variances
Suppose that a srs of size n1 is drawn from a normal population with
unknown mean
215
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 Vehicles, J.
201
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
id
x
1
2
238
Lecture 23:
CHAPTER 10: STATISTICAL QUALITY
CONTROL
Control Charts for Variables Contd (Section
10.2, page 757)
Recall: Control charts used for continuous variables are called
variables control charts. ( X chart, R chart, and the S chart.)
Control cha
227
Lecture 22:
CHAPTER 10: STATISTICAL QUALITY
CONTROL
Control Charts for Variables (Section 10.2, page
757)
Recall: Control charts used for continuous variables are called
variables control charts. ( X chart, R chart, and the S chart.)
Control charts us
189
Lecture 19:
CHAPTER 7: CORRELATION AND SIMPLE
LINEAR REGRESSION
The Least-Squares Line (Section 7.2, page 517)
Last class, we took a look at regression analysis which uses
information about x to draw some type of conclusion concerning y .
The linear m
175
Lecture 18:
CHAPTER 7: CORRELATION AND SIMPLE
LINEAR REGRESSION
Correlation (Section 7.1, page 500)
BIVARIATE DATA
Most of the data sets we have encountered up to now deal with
measurements of one variable on each of several individuals (i.e.
incomes
155
Lecture 16:
CHAPTER 6: HYPOTHESIS TESTING
Large-Sample Tests for the Difference Between
Two Means (Section 6.5, page 418)
So far, weve only been looking at a single mean, its now time to
extend our knowledge of hypothesis tests on 2 means. Specificall
139
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
investig
127
Lecture 13:
CHAPTER 5: CONFIDENCE INTERVALS
Small-Sample Confidence Intervals for a
Population Mean Contd (Section 5.3, page 321)
Recall: Last class we introduced the students t-distribution which is
used when the population is approximately normal an
115
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 30) ran
105
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 their va
95
Lecture 10:
CHAPTER 4: COMMONLY USED
DISTRIBUTIONS
The Exponential Distribution Contd (Section
4.7, page 261)
Recall: Last class we introduced the Exponential distribution:
Definition
A variable X is said to have an exponential distribution with
parame
86
Lecture 9:
CHAPTER 4: COMMONLY USED
DISTRIBUTIONS
The Normal Distribution- Contd (Section 4.5, page
240)
Recall: Last class we focused on applications with the use of the
Normal Distribution. Now lets take a look at the mean and variance
for a Normal R
79
Lecture 8:
CHAPTER 4: COMMONLY USED
DISTRIBUTIONS
The Normal Distribution (Section 4.5, page 240)
Definition
A continuous variable x is said to have a normal distribution with
parameters and , where and 0 , if the
density function of x is
f ( x)
2
1
e
72
Lecture 7:
CHAPTER 4: COMMONLY USED
DISTRIBUTIONS
The Binomial Distribution Contd (Section 4.2,
page 202)
THE BINOMIAL DISTRIBUTION (discrete)
Recall: p(x) = proportion of batches with x Ss (successes)
Summary for Binomial Distribution:
If a total of n
62
Lecture 6:
CHAPTER 2: PROBABILITY
Random Variables Contd (Section 2.4, page 88)
Recall: Last class we started setting up discrete random
variables and we also worked with continuous random variables,
lets continue with both:
Example: Two cards are draw
38
Lecture 4:
CHAPTER 2: PROBABILITY
Counting Methods (Section 2.2, page 62)
Permutations:
In how many ways can you order the letters A, B, C?
Five lifeguards are available for duty one Saturday
afternoon. There are three lifeguard stations. In how many
w
26
Lecture 3:
CHAPTER 2: PROBABILITY
A chance experiment, also called a random experiment, is
simply an activity or situation whose outcomes, to some
degree, depend on chance. To decide whether a given
activity qualifies as a chance experiment, ask yourse
12
Lecture 2:
CHAPTER 1: SAMPLING AND DESCRIPTIVE
STATISTICS
Graphical Summaries (Section 1.3, page 25)
Stem-and-Leaf Plots
Most of us already know what a histogram looks like:
A histogram tells us how many observations fall into a
particular class, but w
1
Lecture 1:
CHAPTER 1: SAMPLING AND DESCRIPTIVE
STATISTICS
Question: What is a statistic?
Answer: Any numerical summary measure based on data
from a sample; contrasts with a parameter which is based on
data from a population.
Basic Idea of Statistics
The