STAT 151 F1
Lab 3
NEILL, Katrina
ID# 1459550
1a. The results of the study should not be generalized to other young and elderly subjects. In
order to assume independent sampling, the subjects must be chosen at random, and these ones
volunteered for the stu
STAT 151 F1
Lab 2
NEILL, Katrina
ID# 1459550
1a. I expect the relationship between caratage of the diamond stone and its price to be linear. It
would make sense that as the weight of the diamond increases, its size would be increasing.
Obviously it would
STAT 151 F1
Lab 4
NEILL, Katrina
ID# 1459550
1a. The eggs were not collected randomly. Even though the scientist wandered around looking
for nests, she may have missed some nests, which could affect the study. Random sampling
could be fixed by first locat
STAT 265, Section A1 Final Exam
Instructor: B. Schmuland
When: Tuesday December 13, 2 pm 5 pm
Where: Universiade Pavilion (aka Butterdome)
I will hold drop-in office hours in my office (CAB
473) on Monday December 12 from 9am to 5pm,
and Tuesday December
Bivariate Normal Density
Let X and Y be independent standard normal random
variables. Theirjoint
density, written as a function of
x
1
the vector x =
, is f(x, y) =
exp(xT x/2).
2
y
1
For any 1 < < 1, the matrix =
is
1
symmetric and positive definite,
A multinomial experiment possesses the following properties:
1. The experiment consists of n identical trials.
2. The outcome of each trial falls into one of k classes or cells.
3. The probability that the outcome of a single trial falls into
cell i is pi
CHAPTER 4: DISCRETE DISTRIBUTIONS
4.1 The Bernoulli Distribution
Consider a random experiment that can result in one of two
possible outcomes, one outcome is labelled success, the
other outcome is labelled failure. The probability of
success is constant a
CHAPTER 1: DESCRIPTIVE STATISTICS
1.1 Introduction
Example 1: Making Steel Rods
Consider a machine that makes steel rods for use in optical storage
devices. The specification for the diameter of the rods is 0.450.02
cm. The machine makes 1000 rods per hou
CHAPTER 3: RANDOM VARIABLES
3.1 Introduction
Random variable assigns a numerical value to each
outcome of a random experiment.
Random Variable
Possible
values
X= Number of defectives in a random
sample of two computer chips drawn
from a shipment
X= the wa
CHAPTER 7: SAMPLING DISTRIBUTIONS
7.1 Statistics
Population parameter- a numerical characteristic of a
population of interest
Examples of population parameters: = (population
mean), or =2 (population variance), or =p (population
proportion).
Statistic any
CHAPTER 2: PROBABILITY
2.1 Basic Probability
Random experiment results in one of a number of
possible outcomes. The outcome that occurs cannot be
predicted with certainty.
Examples: Tossing a coin, rolling a die.
Sample Space (S) - the list of all possi
CHAPTER 5: CONTINUOUS DISTRIBUTIONS
5.1 The Uniform Distribution
Random variable X follows the uniform distribution
between a and b if its density function f(x) is given by
f ( x) =
1
ba
for a x b and f(x)=0 elsewhere.
Density curve of a
uniform distribut
CHAPTER 6: NORMAL DISTRIBUTION
6.1 Probabilities for Normal Distributions
X has a normal distribution if its probability density f(x)
is given by
1 x
(
1
f ( x)
e 2
2
)2
,
where - < x < and > 0.
Conclusions: Any normal distribution is specified by two
Name: _
Student No.: _
University of Alberta
Department of Mathematical and Statistical Sciences
Statistics 235 Final Examination SOLUTION
Date: Winter 2009
Time: 9:00-12:00
Instructions: (READ ALL INSTRUCTIONS CAREFULLY.)
1. This is a closed book exam. Y
University of Alberta
Department of Mathematical and Statistical Sciences
Statistics 235 Final Examination SOLUTION
Date: Winter 2010
Time: 9:00-12:00
Instructions: (READ ALL INSTRUCTIONS CAREFULLY.)
1. This is a closed book exam. You are permitted to use
University of Alberta
Department of Mathematical and Statistical Sciences
Statistics 235 Final Examination Version A
Date: Winter 2010
Time: 9:00-12:00
Instructions: (READ ALL INSTRUCTIONS CAREFULLY.)
1. This is a closed book exam. You are permitted to us
Name: _
Student No.: _
University of Alberta
Department of Mathematical and Statistical Sciences
Statistics 235 Final Examination Version A
Date: Winter 2009
Time: 9:00-12:00
Instructions: (READ ALL INSTRUCTIONS CAREFULLY.)
1. This is a closed book exam.
Chapter 12 Simple Linear Regression
Notation:
- bivariate sample: cfw_ (x1, y1), (x2, y2), , (xn, yn)
- sample means: x , y
- sample std dev.: sx, sy
- sums of squares and cross-products:
x
x
n
y
y
2
SXX =
(x
SYY =
(y
SXY =
( xi x )( yi y) xy
i
STAT 235 FINAL EXAM FORMULA SHEET
Summaries:
n
x x2 . xn 2
, s
x 1
n
(x
i 1
i
x)2
n 1
( xi ) 2
1
2
x
i
n 1
n
IQR = Q3 Q1, outliers are observations 1.5*IQR below Q1 or 1.5*IQR above Q3
Probability:
Conditional Probability: P( A | B)
P( A B)
P( B)
Ch 11 Analysis of Variance
Defn: ANalysis Of VAriance (ANOVA) is a procedure to test the equality of three or
more popn means. NOTE: the name of the test refers to comparing different sources of
variability; it WILL test differences among means.
Assumptio
9.3 Analysis of Independent Samples
Defn: Two samples drawn from two populations are independent if the selection of one
sample from one population does not affect the selection of the second sample from the
second population. Otherwise, the samples are d
10.1 Inferences for a Population Proportion
Confidence Interval
Recall the 3 rules from 7.3.1 for the sampling distribution of p .
Thus, the CI for a population proportion p is
p (1 p )
p z / 2
n
Assumptions: random sample, np 15 and n(1 p ) 15
(OR 5 or 1
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.571685
R Square
0.326824
Adjusted R Sq
0.319884
Standard Error
12.79789
Observations
99
ANOVA
df
1
97
98
SS
7713.186
15887.24
23600.42
MS
7713.186
163.7859
F
47.09309
Signif. F
6.4E-10
Coefficients
16.6233
Chapter 14
Random Variables
FromWikipedia,thefreeencyclopedia:
In probabilityandstatistics,a randomvariable orastochastic
variable isa variable whosevalueissubjecttovariationsdueto
chance(i.e. randomness,inamathematicalsense).
For example, suppose we choo
Chapter 3: Displaying and Summarizing
Quantitative Data
We will study quantitative variables using descriptive
statistics in this chapters. In particular, We will talk about:
Histogram,
Stem-and-leaf display,
Dot-plot,
Box-plot,
Numerical measures of cent