LAB 1: INTRODUCTION TO THE R STATISTICAL SOFTWARE PACKAGE
In this course, we will be using the statistical software package R. R is an open-source project that provides a programming environment for carrying out a wide range of statistical analysis. The b
0me 2.,
W,
k
ii? if
f '
w" 4%
" H mm w 9i;
in Law L3
2»; . .M W» 4~ \
, r. ~s, ; ,7 m . w
{Mix a J y {a if}: it»?er
'm
M
. , . mun
"Hm mwm "
I f .
w.w.um.u
m M
,
V
3
,.
:7: "m
C.
,e
4" m MIN;
£3;
I
' ng a, E} _
9 £,:;,.»;.,r;
CW)
Wax-WW
Practice Problems 17
Variables are either categorical or numerical. A categorical variable describes
which category an individual belongs to, while a numerical variable is
expressed as a number.
In studies of association between variables, one
17.
are thought to be a bellwether of coming
chances for other speCies. . -
\vhut is the proportion of aInphlblan speaes
that are at risk of extinction?
Would it make sense to calculate a con-
dence interval for this proportion in the usual
way 1 Why or w
Chapter 5 Probability
18.
19.
17.
A collection of 1600 pea plants from one of
Mendels experiments had 900 that were tall
plants with green pods, 300 that were tall with
yellow pods, 300 that were short with green
pods, and 100 that were short with
Lecture 9:
Discrete Data, 2 Test
Agenda
1. During last lecture we introduced the Binomial test for the
proportion of successes when there are two possible
outcomes.
2. Today we will introduce a goodness of fit test also called
the Chi-Squared test for the
Lecture 11:
Chi-Squared Contingency Test
Agenda for Today
1. We have learned about contingency tables, odds and
odds ratios. Also, in lab you learned about the Poisson
distribution and the chi-squared goodness of fit test for
the Poisson distribution.
2.
Lecture 5:
Conditional Probabilities, Law of Total
Probability and Bayes Theorem
Agenda
1. During the last two lectures we gave an introduction into
probability.
Mutually exclusive events
Venn diagrams, addition rule
Independent events
probability trees
Lecture 10:
Odds Ratio
Agenda for Today
1. Last time we talked about the goodness of fit test.
2. Homework 3 is due tomorrow!
3. Tomorrow in lab you will learn about the Poisson
distribution.
4. Today we will introduce the concept of an odds ratio.
5. For
Lecture 1:
Intro
Collecting and Displaying Data
Agenda
1. Go over syllabus.
2. Statistical inference
3. Understanding variables
4. Collecting and displaying data
General Course Overview
Statistics: The art and science of collecting, analyzing, and
using d
Lecture 3:
Probability
Agenda
1. Last week we gave a brief introduction into statistics and
learned how to describe data using graphical methods
and numbers such as the mean, median and standard
deviation.
2. Reminder: Homework 1 is due on Thursday in lab
Lecture 4:
Probability Continued
Agenda
1. Last lecture we gave an introduction into probability
including some set operations and drawing Venn
Diagrams.
2. Today we will continue talking about probability.
3. For more help on the topics in these lectures
Lecture 5:
Conditional Probabilities, Law of Total
Probability and Bayes Theorem
Agenda
1. During the last two lectures we gave an introduction into
probability.
Mutually exclusive events
Independence
Venn diagrams
Probability trees
2. Reminder: Qui
STAT 2480 Lecture: Statistics for the Life Sciences
Instructor: Dr. Xinyi Xu / Dr. Yunzhang Zhu
Office: Cockins Hall 440G / Cockins Hall 425
Office Hours: F noon-1:30pm / TBD
E-mail: [email protected] / [email protected]
Lecture Location: Hagerty Hall 180
Lect
Exam 1 Review
Lecture 1:
(a)
(b)
(c)
Population vs. sample; parameter vs. statistic
Variables: categorical (nominal vs. ordinal) vs. quantitative (discrete vs. continuous)
Displaying data: bar graphs, histograms, scatterplots, boxplots
Lecture 2:
(a)
(b)
Lecture 8
Hypothesis Testing for Binomial Distribution
Agenda
1. During last lecture we introduced Bernoulli distribution and
Binomial distributions.
2. Reminder: Quiz 2 will be held in tomorrows lab!
3. Today we will introduce the concepts of confidence
Ch. 5 Probability
Base Definitions:
Experiment an activity whose outcome cannot be predicted with certainty
Sample space the list or set of all possible outcomes; S
Event a collection of outcomes (subset of S)
Let A be any event associated with sample spa
Ch. 3 Describing Data
Notation:
a specific observation of the variable y; y -a fixed value; Y - a variable
sample size
, , , subscript denotes which observation; not in order of size, just order collected; data set
or n observations (i.e. y1 is 1st obs
STAT 2480 HW 1
Due Monday, January 30th
Use JMP or another statistical software package to make all of the graphical displays and to
compute the summary statistics.
Your work must be neat, organized, and stapled. You may discuss the problems with others b
Due Monday, March 27th
STAT 2480 HW
Your work must be neat, organized, and stapled. Use probability statements, define events, and
define random variables and give their distribution, when applicable.
You may discuss the problems with others but you must
STAT 2480 HW 2
Due Monday, February 20th
Your work must be neat, organized, and stapled. Use probability statements, define events, and
define random variables and give their distribution, when applicable.
You may discuss the problems with others but you
STAT 2480: Statistics for the Life Sciences
Hitchcock Hall 131
WF 12:40 1:35
The Ohio State University, Spring 2017
Instructor: Kevin Donges
212B Cockins Hall
614.292.2866
[email protected]
Office Hours: M 1:00 3:00, WF 2:00 3:00, or by appointment
Course
Ch. 2 Displaying Data
Types of Displays for Quantitative Data:
1. Line Graph
Shows how the values of the variable change over time.
Time
range of values of the variable
_
goes on the horizontal axis and the _
on the
vertical axis.
not about making graphs,
Ch. 3 Describing Data
Notation:
1 , 2 , ,
=1 =
Summarizing Quantitative Data:
Measures of Center:
The _ is the numerical average of all the observations and is denoted
by _.
The _ is the middle number when the observations are ordered
from smallest to
Ch. 1 Statistics and Samples
What is statistics?
Language of Statistics
1. Population The entire group of individuals, objects or units about which we want information.
2. Sample The part of the population from which we actually collect information, used