TOPIC 1 D
E S C R I2020
BING, EXPLORING,
STAT
AND COMPARING DATA
STATISTICS FOR BIOLOGISTS
'Great Pause' Among Prosecutors As DNA Proves Fallible
Updated January 4, 2016 2:49 AM ET
Over the summer, the Texas Forensic Science Commission, which sets
standar
33,190 31,860 32,590 26,520 33,280
32,320 33,020 32,030 30,460 32,700
23,040 30,930 32,720 33,650 32,340
24,050 30,170 31,300 28,730 31,920
Data Set
1. What is the mean breaking strength?
30841
2. How many of the pieces of wood have strengths less than th
STAT 2020
STATISTICS FOR BIOLOGISTS
T O P I C 3 E X P E R I M E N TA L D E S I G N
OBJECTIVES (PSLS CHAPTER 7)
Samples and observational studies
Observation versus experiment
Population versus sample
The role of randomness in sampling
The simple random sa
STAT 2020
STATISTICS FOR BIOLOGISTS
T O P I C 2 C O R R E L AT I O N A N D S L R
INTRODUCTION TO CORRELATION AND
LINEAR REGRESSION
We will now be looking at the
relationships in sample data that
comes in pairs, with the objective of
determining whether th
Lab 2 More R Basics
Lab 2 Assignment due on Tuesday, September 12, 2017 in the Collab Assignments
Tab by 5pm.
For this lab, you will be going through some of the tutorials on the Data Camp Website. The link
for Data Camp is: https:/www.datacamp.com/.
Go t
Midterm 2 Review
Chapters 7 and 8
STAT 3120
STAT 3120
Midterm 2 Review
1 / 36
Midterm 2 Information
Midterm 2 Information
Date:
March 30, 2017
Time:
11am - 12:15pm
Location:
Material covered:
Resources allowed:
STAT 3120
McLeod 2007
Chapters 7 and 8
8.5i
Discrete Continuous X _ ,I
- - > Standard
Bernoulll Exponential NormaZII w Normal
Bern(p) Exple) N (H. 0 ) a
I I; Convolution I: Convolution N (0, 1)
= a = 1
Binomial t-dIstributIon
Bing p) tv
Law of rare 1 = P
event n > 00
2X c POisson Uniform
Stat Notes
Coefficient of Determination
The percent of the variation that can be explained by the regression equation
Correlation
A method used to determine if a relationship between variables exists
Correlation Coefficient
A statistic or parameter which
Stat notes
Central Limit Theorem
Theorem which stats as the sample size increases, the sampling distribution of
the sample means will become approximately normally distributed.
Correction for Continuity
A correction applied to convert a discrete distribut
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December 2013/January 2014 | Volume 71 | Number 4
Getting Students to Mastery Pages 18-23
In Search of a Useful Definition of Mastery
Thomas R. Gus
_
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Report Information from ProQuest
May 19 2017 00:11
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19 May 2017
ProQuest
Table of contents
1. How do students define their roles and responsibilities in online learning group projects?.
1
Bibliography. 13
19 May 2017
ii
ProQuest
Document 1 of 1
How d
Gateways to Better Education
The
Bible
in State Academic
Standards
A report on all 50 States
A summary of each states academic standards indicating
where educators can and, in some cases, are expected to
teach about the influence of the Bible and Christia
Spring 2015 Varied Learning Environments
7
Using Varied Learning
Environments for Deeper
Learning and Student Mastery
of Complex Content
By Sharon Harsh and Jason Young
T
he authors explore the use of varied learning environments for deeper learning of co
This session looks at the academic expectations that states have regarding teaching
about the Bible, Christianity, and Christians in history.
1
If you havent done so already, then you will need to download the document
entitled The Bible in State Academic
Analyzing Measures of
Central Tendency
When to choose Mean, Median,
Mode and what they indicate
Measures of Central Tendency
These are statistical terms that try to
find the center of the numbers that
are in a group of data.
Mean average
Median middle
STAT 2120: Notes on Topic 5
Conditional probability:
A conditional probability, !"#|%&, gives the
probability of some event, #, under the condition
that some other event, %, has definitely occurred.
The general multiplication rule is !"% and #& =
!"%&!"
STAT 2120: Notes on Topic 10
Analysis of two-way tables:
Some relevant summaries of categorical data in
two categories:
o Count or percent of the number of successes
(as in two-sample setup of inference for
proportions).
o A two-way table, which easily g
STAT 2120: Notes on Topic 9
Introduction to inference for proportions:
The interest is in analyzing data on categorical
variables.
o Data are in the form of counts or percents.
o Relevant parameters are population
proportions.
o Suitable estimates are sa
STAT 2120: Notes on Topic 8
Introduction to inference for distributions:
Emphasis turns from statistical reasoning to
statistical practice.
o Population standard deviation is no longer
assumed known, !.
o Start with inference on " and comparisons of "
be
STAT 2120: Notes on Topic 4
Random variables (continued):
The mean, or expected value, of a random
variable, !, is an idealization of the mean, " , of
data recorded after many repetitions of the
chance-happening associated with !. It is
denoted $% , or s
STAT 2120: Notes on Topic 3
Toward statistical inference:
The context of sampling from a population
provides insight and intuition for statistical
inference.
o Statistical inference is when a conclusion
about a population is inferred from the
characteris
STAT 2120: Notes on Topic 6
Introduction:
Probability calculations help distinguish patterns
seen in data between those that are due to chance
and those that reflect a real feature of the
phenomenon under study.
The two most prominent types of formal
st
Inference for Regression
Inference about the Regression Model and
U i the
Using
h R
Regression
i Li
Line, with
ihD
Details
il
Section 10.1, 2, 3
Basic components of regression setup
Target of inference: linear dependency of a response
variable on one or m
STAT 2120: Notes on Topic 7
Tests of significance (continued):
The four steps for carrying out a significance test
are:
o Formulate the hypotheses.
o Calculate the test statistic and the relevant Pvalue.
o Compare the P-value with a stated significance
l
STAT 2120: Notes on Topic 1
Introduction to Examining Distributions:
A variable records characteristics of cases (i.e.,
objects of interest) in its values.
Classify a variable by its possible values:
o Categorical: records group labels; numeric
labels m