Amy G. Froelich
Iowa State University
Acknowledgments
 Interactive Teaching and Learning Tools
 Developed by Amy G. Froelich, Iowa State University
and
d William
Willi
M Duckworth,
M.
D k
th C
Creighton
i ht University
U i
it
 Programmed by Wayne Levin and Brian McFarlane of
Predictum,, Inc.
 Supported by JMP, a business division of SAS Institute,
Inc.
JMP Statistical Discovery Software
 Interactive, comprehensive and highly visual software.
 Dynamically links data to graphics
 Explore data interactively.
Teaching Conceptual Understanding
 Hands‐on Activities
 Java Applets
 Commercial Tools
In the Before Time
 JMP for data analysis; Java Applets for conceptual
understanding.
 Applets
A l are limited
li i d to only
l particular
i l situations.
i
i
 Generally not integrated with similar formats for all
situations.
 Different notation and graphical output.
 Students struggle with the transition between JMP and
Java Applets
Letting Go to Grow!
 Now JMP for both data analysis and
p
understanding.
g
conceptual
Interactive Teaching and Learning Tools
 Tools that assist both instructor and student in
teaching and learning concepts in Intro Stat.
 Can
C be
b used
d as classroom
l
d
demonstration
i tooll or
student‐led discovery activities.
 Tools feature similar format as JMP data analysis
output.
 Tools have same interactive JMP graphics as JMP data
analysis output.
Interactive Teaching and Learning Tools
 Emphasize the connection between statistical
concepts and data analysis
 Integrated
I
d and
d feature
f
similar
i il fformats ffor all
ll tools.
l
 No notation used: Applicable to any textbook.
 Flexible:
Fl ibl T
Tools
l can b
be adapted
d
d to fi
fit many types off
problems.
Current Tools Available
 Sampling Distribution
 Confidence Interval
 Hypothesis Tests (Type I Error only)
 Means
 Proportions
P
i
 Concept of a Distribution
 Distribution
Di t ib ti Calculator
C l l t
Tools Coming Soon
 Hypotheses Tests (with power option)
 Means
 Proportions
 Simple Linear Regression
 Descriptive
D
i ti
 Inferential
 ANOVA
Focus of Breakout Session
 Proportions
 Sampling Distribution

Blue
l Eyes?
 Confidence Interval

Approval Rating for President Obama
 Hypothesis Test

Cracking of Ingots
 Test Driving Time
Sampling Distribution of Sample
Proportions
 Beginning Activity – Student Survey
 Eye Colors (Blue, Brown, Hazel, Green, Other)
 Data Analysis Using JMP
To Be or Not to Be: Blue Eyes
Population and Sample Info
 All ISU undergraduate students (25,000)
 Categorical variable of interest: Eye Color
 Category of interest: Blue Eyes
 Samples of size 200
 Assume proportion of all ISU undergraduate students
with blue eyes is 0.35 (p = 0.35)
 How
H will
ill sample
l proportion
ti vary from
f
sample
l to
t
sample?
I t
ti Tool
T l
Interactive
Investigations
 Sampling Variability
 Distribution of Sample Proportions
 Mean of Sample Proportions
 Standard Deviation of Sample Proportions
 Shape of Sample Proportions
 Change sample size and population proportion to
investigate assumptions.
assumptions
Confidence Interval for Population
Proportion
 In a Gallop Poll taken June 16‐18, 2009, 58% of 1504
national adults (aged 18 or older) surveyed stated that
they approved of the job performance of President
Obama.
 Population: US adults aged 18 or older
 Population parameter: proportion of population who
approve of the job performance of President Obama.
 Sample: 1504 US adults aged 18 or older
 Sample statistic: 0.58 (rounded)
Confidence Interval
Confidence Interval
 We are 95% confident the proportion of US adults
aged 18 or older who approve of the job President
Obama is doing is between 55.47%
55 47% and 60
60.45%.
45%
 What does “95% confident” mean?
I t
ti TTooll
Interactive
Investigations
 Sampling Variability
 Variability of Confidence Interval
 Capture Rate of CI vs. Confidence Level
 Change sample size and population proportion to
i
investigate
i
effect
ff off assumptions
i
on capture rate.
Hypothesis Test for Population
Proportion
 The cracking rate of ingots used in manufacturing
airplanes is 20%. A new process is designed to lower
the proportion of cracked ingots.
ingots In a sample of 400
ingots, 18% of them were cracked. Did the new
process actuallyy lower the p
p
proportion
p
of cracked
ingots?
Hypothesis Test
Hypothesis Test
 We will fail to reject the null hypothesis that p = 0.2,
since the p‐value is 0.1749, which is greater than the
Type I error of 0.05.
0 05
 What does this mean? What is a p‐value and what
does Type I error mean?
Interactive Tool
Investigations
 Sampling Variability
 Variability of Test Statistic
 Meaning of P‐value
 Meaning of Type I Error
 Change sample size and population proportion to
investigate effect of assumptions on percentage of
rejected null hypothesis.
hypothesis
Rest of Breakout Session
 Take the new tools out for a spin.
 All current tools are available on computers.
 Let us know what you think.