how to give a memorable presentation examples and tips for an effective presentation dr jenny...
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How to give a memorable presentation Examples and tips for an effective presentation
Dr Jenny Freeman, University of Sheffield
Used under a Creative Commons By Attribution Licence -Some rights reserved by eyeonjapan.com
Starting out• Knowledge
• Material
Outline
• Before the day
• On the day
Outline
•Before the day• On the day
Do your homework
• Audience
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
sin(ωt) and cos(ωt) allow for seasonality
ωt=(2πt)/12
b3 is the slope
b4 allows for change in slope at t0 where t0 is the time of the intervention
t’ = 0 when t< t0
t’ = t- t0 when t≥ t0
δ(t) allows for break in curve (interruption)
δ(t)) = 0 when t< t0
δ(t)) = 1 when t≥ t0
ξt is the error term which we allow to have first order autocorrelation structure with parameter ρ
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
Do your homework
• Audience
• Venue
Do your homework
• Audience
• Venue
• Timing
Before the day
• Message
• Slide design
Before the day
• Message
• Slide design
• Practise
Before the day
• Message
• Slide design
• Practise
• Practise
Before the day
• Message
• Slide design
• Practise
• Practise
•Practise again
Practise• NEVER, ever don’t practise
• Do it at least twice (if possible)
Used under a Creative Commons By Attribution Licence -Some rights reserved by Nick J Webb
Voice trainingUsed under a Creative Commons By Attribution Licence -Some rights reserved by MiiiSH
Slides
Graphic design of slides
• Key elements−Text
−Pictures and graphics
−Colour
−Space
Graphic design of slides: text
Slide design
• Keep it simple
• Keep it legible
• Generally, light text on a darker background projects well
• Sans serif fonts such as Arial are more easily read when projected.
Slide design
• Keep it simple
• Text is meant to be read, so ensure that slides are legible
• No hard and fast rules, but in general, light text on a darker background projects well
• Sans serif fonts such as Arial are more easily read when projected.
What do you think?
Graphic design of slides: pictures, graphics & animations
For example….
And what do the students think?
• small group sessions
• lectures and lecture notes
• the videos
• first two lectures
• clinical examples
• logical and clear to understand
What did the students find most useful:
What did the students find least useful:
• the group sessions
• the lectures as they are too hard
• the videos
• the early sessions
• clinical scenarios
• too much stats
And what do the students think?
• small group sessions
• lectures and lecture notes
• the videos
• first two lectures
• clinical examples
• logical and clear to understand
What did the students find most useful:
What did the students find least useful:
• the group sessions
• the lectures as they are too hard
• the videos
• the early sessions
• clinical scenarios
• too much stats
Graphic design of slides: colour
Graphic design of slides: space
For example….
20/04/23 © The University of Sheffield
What do we mean when we talk about bivariate data• Data where there are two variables
• The two variables can be either categorical or numerical
• This session we are dealing with continuous bivariate data i.e. Both variables are continuous
• We have also looked at categorical bivariate data.....
..... Categorical bivariate data from the risk lecture:
• There are two binary (categorical) variables Type of statin (Baycol/other)
Whether died of rhabdomylysis
• With these data we examined the risk of death from rhabdomyolysis of Baycol compared to other statins
Baycol Other statins
Number who die from rhabdomyolysis
2 1
Number who are alive or died from other causes
999,998 9,999,999
Total 1,000,000 10,000,000
What do you think now…?
What do we mean when we talk about bivariate data
• Data where there are two variables
• The two variables can be either categorical or numerical
• This session we are dealing with continuous bivariate data i.e. Both variables are continuous
• We have also looked at categorical bivariate data...
...categorical bivariate data example from Risk lecture
• There are two binary (categorical) variables Type of statin (Baycol/other)
Whether died of rhabdomylysis
• With these data we examined the risk of death from rhabdomyolysis of Baycol compared to other statins
Baycol Other statins
Number who die from rhabdomyolysis
2 1
Number who are alive or died from other causes
999,998 9,999,999
Total 1,000,000 10,000,000
Highlighting key points
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
sin(ωt) and cos(ωt) allow for seasonality
ωt=(2πt)/12
b3 is the slope
b4 allows for change in slope at t0 where t0 is the time of the intervention
t’ = 0 when t< t0
t’ = t- t0 when t≥ t0
δ(t) allows for break in curve (interruption)
δ(t)) = 0 when t< t0
δ(t)) = 1 when t≥ t0
ξt is the error term which we allow to have first order autocorrelation structure with parameter ρ
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
sin(ωt) and cos(ωt) allow for seasonality
ωt=(2πt)/12
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
sin(ωt) and cos(ωt) allow for seasonality
ωt=(2πt)/12
b3 is the slope
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
sin(ωt) and cos(ωt) allow for seasonality
ωt=(2πt)/12
b3 is the slope
b4 allows for change in slope at t0 where t0 is the time of the intervention
t’ = 0 when t< t0
t’ = t- t0 when t≥ t0
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
sin(ωt) and cos(ωt) allow for seasonality
ωt=(2πt)/12
b3 is the slope
b4 allows for change in slope at t0 where t0 is the time of the intervention
t’ = 0 when t< t0
t’ = t- t0 when t≥ t0
δ(t) allows for break in curve (interruption)
δ(t)) = 0 when t< t0
δ(t)) = 1 when t≥ t0
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
The model:
Where:
b0, b1, b2, b3, b4, b5 are constants
sin(ωt) and cos(ωt) allow for seasonality
ωt=(2πt)/12
b3 is the slope
b4 allows for change in slope at t0 where t0 is the time of the intervention
t’ = 0 when t< t0
t’ = t- t0 when t≥ t0
δ(t) allows for break in curve (interruption)
δ(t)) = 0 when t< t0
δ(t)) = 1 when t≥ t0
ξt is the error term which we allow to have first order autocorrelation structure with parameter ρ
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
Potential models: No impact at all
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
Potential models: Change in slope
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
Potential models: Break in curve
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
Potential models: Both change in slope and break
yt= b0+b1sin(ωt)+b2cos(ωt)+b3t+b4t’+b5δ(t)+ξt
Tables in presentations
• What do you think?
Baseline characteristics(n=584,321)
Missing data(%)
Before Minor Injuries
(Dec 2002)
Before 90% target
(March 2004)
Before 98% target
(January 2005)
After 98% target
N 250,489(7,828/month)
122,938(8,196/
month)
87,647(8,765/month)
123,247(8,803/
month)
Gender: % Male (0.0%) 54.3 54.0 53.9 53.9
Age: median (IQR)
(0.1%) 40.0(27.2 to 61.5)
40.9(27.1 to 62.8)
40.8(26.8 to 61.4)
40.4(26.3 to
60.6)
% > 65 years (0.1%) 22.0 23.2 22.1 21.2
% with trauma (1.6%) 52.3 50.7 50.4 49.1
% by ambulance - 33.4 33.8 31.3 31.8
% seen & treated within 4 hours
(0.3%) 82.9 77.8 82.8 88.2
Tables in presentations
• Can we do better?
Baseline characteristics(n=584,321)
Missing data(%)
Before Minor Injuries
(Dec 2002)
Before 90% target
(March 2004)
Before 98% target
(January 2005)
After 98% target
N
Gender: % Male (0.0%)
250,489(7,828/month)
54.3
122,938(8,196/
month)
54.0
87,647(8,765/month)
53.9
123,247(8,803/
month)
53.9
Baseline characteristics(n=584,321)
Missing data(%)
Before Minor Injuries
(Dec 2002)
Before 90% target
(March 2004)
Before 98% target
(January 2005)
After 98% target
N
Gender: % Male
Age: median (IQR)
(0.0%)
(0.1%)
250,489(7,828/month)
54.3
40.0(27.2 to
61.5)
122,938(8,196/
month)
54.0
40.9(27.1 to
62.8)
87,647(8,765/month)
53.9
40.8(26.8 to
61.4)
123,247(8,803/
month)
53.9
40.4(26.3 to
60.6)
Baseline characteristics(n=584,321)
Missing data(%)
Before Minor Injuries
(Dec 2002)
Before 90% target
(March 2004)
Before 98% target
(January 2005)
After 98% target
N
Gender: % Male
Age: median (IQR)
% > 65 years
(0.0%)
(0.1%)
(0.1%)
250,489(7,828/month)
54.3
40.0(27.2 to 61.5)
22.0
122,938(8,196/
month)
54.0
40.9(27.1 to 62.8)
23.2
87,647(8,765/month)
53.9
40.8(26.8 to 61.4)
22.1
123,247(8,803/
month)
53.9
40.4(26.3 to
60.6)
21.2
Baseline characteristics(n=584,321)
Missing data(%)
Before Minor Injuries
(Dec 2002)
Before 90% target
(March 2004)
Before 98% target
(January 2005)
After 98% target
N
Gender: % Male
Age: median (IQR)
% > 65 years
% with trauma
(0.0%)
(0.1%)
(0.1%)
(1.6%)
250,489(7,828/month)
54.3
40.0(27.2 to 61.5)
22.0
52.3
122,938(8,196/
month)
54.0
40.9(27.1 to 62.8)
23.2
50.7
87,647(8,765/month)
53.9
40.8(26.8 to 61.4)
22.1
50.4
123,247(8,803/
month)
53.9
40.4(26.3 to
60.6)
21.2
49.1
Baseline characteristics(n=584,321)
Missing data(%)
Before Minor Injuries
(Dec 2002)
Before 90% target
(March 2004)
Before 98% target
(January 2005)
After 98% target
N
Gender: % Male
Age: median (IQR)
% > 65 years
% with trauma
% by ambulance
(0.0%)
(0.1%)
(0.1%)
(1.6%)
-
250,489(7,828/month)
54.3
40.0(27.2 to 61.5)
22.0
52.3
33.4
122,938(8,196/
month)
54.0
40.9(27.1 to 62.8)
23.2
50.7
33.8
87,647(8,765/month)
53.9
40.8(26.8 to 61.4)
22.1
50.4
31.3
123,247(8,803/
month)
53.9
40.4(26.3 to
60.6)
21.2
49.1
31.8
Baseline characteristics(n=584,321)
Missing data(%)
Before Minor Injuries
(Dec 2002)
Before 90% target
(March 2004)
Before 98% target
(January 2005)
After 98% target
N
Gender: % Male
Age: median (IQR)
% > 65 years
% with trauma
% by ambulance
% seen & treated within 4 hours
(0.0%)
(0.1%)
(0.1%)
(1.6%)
-
(0.3%)
250,489(7,828/month)
54.3
40.0(27.2 to 61.5)
22.0
52.3
33.4
82.9
122,938(8,196/
month)
54.0
40.9(27.1 to 62.8)
23.2
50.7
33.8
77.8
87,647(8,765/month)
53.9
40.8(26.8 to 61.4)
22.1
50.4
31.3
82.8
123,247(8,803/
month)
53.9
40.4(26.3 to
60.6)
21.2
49.1
31.8
88.2
Life after death by Powerpoint• http://www.youtube.com/watch?
v=lpvgfmEU2Ck
Powerpoint summary
Powerpoint summary
• Keep slides simple
• Keep slides consistent
• Make sure they are legible
• Graphs easier to read than tables
• In general six words per row, six lines per slide
• Use graphics and animations sparingly
On the day
On the day
• Breathe
On the day
• Breathe
• Friend
Used under a Creative Commons By Attribution Licence -Some rights reserved by graciehagen
First impressions
• Dress
• Eye contact
Props/crutches
20/04/23 © The University of Sheffield
20/04/23 © The University of Sheffield
How to stop
• Summary
• Main message
• Your contact detailsUsed under a Creative Commons By Attribution Licence -Some rights reserved by bpende
Dealing with questions
• Always repeat the question
Dealing with questions
• What do you do if you don’t know the answer?
Learning from others
• Poor speakers
• Good speakers
What will you do differently next time?
What will you do differently next time?• Do your research
• Prepare well in advance
• Practise again, and again, and again
• Plant a friend in the audience
• Breathe (& smile)
Used under a Creative Commons By Attribution Licence -Some rights reserved by Micky.!