M140: Summarising & Plotting Data Computer Book with Minitab

M140: Summarising & Plotting Data with Minitab

Dr Jason [email protected]

07311 188800

This tutorial will begin at 7:30pm and will last for approximately an hour.

This tutorial will be recorded. Please let me know if you have any questions or concerns about this.

Things you might need for this tutorial:• M140 Computer Book & Handbook• Pen, paper & calculator• Drink of your choice

Don’t forget to set up your audio using the Audio Wizard (in the ‘Meeting Menu’). Some headsets have independent volume controls so you may need to adjust these too.

You will also need to set up your mic if you plan on using it. Clicking the Mic symbol at the top of you Adobe Connect Window will toggle it on/off.

Not connected Connected and live Connected and muted

TMA01 due by next Wednesday!

Good evening!

2

Mics will be muted until towards the end of the tutorial, when I will also stoprecording.Do use the Chat Box if you have a question during the tutorial!I will email slides out after the tutorial.

Tutorials are enhanced by your interactionPlease vote in the polls, ask questions and work through the exercises

Feel free to ask any questions or provide feedback by emailafterwards, or use the Private Chat function if you prefer during thetutorial

Summarising & Plotting Data with Minitab

Scientific Skills • Sample or population?• Types of data• Locators & descriptors• Good graphs• Interpreting graphs

Minitab Skills• Calculating statistics• Plotting graphs• Customising graphs

3Computer Book has detailed instructions!

Summarising & Plotting Data with Minitab

Scientific Skills • Sample or population?• Types of data• Locators & descriptors• Good graphs• Interpreting graphs

Minitab Skills• Calculating statistics• Plotting graphs• Customising graphs

4Computer Book has detailed instructions!

Have you stated using Minitab

yet?

Sample or Population?

5http

s://

ww

w.p

inte

rest

.co.

uk/p

in/2

2539

1156

3259

5899

9/

Population


6

• We are unlikely to ever be able to study a whole population• Example: A fishing boat will not be able to catch all the fish in the sea• Whole population may be available in clinical trials or other highly controlled environments

http

s://

ww

w.p

inte

rest

.co.

uk/p

in/2

2539

1156

3259

5899

9/

Population


7

• Samples are taken instead from a population and we infer the properties of the population from these samples

• However sampling methods can introduce bias, or skew results if not planned for

http

s://

ww

w.p

inte

rest

.co.

uk/p

in/2

2539

1156

3259

5899

9/

Population Samples


8

• Samples are taken instead from a population and we infer the properties of the population from these samples

• However sampling methods can introduce bias, or skew results if not planned for

http

s://

ww

w.p

inte

rest

.co.

uk/p

in/2

2539

1156

3259

5899

9/

Samples

• Assume you are working with a sample from a population unless told otherwise

• Your statistics will thus be an estimateof the true population statistic• Don’t worry about this difference now!• M248 & M249 cover samples, estimators

and bias in more detail

Discrete• Counts, quantities• Data are usually

integer• Binomial, Poisson

Types of Data 1

9

Raw data can be analysed without modification; transformation may be

necessary for some tests



Continuous• Sizes, heights and

measures of physical properties

• Data are usually decimal

• Normal, Exponential

Types of Data 4

10









Ordinal• Data with an intrinsic

order• Survey responses,

degree classifications, sports medals

Types of Data 4

11









Ordinal• Data with an intrinsic

order• Survey responses,

degree classifications, sports medals

Nominal• Data are unordered

names or descriptors• Types of fruit, disease

symptoms

Types of Data 4

12

Raw data likely to need coding for analysis



Locators & Descriptors

13

Locators

A typical value to describe a dataset

Usually a measure of central tendency

For example:• Arithmetic mean (average)• Weighted mean• Median• Mode

Locators & Descriptors

14

Locators

A typical value to describe a dataset

Usually a measure of central tendency

For example:• Arithmetic mean (average)• Weighted mean• Median• Mode

Descriptors

A value describing the shape of a dataset

Usually a measure of spread

For example:• Range• Standard deviation • Variance• Interquartile Range

Knowledge Check!

15

7 8 8 9 10 11 12 25 15 15 16 18 19 20 13

Mean (average, �̅�𝑥) =

Median =

Mode =

Range =

�̅�𝑥 =∑𝑥𝑥𝑛𝑛

Mean

Fill in the blanks!(1 dp)

Knowledge Check!

16

7 8 8 9 10 11 12 25 15 15 16 18 19 20 13

Mean (average, �̅�𝑥) =

Median =

Mode =

Range =

�̅�𝑥 =∑𝑥𝑥𝑛𝑛

Mean13.7

13

8, 15

18

Minitab 1 - Interface

17


18

Menus & shortcuts


19

Output pane


20

Output pane navigator


21

Data sheet pane


22

Data sheet navigator

Minitab 2 – Descriptive Statistics

23

Get descriptive statistics and a graphical summary


24

Get descriptive statistics and a graphical summary Load catweights.mwx


25

Get descriptive statistics and a graphical summary Load catweights.mwx Select Descriptive Statistics

26

Data sheet column number

Column title

27

Double click the variable of interest

Or use the Select button

28

Variable of interest

Select Statistics…

29

Select whichever statistics you would like to include in the

output report

Click OK

30

Ta da!

31

Output pane navigator

Charts Components Refresher

• Charts, graphs, plots – are all essential to explain any data or analysis• However, poor charts can unintentionally (or intentionally)

misrepresent data leading to inaccurate or erroneous interpretations by the viewer

• Example shortly…

• What are the key components for every chart and graph?

32

Essential Components

Figure 1. Simple exponential plot (𝑦𝑦 = 𝑥𝑥2).

Data

33



DataTitle

34



DataTitleAxes

35



DataTitleAxesScale

36



DataTitleAxesScale

Labels

37



DataTitleAxesScale

LabelsLegend

38

Is this a good chart?

39

1. What’s missing?2. What’s the

message?

Is this a good chart?

40

No titleNo axis labels

Graph purports to show how many shops are reporting a change in their opening hours and what those additional hours are.

Minitab 3 – Graphical Data Summary

41

Get descriptive statistics and a graphical summary Load catweights.mwx


42

Get descriptive statistics and a graphical summary Load catweights.mwx Select Graphical Summary


43

Get descriptive statistics and a graphical summary Load catweights.mwx Select Graphical Summary

Select the variable of interest and click OK

45

Histogramwith a fitted distribution

line

46

Box plot

47

Confidence intervals(using t

distribution)

48

Hypothesis test for Normal

distribution

49

Data set descriptive

statistics

Minitab 4 – Simple Graphs

50

Build and customise a simple histogram


51


Load catweights.mwx


52


Load catweights.mwx

Select Graph -> Histogram

60

Annotationsincluding text,

highlighter shapes, arrows

61

Additions & Edits

including grid lines, labels,

footnotes

62

Double-click to edit

63

Axis ticksDouble-click to edit

64

Axis ticks

65

Axis scale

66

Swap horizontal (X)

and vertical (Y) axes

67

Right-click on the histogram bars

68

Right-click on the histogram bars

Select Edit Bars…

69

Change colours & lines here

70

Change variable groups & origin here

71

Change data bin sizes & midpoints

here

Minitab 5 – Box Plot Interpretation

74

Create a boxplot using the same cat weight data, with

default settingsplus

“Transpose value and category scales”

75

Fill in the blanks!

Whiskers indicate data rage

Median (1 dp) =

Skew (direction) =

Range =

IQR (1 dp) =

2.6

Right/Neg

2.2

0.9

76


Median (1 dp) =

Skew (direction) =

Range =

IQR (1 dp) =

2.6

Right/Neg

2.2

0.9

± 0.2 is still good in this example!

77

Right > left


Median (1 dp) =

Skew (direction) =

Range =

IQR (1 dp) =

2.6

Right/Pos

2.2

0.9


78


Median (1 dp) =

Skew (direction) =

Range =

IQR (1 dp) =

2.6

Right/Neg

2.2

0.9


79

Median (1 dp) =

Skew (direction) =

Range =

IQR (1 dp) =

2.6

Right/Neg

2.2

0.9


Whiskers indicate data range

Scatterplot – petrol.mwx

80

Variable relationship =

Positive

Simple interpretation =

More miles are driven as petrol price rises

Fill in the blanks!


81


Positive


Petrol consumption increases with the amount of driving

miles


82


Positive


Petrol consumption increases with the amount of driving

miles

Thank you! Any questions?

• M140 materials online• Course Books & Screencasts• (https://learn2.open.ac.uk/course/view.php?id=208584&area=resources )

• M140 student forums• Wikipedia• CrossValidated (https://stats.stackexchange.com/ )• Minitab channel on YouTube:

• https://www.youtube.com/user/MinitabInc• Minitab help

• https://support.minitab.com/en-us/minitab/19/• Contact me:

• [email protected]• 07311 188 800

83Recording should be available in the M140 20J Online Tutorial Room• https://learn2.open.ac.uk/mod/connecthosted/viewrecordings.php?id=1644077&group=274133

https://learn2.open.ac.uk/course/view.php?id=208584&area=resources

https://stats.stackexchange.com/

https://www.youtube.com/user/MinitabInc

https://support.minitab.com/en-us/minitab/19/

mailto:[email protected]

https://learn2.open.ac.uk/mod/connecthosted/viewrecordings.php?id=1644077&group=274133

M140: Summarising & Plotting Data Computer Book with Minitab

Documents

Transcript of M140: Summarising & Plotting Data Computer Book with Minitab