SlideShare a Scribd company logo
Dual vertical axes chart scaling algorithm
comparison with Excel’s
May 2016
Confidential
Questions/Comments?
Please contact Jennifer Lin
at graphician1122@gmail.com
1
®
100
36
100
84
36
52
68
84
100
36
52
68
84
100
Q1 Q2
A B
100
36
100
84
75
80
85
90
95
100
105
0
20
40
60
80
100
120
Q1 Q2
A B
Executive Summary
2
 Graphician is pleased to present one of our 5 US-patented algorithms.
 An algorithm to truthfully present the intelligence of data graphically on dual vertical axes chart.
 An algorithm can be easily incorporated into conventional tableted data applications such as Excel.
-16-64
Excel algorithm mispresents a decrease from 100 to 36 and
a decrease from 100 to 84 is the same.
Graphician algorithmExcel dual vertical axes chart
-16
-64
Graphician algorithm shows a decrease from 100 to 36 is
more than a decrease from 100 to 84.
®
-91
100
94
90
92
94
96
98
100
102
-150
-100
-50
0
50
100
150
Q1 Q2
A B
What algorithm Excel adopts for dual axes chart now?
3
Excel single vertical axis chart Excel dual vertical axes chart
-6-191
Excel adopts the same algorithm for single vertical axis chart when setting the scales of dual vertical axes chart,
thus the elongations of both axes are not coordinated to be the same.
100100
-91
-150
-100
-50
0
50
100
150
Q1 Q2
A
®
-91
100
94
-91
-43
5
52
100
-91
-43
5
52
100
Q1 Q2
A B
-91
100
94
90
92
94
96
98
100
102
-150
-100
-50
0
50
100
150
Q1 Q2
A B
4
Excel algorithm vs. Graphician algorithm
Excel algorithm mispresents a decrease from 100 to -91
and a decrease from 100 to 94 is about the same.
Graphician algorithm shows a decrease from 100 to -91 is
much more than a decrease from 100 to 94.
Excel dual vertical axes chart
-6
-191
Graphician algorithm
-6
-191
100
100
®
No negative base value
Commonly used “Base Value” method misleads too
5
Graphician algorithm is the only solution which can correctly present the interaction/relationship between the data sets in all
kinds of situations on chart.
Base Value
Period A B
Q1 100 20
Q2 20 100
Change -80 +80
100%
20%
100%
500%
0%
100%
200%
300%
400%
500%
600%
Q1 Q2
A B
Line A’s decrease should be equal to
Line B’s increase
100
2020
100
20
40
60
80
100
20
40
60
80
100
Q1 Q2
A B
With negative base value Base Value
100%
-80%
100%
-300%
-400%
-300%
-200%
-100%
0%
100%
200%
Q1 Q2
A B
Line A’s decrease should be more
than Line B’s decrease
100
-80
-20
-100 -100
-80
-60
-40
-20
0
20
40
60
80
-80
-60
-40
-20
0
20
40
60
80
100
Q1 Q2
A B
Period A B
Q1 100 -20
Q2 -80 -100
Change -180 -80
Graphician
Graphician
®
6
Misled by chart (case 1):
What drove the increase of sales?
Period Selling Price Units Sold Sales
Q1 84 9,762 820,000
Q2 100 10,000 1,000,000
Excel
Excel algorithm presents as the selling price and units sold both increased, but there was no much increase on sales.
820,000
1,000,000
84
100
75
80
85
90
95
100
105
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Q1 Q2
Sales Selling Price
Excel
820,000
1,000,000
9,762
10,000
9,600
9,650
9,700
9,750
9,800
9,850
9,900
9,950
10,000
10,050
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Q1 Q2
Sales Units Sold
®
7
Misled by chart (case 1):
What drove the increase of sales? (cont.)
Period Selling Price Units Sold Sales
Q1 84 9,762 820,000
Q2 100 10,000 1,000,000
Graphician
820,000
1,000,000
9,762
10,000
8,200
8,560
8,920
9,280
9,640
10,000
820,000
856,000
892,000
928,000
964,000
1,000,000
Q1 Q2
Sales Units Sold
Graphician
820,000
1,000,000
84
100
82.0
85.6
89.2
92.8
96.4
100.0
820,000
856,000
892,000
928,000
964,000
1,000,000
Q1 Q2
Sales Selling Price
Graphician algorithm presents the fact that the main driver of increased sales is the increased selling price.
®
820,000
1,000,000
84
100
82.0
85.6
89.2
92.8
96.4
100.0
820,000
856,000
892,000
928,000
964,000
1,000,000
Q1 Q2
Sales Selling Price
8,200
8,560
8,920
9,280
9,640
10,000
9,762
10,000
8
Misled by chart (case 1):
What drove the increase of sales? (cont.)
Period Selling Price Units Sold Sales
Q1 84 9,762 820,000
Q2 100 10,000 1,000,000
Graphician
Graphician algorithm presents the fact that the main driver of increased sales is the increased selling price.
®
Misled by chart (case 2):
What drove the growth of number of employed labor?
9
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
110,000
115,000
120,000
125,000
130,000
135,000
140,000
145,000
150,000
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Employed in all industries
Employed in non-agricultural industries
Excel Graphician auto scaling algorithm
119,651
124,511
129,371
134,232
139,092
143,952
121,392
126,323
131,254
136,185
141,116
146,047
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Employed in all industries
Employed in non-agricultural industries
Graphician
18.7%
20.3%
18.7% 20.3%
 Graphician shows the fact that increase of employed in all
industries was mainly contributed by increase of
employed in non-agricultural industries in modern society.
 Excel shows there was few relationship between the 2
data sets.
Source: U.S. Bureau of Labor Statistics
®
Misled by chart (case 3):
Which stock performed better?
10
Source: Stock price data base
30.53
31.76
32.98
34.21
35.43
36.66
24.15
25.12
26.09
27.06
28.03
29.00
3/23/2004
4/23/2004
5/23/2004
6/23/2004
7/23/2004
8/23/2004
9/23/2004
10/23/2004
Microsoft Dell
31.00
32.00
33.00
34.00
35.00
36.00
37.00
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
3/23/2004
4/23/2004
5/23/2004
6/23/2004
7/23/2004
8/23/2004
9/23/2004
10/23/2004
Microsoft Dell
Excel Graphician auto scaling algorithmGraphician
20.1%
11.1%
20.1%
11.1%
 Graphician shows the movement of the 2 stocks in same
elongation and the fact that Microsoft’s share price
performed better than Dell’s.
 Microsoft’s share price grew 20.1% while Dell’s grew only
11.1%. However, the chart indicated that Dell’s price
movement was much larger than Microsoft’s.
®
100
36
1000
840
360
520
680
840
1000
36
52
68
84
100
Q1 Q2
A B
100
36
1000
840
750
800
850
900
950
1000
1050
0
20
40
60
80
100
120
Q1 Q2
A B
Key steps of the algorithm
11
A B
Q1 100 1000
Q2 36 840
Original E-value
0.64
= (100-36)/100
0.16
= (1000-840)/1000
Upper limit
of the axis
100
= A’s Max
1000
= B’s Max
Lower limit
of the axis
36
= A’s Min
360
= B’s Max ×
A’s Min/A’s Max
New E-value N/A
0.64
= (1000-360)/1000
Note: (1) Which of the upper and lower limit should be unchanged and how to calculate the other limit is disclosed in the flowchart next page.
Calculate the E-value of each sequence (A: 0.64; B:0.16)
Set upper and lower limits of the axis with larger E-value (A: 0.64)
as its Max & Min (100 & 36)
Set one of the upper and lower limit of the axis with smaller E-
value (B: 0.16) unchanged (1000) (1)
Calculate the other limit of the axis with smaller E-value (360).
B’ new E-value (0.64) equals to A’s original E-value (0.64) (1)
4 key steps
1
2
3
4
1
2 3
4
1
2
4
2
2
3
4
-16%
-64%-16%-64%
Excel algorithm Graphician algorithm
®
Step 3 & 4 of the algorithm:
Which of the upper and lower limit should be changed and how?
│Max value of 1st data set │
≥
│Min value of 1st data set │
│Max value of 2nd data set │
≥
│Min value of 2nd data set │
Adjust lower limit
of 2nd data set’s axis
= 2nd data set max value
× 1st data set mini value
÷ 1st data set max value
│Max value of 2nd data set │
≥
│Min value of 2nd data set │
Yes
Yes Yes
No
No No
Adjust upper limit
of 2nd data set’s axis
= 2nd data set mini value
× 1st data set mini value
÷ 1st data set max value
Adjust lower limit
of 2nd data set’s axis
= 2nd data set max value
× 1st data set max value
÷ 1st data set mini value
Adjust upper limit
of 2nd data set’s axis
= 2nd data set mini value
× 1st data set max value
÷ 1st data set mini value
i. 1st data set refers to the data set
with larger E-Value
ii. 2nd data set refers to the data set
with smaller E-Value
Upper limit of 2nd data
set’s axis unchanged
= 2nd data set max value
Upper limit of 2nd data
set’s axis unchanged
= 2nd data set max value
Lower limit of 2nd data
set’s axis unchanged
= 2nd data set mini value
Lower limit of 2nd data
set’s axis unchanged
= 2nd data set mini value
12
®
Step 3 & 4 of the algorithm (cont.):
Yes
Yes 100
36
1000
840
360
520
680
840
1000
36
52
68
84
100
Q1 Q2
A B
A B
Q1 100 1000
Q2 36 840
Original E-value
0.64
= (100-36)/100
0.16
= (1000-840)/1000
Upper limit
of the axis
100
= A’s Max
1000
= B’s Max
Lower limit
of the axis
36
= A’s Min
360
= B’s Max ×
A’s Min/A’s Max
New E-value N/A
0.64
= (1000-360)/1000
1
2 3
4
1
2
4
2
2
3
4
-16%
-64%
Graphician algorithm
│100│≥│36│
│1000│≥│840│
Adjust lower limit
of 2nd data set’s axis
= 1000 × 36 ÷ 100
=360
Upper limit of 2nd data
set’s axis unchanged
= 1000
i. Here 1st data set is data set A
ii. Here 2nd data set is data set B
13
®
Appendix: Demonstration of all kinds of situations
14
®
Demonstration of all kinds of situations
15
We define:
a1 = Max value of sequence (A);
an = Min value of sequence (A)
 To prove that the patented algorithm can present the true interaction of data with the same elongation ratio under all kinds of
situations, we will demonstrate one example for each situation.
 Though the algorithm can be applied to charts with multiple vertical axes, to simplify the demonstration, we assume there
are only two sets of sequences: sequence (A) and sequence (B). Each sequence has only two data, 1st data and 2nd data.
Note: The case of “Max = Min” is not included as there is special treatment as disclosed in the patent.
There are total 16 (=4*4) combinations crossed sequence (A) and (B).
We define:
b1 = Max value of sequence (B);
bn = Min value of sequence (B)
A1: a1 ≥ 0 an ≥ 0 │a1│>│an│
A2: a1 > 0 an < 0 │a1│>│an│
A3: a1 ≥ 0 an < 0 │a1│<│an│
A4: a1 < 0 an < 0 │a1│<│an│
B1: b1 ≥ 0 bn ≥ 0 │b1│>│bn│
B2: b1 > 0 bn < 0 │b1│>│bn│
B3: b1 ≥ 0 bn < 0 │b1│<│bn│
B4: b1 < 0 bn < 0 │b1│<│bn│
For any sequence of data, the range of the Max value and
the Min value can only be one of the 4 situations:
1: Max ≥ 0 Min ≥ 0 │Max│>│Min│
2: Max > 0 Min < 0 │Max│>│Min│
3: Max ≥ 0 Min < 0 │Max│<│Min│
4: Max < 0 Min< 0 │Max│<│Min│
®
16
Sample 1 - A1B1
A1B1 A B
1st 100 100
2nd 83 84
Original E-value 0.17 0.16
Upper Limit 100 100
Lower Limit 83 83
New E-value - 0.17
Excel Graphician
A1: B1:
a1 ≥ 0 b1 ≥ 0
an ≥ 0 bn ≥ 0
│a1│>│an│ │b1│>│bn│
100
83
84
75
80
85
90
95
100
105
0
20
40
60
80
100
120
1st 2nd
A B
100
83
84
83.0
86.4
89.8
93.2
96.6
100.0
83.0
86.4
89.8
93.2
96.6
100.0
1st 2nd
A B
-17%
-16%
-17% -16%
®
17
Sample 2 - A1B2
Excel Graphician
94
100
-91
-150
-100
-50
0
50
100
150
90
92
94
96
98
100
102
1st 2nd
A B
94
100
-91 -91.0
-52.8
-14.6
23.6
61.8
100.0
-91.0
-52.8
-14.6
23.6
61.8
100.0
1st 2nd
A B
A1: B2:
a1 ≥ 0 b1 > 0
an ≥ 0 bn < 0
│a1│>│an│ │b1│>│bn│
A1B2 A B
1st 100 100
2nd 94 -91
Original E-value 0.06 1.91
Upper Limit 100 100
Lower Limit -91 -91
New E-value 1.91 -
-6%
-191%
-6%
-191%
®
18
Sample 3 - A1B3
Excel Graphician
100
99
-100
90
-150
-100
-50
0
50
100
98.4
98.6
98.8
99.0
99.2
99.4
99.6
99.8
100.0
100.2
1st 2nd
A B
100 99
-100
90
-100
-62
-24
14
52
90
-90
-52
-14
24
62
100
1st 2nd
A B
A1: B3:
a1 ≥ 0 b1 ≥ 0
an ≥ 0 bn < 0
│a1│>│an│ │b1│<│bn│
A1B3 A B
1st 100 -100
2nd 99 90
Original E-value 0.01 1.9
Upper Limit 100 90
Lower Limit -90 -100
New E-value 1.9 -
®
19
Sample 4 - A1B4
Excel Graphician
100
99
-33
-100
-120
-100
-80
-60
-40
-20
0
98.4
98.6
98.8
99.0
99.2
99.4
99.6
99.8
100.0
100.2
1st 2nd
A B 100 99
-33
-100
-100.0
-86.6
-73.2
-59.8
-46.4
-33.0
33.0
46.4
59.8
73.2
86.6
100.0
1st 2nd
A B
A1: B4:
a1 ≥ 0 b1 < 0
an ≥ 0 bn < 0
│a1│>│an│ │b1│<│bn│
A1B4 A B
1st 100 -33
2nd 99 -100
Original E-value 0.01 0.67
Upper Limit 100 -33
Lower Limit 33 -100
New E-value 0.67 -
®
20
Sample 5 – A2B1
Excel Graphician
100
-60
90
100
84
86
88
90
92
94
96
98
100
102
-80
-60
-40
-20
0
20
40
60
80
100
120
1st 2nd
A B 100
-60
90
100
-60
-40
-20
0
20
40
60
80
100
-60
-40
-20
0
20
40
60
80
100
1st 2nd
A B
A2: B1:
a1 > 0 b1 ≥ 0
an < 0 bn ≥ 0
│a1│>│an│ │b1│>│bn│
A2B1 A B
1st 100 90
2nd -60 100
Original E-value 1.6 0.1
Upper Limit 100 100
Lower Limit -60 -60
New E-value - 1.6
®
21
Sample 6 – A2B2
Excel Graphician
-84
100
-34
-84
-38
8
54
100
-84
-38
8
54
100
1st 2nd
A B
100
-84
100
-34
-60
-40
-20
0
20
40
60
80
100
120
-100
-50
0
50
100
150
1st 2nd
A B
A2: B2:
a1 > 0 b1 > 0
an < 0 bn < 0
│a1│>│an│ │b1│>│bn│
A2B2 A B
1st 100 100
2nd -84 -34
Original E-value 1.84 1.34
Upper Limit 100 100
Lower Limit -84 -84
New E-value - 1.84
®
22
Sample 7 – A2B3
Excel Graphician
100
-99
1
-100
-120
-100
-80
-60
-40
-20
0
20
-150
-100
-50
0
50
100
150
1st 2nd
A B
100
-99
1
-100 -100.0
-60.2
-20.4
19.4
59.2
99.0
-99.0
-59.2
-19.4
20.4
60.2
100.0
1st 2nd
A B
A2: B3:
a1 > 0 b1 ≥ 0
an < 0 bn < 0
│a1│>│an│ │b1│<│bn│
A2B3 A B
1st 100 1
2nd -99 -100
Original E-value 1.99 1.01
Upper Limit 100 99
Lower Limit -99 -100
New E-value - 1.99
®
23
Sample 8 – A2B4
Excel Graphician
100
-40
-20
-100
-120
-100
-80
-60
-40
-20
0
-60
-40
-20
0
20
40
60
80
100
120
1st 2nd
A B
100
-40
-20
-100 -100
-80
-60
-40
-20
0
20
40
-40
-20
0
20
40
60
80
100
1st 2nd
A B
A2: B4:
a1 > 0 b1 < 0
an < 0 bn < 0
│a1│>│an│ │b1│<│bn│
A2B4 A B
1st 100 -20
2nd -40 -100
Original E-value 1.4 0.8
Upper Limit 100 40
Lower Limit -40 -100
New E-value - 1.4
®
24
Sample 9 – A3B1
Excel Graphician
16
-100
100
16
0
20
40
60
80
100
120
-120
-100
-80
-60
-40
-20
0
20
40
1st 2nd
A B
16
-100
100
16
-16
13
42
71
100
-100
-71
-42
-13
16
1st 2nd
A B
A3: B1:
a1 ≥ 0 b1 ≥ 0
an < 0 bn ≥ 0
│a1│<│an│ │b1│>│bn│
A3B1 A B
1st 16 100
2nd -100 16
Original E-value 1.16 0.84
Upper Limit 16 100
Lower Limit -100 -16
New E-value - 1.16
®
25
Sample 10 – A3B2
Excel Graphician
83
-100
100
-16
-40
-20
0
20
40
60
80
100
120
-150
-100
-50
0
50
100
1st 2nd
A B
83
-100
100
-16
-83.0
-52.5
-22.0
8.5
39.0
69.5
100.0
-100.0
-69.5
-39.0
-8.5
22.0
52.5
83.0
1st 2nd
A B
A3: B2:
a1 ≥ 0 b1 > 0
an < 0 bn < 0
│a1│<│an│ │b1│>│bn│
A3B2 A B
1st 83 100
2nd -100 -16
Original E-value 1.83 1.16
Upper Limit 83 100
Lower Limit -100 -83
New E-value - 1.83
®
26
Sample 11 – A3B3
Excel Graphician
10
-100
80
-100
-150
-100
-50
0
50
100
-120
-100
-80
-60
-40
-20
0
20
1st 2nd
A B
10
-100
80
-100
-80
-60
-40
-20
0
20
40
60
80
-100
-80
-60
-40
-20
0
20
40
60
80
1st 2nd
A B
A3: B3:
a1 ≥ 0 b1 ≥ 0
an < 0 bn < 0
│a1│<│an│ │b1│<│bn│
A3B3 A B
1st 10 80
2nd -100 -100
Original E-value 1.1 1.8
Upper Limit 80 80
Lower Limit -100 -100
New E-value 1.8 -
®
27
Sample 12 – A3B4
Excel Graphician
-1.0
0.9
-99
-100
-100.2
-100.0
-99.8
-99.6
-99.4
-99.2
-99.0
-98.8
-98.6
-98.4
-1.5
-1.0
-0.5
0.0
0.5
1.0
1st 2nd
A B
-1.0
0.9
-99 -100
-100
-62
-24
14
52
90
-1.00
-0.62
-0.24
0.14
0.52
0.90
1st 2nd
A B
A3: B4:
a1 ≥ 0 b1 < 0
an < 0 bn < 0
│a1│<│an│ │b1│<│bn│
A3B4 A B
1st -1.0 -99
2nd 0.9 -100
Original E-value 1.9 0.01
Upper Limit 0.9 90
Lower Limit -1.0 -100
New E-value - 1.9
®
28
Sample 13 – A4B1
Excel Graphician
-84
-100
100
83
0
20
40
60
80
100
120
-105
-100
-95
-90
-85
-80
-75
1st 2nd
A B
-84
-100
100
83
83.0
86.4
89.8
93.2
96.6
100.0
-100.0
-96.6
-93.2
-89.8
-86.4
-83.0
1st 2nd
A B
A4: B1:
a1 < 0 b1 ≥ 0
an < 0 bn ≥ 0
│a1│<│an│ │b1│>│bn│
A4B1 A B
1st -84 100
2nd -100 83
Original E-value 0.16 0.17
Upper Limit -83 83
Lower Limit -100 100
New E-value 0.17 -
®
29
Sample 14 – A4B2
Excel Graphician
-99
-100
-90
100
-100
-50
0
50
100
150
-100.2
-100.0
-99.8
-99.6
-99.4
-99.2
-99.0
-98.8
-98.6
-98.4
1st 2nd
A B
-99 -100-90
100
-90
-52
-14
24
62
100
-100
-62
-24
14
52
90
1st 2nd
A B
A4: B2:
a1 < 0 b1 > 0
an < 0 bn < 0
│a1│<│an│ │b1│>│bn│
A4B2 A B
1st -99 -90
2nd -100 100
Original E-value 0.01 1.9
Upper Limit 90 100
Lower Limit -100 -90
New E-value 1.9 -
®
30
Sample 15 – A4B3
Excel Graphician
-16
-100
82
-100
-150
-100
-50
0
50
100
-120
-100
-80
-60
-40
-20
0
1st 2nd
A B
-16
-100
82
-100
-74
-48
-22
4
30
56
82
-100
-74
-48
-22
4
30
56
82
1st 2nd
A B
A4: B3:
a1 < 0 b1 ≥ 0
an < 0 bn < 0
│a1│<│an│ │b1│<│bn│
A4B3 A B
1st -16 82
2nd -100 -100
Original E-value 0.84 1.82
Upper Limit 82 82
Lower Limit -100 -100
New E-value 1.82 -
®
31
Sample 16 – A4B4
Excel Graphician
-83
-100
-100
-84
-105
-100
-95
-90
-85
-80
-75
-120
-100
-80
-60
-40
-20
0
1st 2nd
A B
-83
-100-100
-84
-100.0
-96.6
-93.2
-89.8
-86.4
-83.0
-100.0
-96.6
-93.2
-89.8
-86.4
-83.0
1st 2nd
A B
A4: B4:
a1 < 0 b1 < 0
an < 0 bn < 0
│a1│<│an│ │b1│<│bn│
A4B4 A B
1st -83 -100
2nd -100 -84
Original E-value 0.17 0.16
Upper Limit -83 -83
Lower Limit -100 -100
New E-value - 0.17

More Related Content

What's hot

Chapter%201%20 examples 2 2
Chapter%201%20 examples 2 2Chapter%201%20 examples 2 2
Chapter%201%20 examples 2 2
jajudice911
 
Introduction to r
Introduction to rIntroduction to r
Introduction to r
Golden Julie Jesus
 
Stock price prediction regression nn
Stock price prediction   regression nnStock price prediction   regression nn
Stock price prediction regression nn
Zanshila Rodrigues
 
Curve fitting
Curve fittingCurve fitting
Curve fitting
dusan4rs
 
Basic concepts in_matlab
Basic concepts in_matlabBasic concepts in_matlab
Basic concepts in_matlab
Mohammad Alauddin
 
Microsoft Office Execel 2013 Question Bank Presentation
Microsoft Office Execel 2013 Question Bank PresentationMicrosoft Office Execel 2013 Question Bank Presentation
Microsoft Office Execel 2013 Question Bank Presentation
Liladhar Meshram
 
ML - Multiple Linear Regression
ML - Multiple Linear RegressionML - Multiple Linear Regression
ML - Multiple Linear Regression
Andrew Ferlitsch
 
Lesson 6
Lesson 6Lesson 6
Lesson 6
Vinnu Vinay
 
Mathematical modeling
Mathematical modelingMathematical modeling
Mathematical modeling
Champion Christian College
 
Matlab polynimials and curve fitting
Matlab polynimials and curve fittingMatlab polynimials and curve fitting
Matlab polynimials and curve fitting
Ameen San
 
ML - Simple Linear Regression
ML - Simple Linear RegressionML - Simple Linear Regression
ML - Simple Linear Regression
Andrew Ferlitsch
 
Penyajian Data Dalam Bentuk Tabel Mtk
Penyajian Data Dalam Bentuk Tabel MtkPenyajian Data Dalam Bentuk Tabel Mtk
Penyajian Data Dalam Bentuk Tabel Mtk
galih
 
Curve fitting
Curve fitting Curve fitting
Curve fitting
shopnohinami
 
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...
Dr.Summiya Parveen
 
Basic concepts of curve fittings
Basic concepts of curve fittingsBasic concepts of curve fittings
Basic concepts of curve fittings
Tarun Gehlot
 
Applied numerical methods lec8
Applied numerical methods lec8Applied numerical methods lec8
Applied numerical methods lec8
Yasser Ahmed
 
Graphing Absolute Value
Graphing Absolute ValueGraphing Absolute Value
Graphing Absolute Value
swartzje
 
Absolute Value Functions & Graphs
Absolute Value Functions & GraphsAbsolute Value Functions & Graphs
Absolute Value Functions & Graphs
toni dimella
 
Final project
Final projectFinal project
Polynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLABPolynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLAB
Shameer Ahmed Koya
 

What's hot (20)

Chapter%201%20 examples 2 2
Chapter%201%20 examples 2 2Chapter%201%20 examples 2 2
Chapter%201%20 examples 2 2
 
Introduction to r
Introduction to rIntroduction to r
Introduction to r
 
Stock price prediction regression nn
Stock price prediction   regression nnStock price prediction   regression nn
Stock price prediction regression nn
 
Curve fitting
Curve fittingCurve fitting
Curve fitting
 
Basic concepts in_matlab
Basic concepts in_matlabBasic concepts in_matlab
Basic concepts in_matlab
 
Microsoft Office Execel 2013 Question Bank Presentation
Microsoft Office Execel 2013 Question Bank PresentationMicrosoft Office Execel 2013 Question Bank Presentation
Microsoft Office Execel 2013 Question Bank Presentation
 
ML - Multiple Linear Regression
ML - Multiple Linear RegressionML - Multiple Linear Regression
ML - Multiple Linear Regression
 
Lesson 6
Lesson 6Lesson 6
Lesson 6
 
Mathematical modeling
Mathematical modelingMathematical modeling
Mathematical modeling
 
Matlab polynimials and curve fitting
Matlab polynimials and curve fittingMatlab polynimials and curve fitting
Matlab polynimials and curve fitting
 
ML - Simple Linear Regression
ML - Simple Linear RegressionML - Simple Linear Regression
ML - Simple Linear Regression
 
Penyajian Data Dalam Bentuk Tabel Mtk
Penyajian Data Dalam Bentuk Tabel MtkPenyajian Data Dalam Bentuk Tabel Mtk
Penyajian Data Dalam Bentuk Tabel Mtk
 
Curve fitting
Curve fitting Curve fitting
Curve fitting
 
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...
Data Approximation in Mathematical Modelling Regression Analysis and Curve Fi...
 
Basic concepts of curve fittings
Basic concepts of curve fittingsBasic concepts of curve fittings
Basic concepts of curve fittings
 
Applied numerical methods lec8
Applied numerical methods lec8Applied numerical methods lec8
Applied numerical methods lec8
 
Graphing Absolute Value
Graphing Absolute ValueGraphing Absolute Value
Graphing Absolute Value
 
Absolute Value Functions & Graphs
Absolute Value Functions & GraphsAbsolute Value Functions & Graphs
Absolute Value Functions & Graphs
 
Final project
Final projectFinal project
Final project
 
Polynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLABPolynomials and Curve Fitting in MATLAB
Polynomials and Curve Fitting in MATLAB
 

Viewers also liked

CURRICULUM
CURRICULUMCURRICULUM
Historia del pc
Historia del pcHistoria del pc
Historia del pc
Camilo Triana Gutierrez
 
Hiren_Portfolio
Hiren_PortfolioHiren_Portfolio
Hiren_PortfolioHiren Modi
 
UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)
UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)
UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)
Aditya Rana
 
Mapa conceptual simulacion de audiencia en juicio oral equipo 5
Mapa conceptual simulacion de audiencia en juicio oral equipo 5Mapa conceptual simulacion de audiencia en juicio oral equipo 5
Mapa conceptual simulacion de audiencia en juicio oral equipo 5
Yorjan Coa
 
USED LUBE OIL RECYCLING - PRESENTATION
USED LUBE OIL RECYCLING - PRESENTATIONUSED LUBE OIL RECYCLING - PRESENTATION
USED LUBE OIL RECYCLING - PRESENTATION
Oorja Systems & Consultants
 

Viewers also liked (6)

CURRICULUM
CURRICULUMCURRICULUM
CURRICULUM
 
Historia del pc
Historia del pcHistoria del pc
Historia del pc
 
Hiren_Portfolio
Hiren_PortfolioHiren_Portfolio
Hiren_Portfolio
 
UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)
UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)
UNO MINDA GROUP CORPORATE PROFILE 2014-15 (Nov Issue)
 
Mapa conceptual simulacion de audiencia en juicio oral equipo 5
Mapa conceptual simulacion de audiencia en juicio oral equipo 5Mapa conceptual simulacion de audiencia en juicio oral equipo 5
Mapa conceptual simulacion de audiencia en juicio oral equipo 5
 
USED LUBE OIL RECYCLING - PRESENTATION
USED LUBE OIL RECYCLING - PRESENTATIONUSED LUBE OIL RECYCLING - PRESENTATION
USED LUBE OIL RECYCLING - PRESENTATION
 

Similar to Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart?

CIV1900 Matlab - Plotting & Coursework
CIV1900 Matlab - Plotting & CourseworkCIV1900 Matlab - Plotting & Coursework
CIV1900 Matlab - Plotting & Coursework
TUOS-Sam
 
data-exp-Viz-00-2.pdf
data-exp-Viz-00-2.pdfdata-exp-Viz-00-2.pdf
data-exp-Viz-00-2.pdf
betsegaw123
 
2014-mo444-practical-assignment-04-paulo_faria
2014-mo444-practical-assignment-04-paulo_faria2014-mo444-practical-assignment-04-paulo_faria
2014-mo444-practical-assignment-04-paulo_faria
Paulo Faria
 
Using Microsoft Excel for Weibull Analysis by William Dorner
Using Microsoft Excel for Weibull Analysis by William DornerUsing Microsoft Excel for Weibull Analysis by William Dorner
Using Microsoft Excel for Weibull Analysis by William Dorner
Melvin Carter
 
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docxSAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
agnesdcarey33086
 
Statistics is both the science of uncertainty and the technology.docx
Statistics is both the science of uncertainty and the technology.docxStatistics is both the science of uncertainty and the technology.docx
Statistics is both the science of uncertainty and the technology.docx
rafaelaj1
 
ENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docx
ENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docxENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docx
ENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docx
YASHU40
 
More instructions for the lab write-up1) You are not obli.docx
More instructions for the lab write-up1) You are not obli.docxMore instructions for the lab write-up1) You are not obli.docx
More instructions for the lab write-up1) You are not obli.docx
gilpinleeanna
 
Statistics Questions to Answer.doc.rtf2Note An Excel Wor.docx
Statistics Questions to Answer.doc.rtf2Note An Excel Wor.docxStatistics Questions to Answer.doc.rtf2Note An Excel Wor.docx
Statistics Questions to Answer.doc.rtf2Note An Excel Wor.docx
dessiechisomjj4
 
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wiMAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
AbramMartino96
 
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wiMAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
AbramMartino96
 
qc-tools.ppt
qc-tools.pptqc-tools.ppt
qc-tools.ppt
Alpharoot
 
Introduction to Regression Analysis
Introduction to Regression AnalysisIntroduction to Regression Analysis
Introduction to Regression Analysis
Minha Hwang
 
2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fit2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fit
hartcher
 
Using microsoft excel for weibull analysis
Using microsoft excel for weibull analysisUsing microsoft excel for weibull analysis
Using microsoft excel for weibull analysis
Melvin Carter
 
Business statistics homework help
Business statistics homework helpBusiness statistics homework help
Business statistics homework help
Statistics Help Desk
 
Regression analysis in excel
Regression analysis in excelRegression analysis in excel
Regression analysis in excel
Thilina Rathnayaka
 
Matlabch01
Matlabch01Matlabch01
Matlabch01
Mohammad Ayyash
 
Exploratory data analysis v1.0
Exploratory data analysis v1.0Exploratory data analysis v1.0
Exploratory data analysis v1.0
Vishy Chandra
 
Environmental Engineering Assignment Help
Environmental Engineering Assignment HelpEnvironmental Engineering Assignment Help
Environmental Engineering Assignment Help
Matlab Assignment Experts
 

Similar to Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart? (20)

CIV1900 Matlab - Plotting & Coursework
CIV1900 Matlab - Plotting & CourseworkCIV1900 Matlab - Plotting & Coursework
CIV1900 Matlab - Plotting & Coursework
 
data-exp-Viz-00-2.pdf
data-exp-Viz-00-2.pdfdata-exp-Viz-00-2.pdf
data-exp-Viz-00-2.pdf
 
2014-mo444-practical-assignment-04-paulo_faria
2014-mo444-practical-assignment-04-paulo_faria2014-mo444-practical-assignment-04-paulo_faria
2014-mo444-practical-assignment-04-paulo_faria
 
Using Microsoft Excel for Weibull Analysis by William Dorner
Using Microsoft Excel for Weibull Analysis by William DornerUsing Microsoft Excel for Weibull Analysis by William Dorner
Using Microsoft Excel for Weibull Analysis by William Dorner
 
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docxSAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
SAMPLE QUESTIONExercise 1 Consider the functionf (x,C).docx
 
Statistics is both the science of uncertainty and the technology.docx
Statistics is both the science of uncertainty and the technology.docxStatistics is both the science of uncertainty and the technology.docx
Statistics is both the science of uncertainty and the technology.docx
 
ENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docx
ENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docxENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docx
ENGR 102B Microsoft Excel Proficiency LevelsPlease have your in.docx
 
More instructions for the lab write-up1) You are not obli.docx
More instructions for the lab write-up1) You are not obli.docxMore instructions for the lab write-up1) You are not obli.docx
More instructions for the lab write-up1) You are not obli.docx
 
Statistics Questions to Answer.doc.rtf2Note An Excel Wor.docx
Statistics Questions to Answer.doc.rtf2Note An Excel Wor.docxStatistics Questions to Answer.doc.rtf2Note An Excel Wor.docx
Statistics Questions to Answer.doc.rtf2Note An Excel Wor.docx
 
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wiMAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
 
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wiMAT 240 Random Sampling in Excel Tutorial This tutorial wi
MAT 240 Random Sampling in Excel Tutorial This tutorial wi
 
qc-tools.ppt
qc-tools.pptqc-tools.ppt
qc-tools.ppt
 
Introduction to Regression Analysis
Introduction to Regression AnalysisIntroduction to Regression Analysis
Introduction to Regression Analysis
 
2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fit2.6b scatter plots and lines of best fit
2.6b scatter plots and lines of best fit
 
Using microsoft excel for weibull analysis
Using microsoft excel for weibull analysisUsing microsoft excel for weibull analysis
Using microsoft excel for weibull analysis
 
Business statistics homework help
Business statistics homework helpBusiness statistics homework help
Business statistics homework help
 
Regression analysis in excel
Regression analysis in excelRegression analysis in excel
Regression analysis in excel
 
Matlabch01
Matlabch01Matlabch01
Matlabch01
 
Exploratory data analysis v1.0
Exploratory data analysis v1.0Exploratory data analysis v1.0
Exploratory data analysis v1.0
 
Environmental Engineering Assignment Help
Environmental Engineering Assignment HelpEnvironmental Engineering Assignment Help
Environmental Engineering Assignment Help
 

Recently uploaded

Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language ProcessorsFundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
Rakesh Kumar R
 
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeA Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Aftab Hussain
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOMLORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
lorraineandreiamcidl
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Safe Software
 
GreenCode-A-VSCode-Plugin--Dario-Jurisic
GreenCode-A-VSCode-Plugin--Dario-JurisicGreenCode-A-VSCode-Plugin--Dario-Jurisic
GreenCode-A-VSCode-Plugin--Dario-Jurisic
Green Software Development
 
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdfAutomated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
timtebeek1
 
SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024
Hironori Washizaki
 
May Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdfMay Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdf
Adele Miller
 
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
Łukasz Chruściel
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
Octavian Nadolu
 
APIs for Browser Automation (MoT Meetup 2024)
APIs for Browser Automation (MoT Meetup 2024)APIs for Browser Automation (MoT Meetup 2024)
APIs for Browser Automation (MoT Meetup 2024)
Boni García
 
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise EditionWhy Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
Envertis Software Solutions
 
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppAI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
Google
 
Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604
Fermin Galan
 
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Crescat
 
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Łukasz Chruściel
 
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsUI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
Peter Muessig
 
E-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet DynamicsE-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet Dynamics
Hornet Dynamics
 
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissancesAtelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Neo4j
 
Mobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona InfotechMobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona Infotech
Drona Infotech
 

Recently uploaded (20)

Fundamentals of Programming and Language Processors
Fundamentals of Programming and Language ProcessorsFundamentals of Programming and Language Processors
Fundamentals of Programming and Language Processors
 
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeA Study of Variable-Role-based Feature Enrichment in Neural Models of Code
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
 
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOMLORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
 
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FMEEssentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
 
GreenCode-A-VSCode-Plugin--Dario-Jurisic
GreenCode-A-VSCode-Plugin--Dario-JurisicGreenCode-A-VSCode-Plugin--Dario-Jurisic
GreenCode-A-VSCode-Plugin--Dario-Jurisic
 
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdfAutomated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
 
SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024SWEBOK and Education at FUSE Okinawa 2024
SWEBOK and Education at FUSE Okinawa 2024
 
May Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdfMay Marketo Masterclass, London MUG May 22 2024.pdf
May Marketo Masterclass, London MUG May 22 2024.pdf
 
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf2024 eCommerceDays Toulouse - Sylius 2.0.pdf
2024 eCommerceDays Toulouse - Sylius 2.0.pdf
 
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension FunctionsArtificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
 
APIs for Browser Automation (MoT Meetup 2024)
APIs for Browser Automation (MoT Meetup 2024)APIs for Browser Automation (MoT Meetup 2024)
APIs for Browser Automation (MoT Meetup 2024)
 
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise EditionWhy Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
 
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppAI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
 
Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604Orion Context Broker introduction 20240604
Orion Context Broker introduction 20240604
 
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
 
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Need for Speed: Removing speed bumps from your Symfony projects ⚡️
Need for Speed: Removing speed bumps from your Symfony projects ⚡️
 
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsUI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
 
E-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet DynamicsE-commerce Development Services- Hornet Dynamics
E-commerce Development Services- Hornet Dynamics
 
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissancesAtelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
 
Mobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona InfotechMobile App Development Company In Noida | Drona Infotech
Mobile App Development Company In Noida | Drona Infotech
 

Have you ever wonder how Excel sets the upper limit and the lower limit on the vertical axis of a chart? And how this may lead to a misleading chart?

  • 1. Dual vertical axes chart scaling algorithm comparison with Excel’s May 2016 Confidential Questions/Comments? Please contact Jennifer Lin at graphician1122@gmail.com 1
  • 2. ® 100 36 100 84 36 52 68 84 100 36 52 68 84 100 Q1 Q2 A B 100 36 100 84 75 80 85 90 95 100 105 0 20 40 60 80 100 120 Q1 Q2 A B Executive Summary 2  Graphician is pleased to present one of our 5 US-patented algorithms.  An algorithm to truthfully present the intelligence of data graphically on dual vertical axes chart.  An algorithm can be easily incorporated into conventional tableted data applications such as Excel. -16-64 Excel algorithm mispresents a decrease from 100 to 36 and a decrease from 100 to 84 is the same. Graphician algorithmExcel dual vertical axes chart -16 -64 Graphician algorithm shows a decrease from 100 to 36 is more than a decrease from 100 to 84.
  • 3. ® -91 100 94 90 92 94 96 98 100 102 -150 -100 -50 0 50 100 150 Q1 Q2 A B What algorithm Excel adopts for dual axes chart now? 3 Excel single vertical axis chart Excel dual vertical axes chart -6-191 Excel adopts the same algorithm for single vertical axis chart when setting the scales of dual vertical axes chart, thus the elongations of both axes are not coordinated to be the same. 100100 -91 -150 -100 -50 0 50 100 150 Q1 Q2 A
  • 4. ® -91 100 94 -91 -43 5 52 100 -91 -43 5 52 100 Q1 Q2 A B -91 100 94 90 92 94 96 98 100 102 -150 -100 -50 0 50 100 150 Q1 Q2 A B 4 Excel algorithm vs. Graphician algorithm Excel algorithm mispresents a decrease from 100 to -91 and a decrease from 100 to 94 is about the same. Graphician algorithm shows a decrease from 100 to -91 is much more than a decrease from 100 to 94. Excel dual vertical axes chart -6 -191 Graphician algorithm -6 -191 100 100
  • 5. ® No negative base value Commonly used “Base Value” method misleads too 5 Graphician algorithm is the only solution which can correctly present the interaction/relationship between the data sets in all kinds of situations on chart. Base Value Period A B Q1 100 20 Q2 20 100 Change -80 +80 100% 20% 100% 500% 0% 100% 200% 300% 400% 500% 600% Q1 Q2 A B Line A’s decrease should be equal to Line B’s increase 100 2020 100 20 40 60 80 100 20 40 60 80 100 Q1 Q2 A B With negative base value Base Value 100% -80% 100% -300% -400% -300% -200% -100% 0% 100% 200% Q1 Q2 A B Line A’s decrease should be more than Line B’s decrease 100 -80 -20 -100 -100 -80 -60 -40 -20 0 20 40 60 80 -80 -60 -40 -20 0 20 40 60 80 100 Q1 Q2 A B Period A B Q1 100 -20 Q2 -80 -100 Change -180 -80 Graphician Graphician
  • 6. ® 6 Misled by chart (case 1): What drove the increase of sales? Period Selling Price Units Sold Sales Q1 84 9,762 820,000 Q2 100 10,000 1,000,000 Excel Excel algorithm presents as the selling price and units sold both increased, but there was no much increase on sales. 820,000 1,000,000 84 100 75 80 85 90 95 100 105 - 200,000 400,000 600,000 800,000 1,000,000 1,200,000 Q1 Q2 Sales Selling Price Excel 820,000 1,000,000 9,762 10,000 9,600 9,650 9,700 9,750 9,800 9,850 9,900 9,950 10,000 10,050 - 200,000 400,000 600,000 800,000 1,000,000 1,200,000 Q1 Q2 Sales Units Sold
  • 7. ® 7 Misled by chart (case 1): What drove the increase of sales? (cont.) Period Selling Price Units Sold Sales Q1 84 9,762 820,000 Q2 100 10,000 1,000,000 Graphician 820,000 1,000,000 9,762 10,000 8,200 8,560 8,920 9,280 9,640 10,000 820,000 856,000 892,000 928,000 964,000 1,000,000 Q1 Q2 Sales Units Sold Graphician 820,000 1,000,000 84 100 82.0 85.6 89.2 92.8 96.4 100.0 820,000 856,000 892,000 928,000 964,000 1,000,000 Q1 Q2 Sales Selling Price Graphician algorithm presents the fact that the main driver of increased sales is the increased selling price.
  • 8. ® 820,000 1,000,000 84 100 82.0 85.6 89.2 92.8 96.4 100.0 820,000 856,000 892,000 928,000 964,000 1,000,000 Q1 Q2 Sales Selling Price 8,200 8,560 8,920 9,280 9,640 10,000 9,762 10,000 8 Misled by chart (case 1): What drove the increase of sales? (cont.) Period Selling Price Units Sold Sales Q1 84 9,762 820,000 Q2 100 10,000 1,000,000 Graphician Graphician algorithm presents the fact that the main driver of increased sales is the increased selling price.
  • 9. ® Misled by chart (case 2): What drove the growth of number of employed labor? 9 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 110,000 115,000 120,000 125,000 130,000 135,000 140,000 145,000 150,000 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Employed in all industries Employed in non-agricultural industries Excel Graphician auto scaling algorithm 119,651 124,511 129,371 134,232 139,092 143,952 121,392 126,323 131,254 136,185 141,116 146,047 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Employed in all industries Employed in non-agricultural industries Graphician 18.7% 20.3% 18.7% 20.3%  Graphician shows the fact that increase of employed in all industries was mainly contributed by increase of employed in non-agricultural industries in modern society.  Excel shows there was few relationship between the 2 data sets. Source: U.S. Bureau of Labor Statistics
  • 10. ® Misled by chart (case 3): Which stock performed better? 10 Source: Stock price data base 30.53 31.76 32.98 34.21 35.43 36.66 24.15 25.12 26.09 27.06 28.03 29.00 3/23/2004 4/23/2004 5/23/2004 6/23/2004 7/23/2004 8/23/2004 9/23/2004 10/23/2004 Microsoft Dell 31.00 32.00 33.00 34.00 35.00 36.00 37.00 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 3/23/2004 4/23/2004 5/23/2004 6/23/2004 7/23/2004 8/23/2004 9/23/2004 10/23/2004 Microsoft Dell Excel Graphician auto scaling algorithmGraphician 20.1% 11.1% 20.1% 11.1%  Graphician shows the movement of the 2 stocks in same elongation and the fact that Microsoft’s share price performed better than Dell’s.  Microsoft’s share price grew 20.1% while Dell’s grew only 11.1%. However, the chart indicated that Dell’s price movement was much larger than Microsoft’s.
  • 11. ® 100 36 1000 840 360 520 680 840 1000 36 52 68 84 100 Q1 Q2 A B 100 36 1000 840 750 800 850 900 950 1000 1050 0 20 40 60 80 100 120 Q1 Q2 A B Key steps of the algorithm 11 A B Q1 100 1000 Q2 36 840 Original E-value 0.64 = (100-36)/100 0.16 = (1000-840)/1000 Upper limit of the axis 100 = A’s Max 1000 = B’s Max Lower limit of the axis 36 = A’s Min 360 = B’s Max × A’s Min/A’s Max New E-value N/A 0.64 = (1000-360)/1000 Note: (1) Which of the upper and lower limit should be unchanged and how to calculate the other limit is disclosed in the flowchart next page. Calculate the E-value of each sequence (A: 0.64; B:0.16) Set upper and lower limits of the axis with larger E-value (A: 0.64) as its Max & Min (100 & 36) Set one of the upper and lower limit of the axis with smaller E- value (B: 0.16) unchanged (1000) (1) Calculate the other limit of the axis with smaller E-value (360). B’ new E-value (0.64) equals to A’s original E-value (0.64) (1) 4 key steps 1 2 3 4 1 2 3 4 1 2 4 2 2 3 4 -16% -64%-16%-64% Excel algorithm Graphician algorithm
  • 12. ® Step 3 & 4 of the algorithm: Which of the upper and lower limit should be changed and how? │Max value of 1st data set │ ≥ │Min value of 1st data set │ │Max value of 2nd data set │ ≥ │Min value of 2nd data set │ Adjust lower limit of 2nd data set’s axis = 2nd data set max value × 1st data set mini value ÷ 1st data set max value │Max value of 2nd data set │ ≥ │Min value of 2nd data set │ Yes Yes Yes No No No Adjust upper limit of 2nd data set’s axis = 2nd data set mini value × 1st data set mini value ÷ 1st data set max value Adjust lower limit of 2nd data set’s axis = 2nd data set max value × 1st data set max value ÷ 1st data set mini value Adjust upper limit of 2nd data set’s axis = 2nd data set mini value × 1st data set max value ÷ 1st data set mini value i. 1st data set refers to the data set with larger E-Value ii. 2nd data set refers to the data set with smaller E-Value Upper limit of 2nd data set’s axis unchanged = 2nd data set max value Upper limit of 2nd data set’s axis unchanged = 2nd data set max value Lower limit of 2nd data set’s axis unchanged = 2nd data set mini value Lower limit of 2nd data set’s axis unchanged = 2nd data set mini value 12
  • 13. ® Step 3 & 4 of the algorithm (cont.): Yes Yes 100 36 1000 840 360 520 680 840 1000 36 52 68 84 100 Q1 Q2 A B A B Q1 100 1000 Q2 36 840 Original E-value 0.64 = (100-36)/100 0.16 = (1000-840)/1000 Upper limit of the axis 100 = A’s Max 1000 = B’s Max Lower limit of the axis 36 = A’s Min 360 = B’s Max × A’s Min/A’s Max New E-value N/A 0.64 = (1000-360)/1000 1 2 3 4 1 2 4 2 2 3 4 -16% -64% Graphician algorithm │100│≥│36│ │1000│≥│840│ Adjust lower limit of 2nd data set’s axis = 1000 × 36 ÷ 100 =360 Upper limit of 2nd data set’s axis unchanged = 1000 i. Here 1st data set is data set A ii. Here 2nd data set is data set B 13
  • 14. ® Appendix: Demonstration of all kinds of situations 14
  • 15. ® Demonstration of all kinds of situations 15 We define: a1 = Max value of sequence (A); an = Min value of sequence (A)  To prove that the patented algorithm can present the true interaction of data with the same elongation ratio under all kinds of situations, we will demonstrate one example for each situation.  Though the algorithm can be applied to charts with multiple vertical axes, to simplify the demonstration, we assume there are only two sets of sequences: sequence (A) and sequence (B). Each sequence has only two data, 1st data and 2nd data. Note: The case of “Max = Min” is not included as there is special treatment as disclosed in the patent. There are total 16 (=4*4) combinations crossed sequence (A) and (B). We define: b1 = Max value of sequence (B); bn = Min value of sequence (B) A1: a1 ≥ 0 an ≥ 0 │a1│>│an│ A2: a1 > 0 an < 0 │a1│>│an│ A3: a1 ≥ 0 an < 0 │a1│<│an│ A4: a1 < 0 an < 0 │a1│<│an│ B1: b1 ≥ 0 bn ≥ 0 │b1│>│bn│ B2: b1 > 0 bn < 0 │b1│>│bn│ B3: b1 ≥ 0 bn < 0 │b1│<│bn│ B4: b1 < 0 bn < 0 │b1│<│bn│ For any sequence of data, the range of the Max value and the Min value can only be one of the 4 situations: 1: Max ≥ 0 Min ≥ 0 │Max│>│Min│ 2: Max > 0 Min < 0 │Max│>│Min│ 3: Max ≥ 0 Min < 0 │Max│<│Min│ 4: Max < 0 Min< 0 │Max│<│Min│
  • 16. ® 16 Sample 1 - A1B1 A1B1 A B 1st 100 100 2nd 83 84 Original E-value 0.17 0.16 Upper Limit 100 100 Lower Limit 83 83 New E-value - 0.17 Excel Graphician A1: B1: a1 ≥ 0 b1 ≥ 0 an ≥ 0 bn ≥ 0 │a1│>│an│ │b1│>│bn│ 100 83 84 75 80 85 90 95 100 105 0 20 40 60 80 100 120 1st 2nd A B 100 83 84 83.0 86.4 89.8 93.2 96.6 100.0 83.0 86.4 89.8 93.2 96.6 100.0 1st 2nd A B -17% -16% -17% -16%
  • 17. ® 17 Sample 2 - A1B2 Excel Graphician 94 100 -91 -150 -100 -50 0 50 100 150 90 92 94 96 98 100 102 1st 2nd A B 94 100 -91 -91.0 -52.8 -14.6 23.6 61.8 100.0 -91.0 -52.8 -14.6 23.6 61.8 100.0 1st 2nd A B A1: B2: a1 ≥ 0 b1 > 0 an ≥ 0 bn < 0 │a1│>│an│ │b1│>│bn│ A1B2 A B 1st 100 100 2nd 94 -91 Original E-value 0.06 1.91 Upper Limit 100 100 Lower Limit -91 -91 New E-value 1.91 - -6% -191% -6% -191%
  • 18. ® 18 Sample 3 - A1B3 Excel Graphician 100 99 -100 90 -150 -100 -50 0 50 100 98.4 98.6 98.8 99.0 99.2 99.4 99.6 99.8 100.0 100.2 1st 2nd A B 100 99 -100 90 -100 -62 -24 14 52 90 -90 -52 -14 24 62 100 1st 2nd A B A1: B3: a1 ≥ 0 b1 ≥ 0 an ≥ 0 bn < 0 │a1│>│an│ │b1│<│bn│ A1B3 A B 1st 100 -100 2nd 99 90 Original E-value 0.01 1.9 Upper Limit 100 90 Lower Limit -90 -100 New E-value 1.9 -
  • 19. ® 19 Sample 4 - A1B4 Excel Graphician 100 99 -33 -100 -120 -100 -80 -60 -40 -20 0 98.4 98.6 98.8 99.0 99.2 99.4 99.6 99.8 100.0 100.2 1st 2nd A B 100 99 -33 -100 -100.0 -86.6 -73.2 -59.8 -46.4 -33.0 33.0 46.4 59.8 73.2 86.6 100.0 1st 2nd A B A1: B4: a1 ≥ 0 b1 < 0 an ≥ 0 bn < 0 │a1│>│an│ │b1│<│bn│ A1B4 A B 1st 100 -33 2nd 99 -100 Original E-value 0.01 0.67 Upper Limit 100 -33 Lower Limit 33 -100 New E-value 0.67 -
  • 20. ® 20 Sample 5 – A2B1 Excel Graphician 100 -60 90 100 84 86 88 90 92 94 96 98 100 102 -80 -60 -40 -20 0 20 40 60 80 100 120 1st 2nd A B 100 -60 90 100 -60 -40 -20 0 20 40 60 80 100 -60 -40 -20 0 20 40 60 80 100 1st 2nd A B A2: B1: a1 > 0 b1 ≥ 0 an < 0 bn ≥ 0 │a1│>│an│ │b1│>│bn│ A2B1 A B 1st 100 90 2nd -60 100 Original E-value 1.6 0.1 Upper Limit 100 100 Lower Limit -60 -60 New E-value - 1.6
  • 21. ® 21 Sample 6 – A2B2 Excel Graphician -84 100 -34 -84 -38 8 54 100 -84 -38 8 54 100 1st 2nd A B 100 -84 100 -34 -60 -40 -20 0 20 40 60 80 100 120 -100 -50 0 50 100 150 1st 2nd A B A2: B2: a1 > 0 b1 > 0 an < 0 bn < 0 │a1│>│an│ │b1│>│bn│ A2B2 A B 1st 100 100 2nd -84 -34 Original E-value 1.84 1.34 Upper Limit 100 100 Lower Limit -84 -84 New E-value - 1.84
  • 22. ® 22 Sample 7 – A2B3 Excel Graphician 100 -99 1 -100 -120 -100 -80 -60 -40 -20 0 20 -150 -100 -50 0 50 100 150 1st 2nd A B 100 -99 1 -100 -100.0 -60.2 -20.4 19.4 59.2 99.0 -99.0 -59.2 -19.4 20.4 60.2 100.0 1st 2nd A B A2: B3: a1 > 0 b1 ≥ 0 an < 0 bn < 0 │a1│>│an│ │b1│<│bn│ A2B3 A B 1st 100 1 2nd -99 -100 Original E-value 1.99 1.01 Upper Limit 100 99 Lower Limit -99 -100 New E-value - 1.99
  • 23. ® 23 Sample 8 – A2B4 Excel Graphician 100 -40 -20 -100 -120 -100 -80 -60 -40 -20 0 -60 -40 -20 0 20 40 60 80 100 120 1st 2nd A B 100 -40 -20 -100 -100 -80 -60 -40 -20 0 20 40 -40 -20 0 20 40 60 80 100 1st 2nd A B A2: B4: a1 > 0 b1 < 0 an < 0 bn < 0 │a1│>│an│ │b1│<│bn│ A2B4 A B 1st 100 -20 2nd -40 -100 Original E-value 1.4 0.8 Upper Limit 100 40 Lower Limit -40 -100 New E-value - 1.4
  • 24. ® 24 Sample 9 – A3B1 Excel Graphician 16 -100 100 16 0 20 40 60 80 100 120 -120 -100 -80 -60 -40 -20 0 20 40 1st 2nd A B 16 -100 100 16 -16 13 42 71 100 -100 -71 -42 -13 16 1st 2nd A B A3: B1: a1 ≥ 0 b1 ≥ 0 an < 0 bn ≥ 0 │a1│<│an│ │b1│>│bn│ A3B1 A B 1st 16 100 2nd -100 16 Original E-value 1.16 0.84 Upper Limit 16 100 Lower Limit -100 -16 New E-value - 1.16
  • 25. ® 25 Sample 10 – A3B2 Excel Graphician 83 -100 100 -16 -40 -20 0 20 40 60 80 100 120 -150 -100 -50 0 50 100 1st 2nd A B 83 -100 100 -16 -83.0 -52.5 -22.0 8.5 39.0 69.5 100.0 -100.0 -69.5 -39.0 -8.5 22.0 52.5 83.0 1st 2nd A B A3: B2: a1 ≥ 0 b1 > 0 an < 0 bn < 0 │a1│<│an│ │b1│>│bn│ A3B2 A B 1st 83 100 2nd -100 -16 Original E-value 1.83 1.16 Upper Limit 83 100 Lower Limit -100 -83 New E-value - 1.83
  • 26. ® 26 Sample 11 – A3B3 Excel Graphician 10 -100 80 -100 -150 -100 -50 0 50 100 -120 -100 -80 -60 -40 -20 0 20 1st 2nd A B 10 -100 80 -100 -80 -60 -40 -20 0 20 40 60 80 -100 -80 -60 -40 -20 0 20 40 60 80 1st 2nd A B A3: B3: a1 ≥ 0 b1 ≥ 0 an < 0 bn < 0 │a1│<│an│ │b1│<│bn│ A3B3 A B 1st 10 80 2nd -100 -100 Original E-value 1.1 1.8 Upper Limit 80 80 Lower Limit -100 -100 New E-value 1.8 -
  • 27. ® 27 Sample 12 – A3B4 Excel Graphician -1.0 0.9 -99 -100 -100.2 -100.0 -99.8 -99.6 -99.4 -99.2 -99.0 -98.8 -98.6 -98.4 -1.5 -1.0 -0.5 0.0 0.5 1.0 1st 2nd A B -1.0 0.9 -99 -100 -100 -62 -24 14 52 90 -1.00 -0.62 -0.24 0.14 0.52 0.90 1st 2nd A B A3: B4: a1 ≥ 0 b1 < 0 an < 0 bn < 0 │a1│<│an│ │b1│<│bn│ A3B4 A B 1st -1.0 -99 2nd 0.9 -100 Original E-value 1.9 0.01 Upper Limit 0.9 90 Lower Limit -1.0 -100 New E-value - 1.9
  • 28. ® 28 Sample 13 – A4B1 Excel Graphician -84 -100 100 83 0 20 40 60 80 100 120 -105 -100 -95 -90 -85 -80 -75 1st 2nd A B -84 -100 100 83 83.0 86.4 89.8 93.2 96.6 100.0 -100.0 -96.6 -93.2 -89.8 -86.4 -83.0 1st 2nd A B A4: B1: a1 < 0 b1 ≥ 0 an < 0 bn ≥ 0 │a1│<│an│ │b1│>│bn│ A4B1 A B 1st -84 100 2nd -100 83 Original E-value 0.16 0.17 Upper Limit -83 83 Lower Limit -100 100 New E-value 0.17 -
  • 29. ® 29 Sample 14 – A4B2 Excel Graphician -99 -100 -90 100 -100 -50 0 50 100 150 -100.2 -100.0 -99.8 -99.6 -99.4 -99.2 -99.0 -98.8 -98.6 -98.4 1st 2nd A B -99 -100-90 100 -90 -52 -14 24 62 100 -100 -62 -24 14 52 90 1st 2nd A B A4: B2: a1 < 0 b1 > 0 an < 0 bn < 0 │a1│<│an│ │b1│>│bn│ A4B2 A B 1st -99 -90 2nd -100 100 Original E-value 0.01 1.9 Upper Limit 90 100 Lower Limit -100 -90 New E-value 1.9 -
  • 30. ® 30 Sample 15 – A4B3 Excel Graphician -16 -100 82 -100 -150 -100 -50 0 50 100 -120 -100 -80 -60 -40 -20 0 1st 2nd A B -16 -100 82 -100 -74 -48 -22 4 30 56 82 -100 -74 -48 -22 4 30 56 82 1st 2nd A B A4: B3: a1 < 0 b1 ≥ 0 an < 0 bn < 0 │a1│<│an│ │b1│<│bn│ A4B3 A B 1st -16 82 2nd -100 -100 Original E-value 0.84 1.82 Upper Limit 82 82 Lower Limit -100 -100 New E-value 1.82 -
  • 31. ® 31 Sample 16 – A4B4 Excel Graphician -83 -100 -100 -84 -105 -100 -95 -90 -85 -80 -75 -120 -100 -80 -60 -40 -20 0 1st 2nd A B -83 -100-100 -84 -100.0 -96.6 -93.2 -89.8 -86.4 -83.0 -100.0 -96.6 -93.2 -89.8 -86.4 -83.0 1st 2nd A B A4: B4: a1 < 0 b1 < 0 an < 0 bn < 0 │a1│<│an│ │b1│<│bn│ A4B4 A B 1st -83 -100 2nd -100 -84 Original E-value 0.17 0.16 Upper Limit -83 -83 Lower Limit -100 -100 New E-value - 0.17