Primary and Secondary Data Analysis with Graphs and Spreadsheets

This presentation focuses on the analysis of primary and secondary data using graphs and spreadsheets. It explores the relationship between training expenditures and student grades in Ann's college. The presentation suggests that while training expenditures are increasing, the improvement in grades is minimal. It recommends taking feedback from students to identify the root cause and make necessary changes to improve the educational institute.

BDM

3.1primary and secondary data collected with the
help of graph and spread sheets
Months Training Expenditures Student Grades %
1 1700 40
2 3300 45
3 3400 50
4 4200 55
5 4500 60

Diagrams
31/12/1899 01/01/1900 02/01/1900 03/01/1900 04/01/1900
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
1700
3300 3400
4200
4500
40 45 50 55 60
Training Expenditures
Student Grades %

Graphs
31/12/1899
01/01/1900
02/01/1900
03/01/1900
04/01/1900
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000
1700
3300
3400
4200
4500
40
45
50
55
60
Student Grades %
Training Expenditures

Graphs
31/12/1899 01/01/1900 02/01/1900 03/01/1900 04/01/1900
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
1700
3300 3400
4200
4500
40 45 50 55 60
Training Expenditures
Student Grades %

Interpretation:
From the graphs and tables it is found that training expenditures are increasing rapidly
in the Ann's college but with the increment in expenses grades of students are also
increasing gradually. But after so many expenses some students have not increased
their grades. So it is required by management of Ann's college that they should analyse
the reason behind it. Even it has been seen that increment in grades is very minute after
improving educational facilities as well in the college (Herrera and et. al, 2014). Ann's
college is needed to take feedback of its students for identifying the root cause of this
problem. So that management can make new plan for the improvement in the
educational institute. By this way management team will get to know what actually
needs of students from the college. So that it will be able to implement necessary
required changes, which can give improvement in the grades (Vázquez, and et. al,
2013).

3.2 business presentation
Months Training Expenditures Student Grades %
1 1700 40
2 3300 45
3 3400 50
4 4200 55
5 4500 60

Trend line diagram of secondary data
35 40 45 50 55 60 65
0
10
20
30
40
50
60
70
f(x) = x
R² = 1
Series1
Linear (Series1)

Interpretation
From the above the graphs it can be concluded that Ann's college is spending
more in training programs so grades will be higher.

References
Croskerry, P., 2013. From mindless to mindful practice—cognitive bias and
clinical decision making. N Engl J Med. 368(26). pp.2445-2448.
Herrera, F. and et. al., 2014. Hesitant fuzzy sets: an emerging tool in decision
making. International Journal of Intelligent Systems. 29(6). pp.493-494.
Kacprzyk, J. and Fedrizzi, M., 2012. Multiperson decision making models
using fuzzy sets and possibility theory (Vol. 18). Springer Science & Business
Media.
Keršulienė, V. and Turskis, Z., 2014. An integrated multi-criteria group
decision making process: selection of the chief accountant. Procedia-Social
and Behavioral Sciences. 110. pp.897-904.

References
Croskerry, P., 2013. From mindless to mindful practice—cognitive bias and
clinical decision making. N Engl J Med. 368(26). pp.2445-2448.
Herrera, F. and et. al., 2014. Hesitant fuzzy sets: an emerging tool in decision
making. International Journal of Intelligent Systems. 29(6). pp.493-494.
Kacprzyk, J. and Fedrizzi, M., 2012. Multiperson decision making models
using fuzzy sets and possibility theory (Vol. 18). Springer Science & Business
Media.
Keršulienė, V. and Turskis, Z., 2014. An integrated multi-criteria group
decision making process: selection of the chief accountant. Procedia-Social
and Behavioral Sciences. 110. pp.897-904.

Thank You