Management Analytics Report: Impact of Ranking on Starting Salary

This report delves into the realm of management analytics, specifically investigating the correlation between academic program rankings and the starting salaries of graduates. The analysis commences with an introduction to management analytics, emphasizing its significance in informed decision-making within businesses. The main body of the report employs descriptive statistics, including mean, median, and mode, to summarize and interpret data, followed by a regression analysis to assess the relationship between program rankings and starting salaries. Furthermore, the study incorporates a regression analysis to evaluate the connection between international student status and starting salaries. The findings reveal that while program ranking has minimal impact, international student status positively influences starting salaries. The report culminates in a discussion of managerial implications, highlighting how statistical techniques aid in data management and the drawing of actionable conclusions. The conclusion underscores the importance of data-driven analysis in effective business management, advocating for the use of statistical tools for comprehensive decision-making.

MANAGEMENT ANALYTICS

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Descriptive analysis....................................................................................................................3
Regression analysis.....................................................................................................................5
Managerial interpretation and implication..................................................................................7
CONCLUSION................................................................................................................................8
REFERENCES................................................................................................................................9

INTRODUCTION
Management analytics is being defined as the development of the understanding relating to the different factors influencing the
decision making. Hence, it is very essential for the different businesses to ensure that they undertake all the factors affecting the
business decision. The current study is based on the analysis of question that “can the ranks of the academic programs have an impact
on the starting salary of graduate”. Hence, for proving this question different statistical tools and techniques will be applied
effectively. This will involve the use of descriptive statistics and the regression analysis. In the end the managerial implication and the
interpretation will be provided.
MAIN BODY
Descriptive analysis
The descriptive analysis is being defined as the type of analysis which assist in summarising the data in brief and clear manner
(Wang and et.al., 2021). It is the summary statistics which is being used quantitatively to describe the data and draw inferences
relating to the objective of the study. Hence, for analysing the effects of rank over the starting salary the following summary statistics
that is mean, median and mode are being calculated.
Fulltime
Business
Week
Ranking Enrollment
Avg
GMAT
Resident
Tuition,
Fees
Pct
International
Pct
Female
Pct Asian
American
Pct
Minority
Pct with job
offers
Avg
starting
base
salary
1 1144 713 97165 35 35 16 7 92 107091
2 1801 720 101660 33 38 94 124378
3 1200 711 93918 34 36 25 13 95 108064
4 1651 714 104410 44 36 7.8 9 89 112186
5 898 706 80879 27 34 21 13 89 103608
6 739 726 97842 43 36 94 121171
7 1234 94104 33 32 12 13 87 107450
8 878 696 95000 40 39 19 12 92 100136

9 780 708 46784 36 35 14 9 92 111184
10 500 714 66475 39 30 29 4 90 108967
11 593 694 93000 27 39 15 7 84 100700
12 506 712 91905 31 33 8 13 94 110305
13 832 708 89184 32 41 14 15 84 101063
14 731 711 66590 32 34 14 6 80 101306
15 473 663 40882 37 26 12 13 88 92000
16 644 693 84000 30 29 6 9 92 103963
17 562 678 43503 32 32 5 10 82 95647
18 201 642 81384 18 28 7 8 80 84828
19 392 690 93840 23 21 25 12 96 103012
20 316 677 77340 28 24 10 9 80 89660
21 552 681 48800 24 31 11 11 88 96537
22 315 673 18530 10 20 5 78 88958
23 373 680 82856 42 39 8 14 82 93620
24 382 718 93098 28 34 14 11 89 99562
25 525 692 88800 34 29 23 6 88 91863
26 257 660 60583 35 29 18 11 81 85225
27 225 688 43556 18 36 15 5 85 86426
28 294 681 83172 34 37 7 12 81 90775
29 156 681 17816 19 27 7 5 98 93403
30 382 656 81076 18 25 7 6 83 89891
Mean 651.20 692.62 75271.73 30.53 32.17 13.70 9.57 87.57 100099
Median 538.50 693 83014 32 33.5 14 9.50 88 100418
Mode 382 681 #N/A 34 36 14 13 92 #N/A
With the above table it was clear that the descriptive analysis is important. With help of the mean value the average of all the
data is being provided. Hence, this will provide an average value for the complete data set which will effectively assist in analysing
the data (Anitha and Patil, 2018). With help of median as well it will be easier for the researcher in analysing the fact that how much

the data is being divided in half from the mean. In addition to this, the mode will assist in finding the number which is coming in most
repetitive manner.
Regression analysis
Regression analysis is being defined as the statistical tool which is used in evaluating the relation between the two variables
that is dependent and independent (Balas, Sharma and Chakrabarti, 2019). This analysis will outline the fact that change in the
independent factor will cause how much changes within the dependent factor as well.
Regression between starting salary and rank
Regression
Statistics
Multiple R 0.80695
R Square 0.65117
Adjusted R
Square 0.63871
Standard Error 6122.45
Observations 30
ANOVA
df SS MS F Significance F
Regression 1 1959218427 1959218427 52.26759991 7.19167E-08
Residual 28 1049562560 37484377.1
Total 29 3008780986
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 114571 2292.69054 49.9723457
6.37866E-
29 109874.7605 119267.49 109874.7605 119267.49
Fulltime -933.67 129.144317 -7.2296335 7.19167E- - -669.1259 - -

Business Week
Ranking 08 1198.206214 1198.206214 669.12593
Regression between starting salary and international student
Regression
Statistics
Multiple R 0.517
R Square 0.267
Adjusted R
Square 0.241
Standard Error 8873.95
Observations 30
ANOVA
df SS MS F Significance F
Regression 1 803863368 803863368 10.208 0.003
Residual 28 2204917618 78747057.8
Total 29 3008780986
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95% Lower 95.0%
Upper
95.0%
Intercept 80542.6 6331.789 12.720 3.7197E-13 67572.481 93512.645 67572.481 93512.645
Pct
International 640.504 200.469 3.195 0.00345 229.861 1051.148 229.861 1051.148

Managerial interpretation and implication
With the analysis of the above calculation it can be stated that the ranking of the academic program does not have much impact
over the starting salary of the student (Izonin, 2021). This is pertaining to the fact that multiple R was 0.81 which simply means that
there is 81 % correlation between both these factors. In addition to this the R square was 0.65 which in turn means that the in case
there is change in independent factor then this will lead to 65 % change within the independent factor. But on basis of the significance
value, there is not much relationship between both these variable that is starting salary and ranking of program. This is particularly
because of the reason that the significance value is 7.19 which is higher than the standard value that is p= 0.05. Hence, it can be stated
that the ranking of the program does not have much impact over the starting salary of the student.
In addition to this, another regression data is used which is based on the student being international and its relation with the
starting salary. Here the correlation between the two variables was 51 % and the R square is 26 %. This simply means that any change
within the independent factor will lead to 26 % of change in the dependent factor (Choi, Wallace and Wang, 2018). In addition to this
the significance value is also on basis of the standard value that is p = 0.05. the significance value in the regression analysis is 0.003
which is smaller than the standard value. Hence, it can be stated that there is a significant relationship between the fact that is the
student is international then this will create a positive impact over the starting salary of the student. Hence it can be stated that the null
hypothesis is rejected and the alternate hypothesis is being accepted stating that there is relationship being present.
Further with the above analysis it can be implied that the use of statistical techniques was assistive in managing the large data.
This was easier with the help of descriptive statistics (Kapur and et.al. eds., 2019). This is particularly because of the reason that with
help of mean a single average value was calculated which was assistive in analysing the data in proper and effective manner. In
addition to this the large set of data can be effectively used on basis of the average value and this will be beneficial for the researcher
to conduct the study in better and effective manner. Also, it was implied that the use of mode was also helpful in analysing the
identifying the most common and repetitive number which has occurred frequently. Hence, this will assist the reader in analysing the
fact that which value is occurring repeatedly and these are the most common points. Thus, this will result in the effective analysis of

the data and proper conclusions can be drawn that the ranking of the program has no impact over the starting salary of the student. On
the other hand, the nationality of the student creates an impact over the starting salary. This simply means that if the student is
international then they will be getting higher starting salary as compared to the local student (Tiwari, Wee and Daryanto, 2018).
CONCLUSION
In the end, it was evaluated that the managerial analytics is very essential for the effective management of the business. this is
pertaining to the fact that before taking any decision there need to be in- depth analysis to be undertaken. Thus, the above study
undertook the use of the different statistical tools. This included the use of the tools like descriptive analysis and regression analysis.
Hence, in the end the data after application of these statistical tool was analysed and interpreted.

REFERENCES
Books and Journals
Anitha, P. and Patil, M. M., 2018. A review on data analytics for supply chain management: a case study. International Journal of
Information Engineering and Electronic Business. 10(5). p.30.
Balas, V. E., Sharma, N. and Chakrabarti, A., 2019. Data Management, Analytics and Innovation. Springer Singapore:.
Choi, T. M., Wallace, S. W. and Wang, Y., 2018. Big data analytics in operations management. Production and Operations
Management. 27(10). pp.1868-1883.
Izonin, I., 2021. AIoT Data Management, Analytics and Decision Making (Artificial Intelligence of Things Data Management,
Analytics and Decision Making). International Journal of Sensors Wireless Communications and Control. 11(5). pp.496-497.
Kapur, P. K., and et.al. eds., 2019. System Performance and Management Analytics. Springer Singapore, Imprint: Springer.
Tiwari, S., Wee, H. M. and Daryanto, Y., 2018. Big data analytics in supply chain management between 2010 and 2016: Insights to
industries. Computers & Industrial Engineering. 115. pp.319-330.
Wang, S., and et.al., 2021. A survey on trajectory data management, analytics, and learning. ACM Computing Surveys (CSUR).
54(2). pp.1-36.