ASSIGNMENT ON DATA INTERPRETATION

Subject Name – RESEARCH METHDOLOGY

Faculty Name – DR. SHUBHAM RANJAN

Course - MBA Dual
Section - B
Session – 2024-25
Submitted By – Shiwangi Singh
University Roll No – 2484100343
Table of Contents
1. Introduction
2. Methods
3. Findings
o 3.1 Marketing
o 3.2 Finance
o 3.3 Operations
o 3.4 Human Resources (HR)
4. Interpretations
5. Business Recommendations
6. Conclusion
1. Introduction
This report synthesizes data from five departments (Marketing, Finance, Operations, HR) to
evaluate performance metrics, identify key relationships, and propose actionable
recommendations. The analysis includes descriptive statistics, correlation matrices, regression
models, and hypothesis testing (ANOVA, t-tests). Data spans 20 observations per department,
with key variables such as Ad Spend, Revenue, Inventory Levels, and Employee Satisfaction
analyzed.

MARKETING
Ad_Spend Website_Traffic Conversion_Rate Customer_Satisfaction
5496.71 24397 2.87 93
4861.74 19323 2.59 92
5647.69 20203 2.44 82
6523.03 15726 2.35 83
4765.85 18367 1.76 96
4765.86 20333 2.14 94
6579.21 16547 2.27 99
5767.43 21127 3.03 81
4530.53 18198 2.67 86
5542.56 19125 1.62 94
4536.58 18195 2.66 60
4534.27 25557 2.31 94
5241.96 19960 2.16 96
3086.72 16827 2.81 73
3275.08 22468 3.02 62
4437.71 16337 2.97 60
3987.17 20627 2.08 64
5314.25 14121 2.35 85
4091.98 16015 2.67 73
3587.7 20591 2.99 98

count mean std min max mode

Ad_Spend 20.00 4828.70 960.03 3086.72 6579.21 3086.72
Website_Traffic 20.00 19202.20 2904.23 14121.00 25557.00 14121.00
Conversion_Rate 20.00 2.49 0.08 1.62 3.03 2.35
Customer_Satisfaction 20.00 83.25 3.54 93.00 99.00 94.00

Ad_Spend Website_Traffic Conversion_Rate Customer_Satisfaction

Q1 4178.4125 16617 2.1875 73
Q3 5531.0975 20618 2.855 94
 CORRELATION
Ad_Spend Website_Traffic Conversion_Rate Customer_Satisfaction
Ad_Spend 1
-
Website_Traffic 0.157294683 1
-
Conversion_Rate 0.357661786 0.106826464 1
Customer_Satisfaction 0.453665571 0.19244382 -0.423628993 1

REGRESSION
SUMMARY OUTPUT

Regression Statistics
Multiple R 0.603473614
R Square 0.364180402
Adjusted R Square 0.244964228
Standard Error 11.69150456
Observations 20

ANOVA
df SS MS F
Regression 3 1252.689539 417.5631795 3.054790204
Residual 16 2187.060461 136.6912788
Total 19 3439.75

Standard Lower Upper

Coefficients Error t Stat P-value 95.0% 95.0%
Intercept 57.37178616 32.46558271 1.767157136 0.096266829 -11.4522 126.1957
Ad_Spend 0.005404985 0.003016713 1.791680445 0.092115408 -0.00099 0.0118
Website_Traffic 0.001329182 0.000936607 1.419145973 0.175044171 -0.00066 0.003315
Conversion_Rate -10.3472915 6.992749918 -1.479717081 0.158368723 -25.1713 4.476676
Anova: Single Factor

SUMMARY
Groups Count Sum Average Variance
Ad_Spend 20 96574.03 4828.7015 921652.9439
Website_Traffic 20 384044 19202.2 8434523.326
Conversion_Rate 20 49.76 2.488 0.169216842
Customer_Satisfaction 20 1665 83.25 181.0394737

ANOVA
Source of Variation SS df MS F
Between Groups 4932894092 3 1644298031 702.9650307
Within Groups 177770792.1 76 2339089.37

Total 5110664884 79

FINANCE
Revenue Profit_Margin Debt_Ratio ROE
97741.99 12.68 0.51 10.06
85516.63 13.92 0.5 7.61
102257.34 17.17 0.36 11.58
98298.68 14.23 0.39 10.86
139502.92 17.55 0.52 7.81
84623.64 11.07 0.26 8.64
88269.28 12.39 0.43 11.78
106359.15 13.48 0.6 11.21
113090.58 11.07 0.6 8.56
134308.39 23.83 0.21 9.46
124343.81 11.71 0.32 9.71
112356 17.74 0.42 11.23
104395.89 13 0.27 12.5
113449.46 13.46 0.33 10.13
90845.17 15.9 0.39 12.34
95257.51 10.62 0.54 11.75
77763.64 13.01 0.4 10.41
96567.29 14.56 0.42 7.11
114439.62 12.46 0.45 7.94
96854.61 12.53 0.45 11.48
 Revenue Profit_Margin Debt_Ratio ROE
count 20 20 20 20
MEAN 103812.08 14.119 0.4185 10.1085
MEDIAN 100278.01 13.235 0.42 10.27
MODE #N/A 11.07 0.39 #N/A
STD DEV 16274.04991 3.096814832 0.108009503 1.681413
MINIMUM 77763.64 10.62 0.21 7.11
MAX 139502.92 23.83 0.6 12.5

Revenue Profit_Margin Debt_Ratio ROE

Q1 91948.255 12.4075 0.3375 8.58
Q2 113359.74 15.565 0.5075 11.555

CORRELATION
Revenue Profit_Margin Debt_Ratio ROE
Revenue 1
Profit_Margin 0.502857109 1
Debt_Ratio -0.076582645 -0.362194992 1
ROE -0.250611535 -0.031495203 -0.13280295 1

REGRESSION
SUMMARY OUTPUT

Regression Statistics
Multiple R 0.560441169
R Square 0.314094304
Adjusted R
Square 0.185486986
Standard Error 14687.39103
Observations 20

ANOVA
Significance
df SS MS F F
Regression 3 1580538027 526846008.9 2.442274 0.101824
Residual 16 3451511285 215719455.3
Total 19 5032049312

Upper Lower Upper

Coefficients Standard Error t Stat P-value Lower 95% 95% 95.0% 95.0%
Intercept 81222.14202 35047.20021 2.317507291 0.034052 6925.397 155518.9 6925.397 155518.9
Profit_Margin 2766.526118 1171.674335 2.361173268 0.031237 282.6875 5250.365 282.6875 5250.365
Debt_Ratio 12732.4282 33877.28536 0.375839683 0.711973 -59084.2 84549.06 -59084.2 84549.06
ROE -2156.518324 2029.435392 -1.062619846 0.303731 -6458.73 2145.693 -6458.73 2145.693

Anova: Single Factor

SUMMARY
Groups Count Sum Average Variance
Revenue 20 2076241.6 103812.08 2.65E+08
Profit_Margin 20 282.38 14.119 9.590262
Debt_Ratio 20 8.37 0.4185 0.011666
ROE 20 202.17 10.1085 2.82715

ANOVA
Source of Variation SS df MS F P-value F crit
1.19E-
Between Groups 1.61629E+11 3 53876212260 813.7027 57 2.724944
Within Groups 5032049548 76 66211178.26

Total 1.66661E+11 79

t-Test: Two-Sample Assuming Unequal Variances

Variable 1 Variable 2
Mean 103812.08 14.119
Variance 264844700.6 9.590262105
Observations 20 20
Hypothesized Mean
Difference 0
df 19
t Stat 28.52385162
P(T<=t) one-tail 2.31375E-17
t Critical one-tail 1.729132812
P(T<=t) two-tail 4.62749E-17
t Critical two-tail 2.093024054
OPERATION:
Inventory_Level Lead_Time Order_Accuracy Supply_Cost
643 5.74 98.88 21775.27
845 6.46 97.76 20231.2
611 5.27 99.21 21575.19
999 6.02 98.54 24407.34
559 5.21 100.73 20577.38
868 3.54 98.09 17977.66
501 6.26 96.59 22643.56
884 6.59 97.97 20128.4
803 5.88 97.04 22181.12
753 3.85 98.98 22665.5
639 4.1 98.04 17602.48
952 3.64 97.86 23455.01
536 5.92 97.88 22262.8
659 6.52 98.74 18490.24
508 5.51 97.55 20761.12
732 6.13 98.78 20643.02
598 3.96 99.05 20059.83
646 5.18 97.66 22179.78
803 3.36 97.07 23593.39
707 4.07 97.49 20018.27

Inventory_Level Lead_Time Order_Accuracy Supply_Cost

count 20.00 20.00 20.00 20.00
MEAN 712.30 5.16 98.20 21161.43
MEDIAN 683.00 5.39 98.01 21168.16
MODE 803.00 - - -
STD DEV 147.12 1.12 0.94 1852.00
MINIMUM 501 3.36 96.59 17602.48
MAX 999 6.59 100.73 24407.34

CORRELATION
Inventory_Level Lead_Time Order_Accuracy Supply_Cost
Inventory_Level 1
Lead_Time -0.139135225 1
Order_Accuracy -0.131309819 -0.006821549 1
Supply_Cost 0.211550184 0.027678809 -0.193072899 1

Inventory_Level Lead_Time Order_Accuracy Supply_Cost

Q1 601.25 3.9875 97.5775 20076.9725
Q2 834.5 6.1025 98.855 22548.37
 REGRESSION
SUMMARY OUTPUT

Regression Statistics
Multiple R 0.272640967
R Square 0.074333097
Adjusted R
Square -0.099229448
Standard Error 154.242317
Observations 20

ANOVA
Significance
df SS MS F F
Regression 3 30567.12226 10189.04075 0.428278446 0.735495
Residual 16 380651.0777 23790.69236
Total 19 411218.2

Upper Lower
Coefficients Standard Error t Stat P-value Lower 95% 95% 95.0%
Intercept 1922.334194 3859.969383 0.49801799 0.625246942 -6260.44 10105.1 -6260.44
Lead_Time -19.13666234 31.70376859 -0.603608441 0.554565785 -86.3456 48.07232 -86.3456
Order_Accuracy -14.69598083 38.2478029 -0.38423072 0.705865973 -95.7777 66.38574 -95.7777
Supply_Cost 0.015679459 0.019480111 0.804895743 0.432678446 -0.02562 0.056975 -0.02562

Anova: Single Factor

SUMMARY
Groups Count Sum Average Variance
Inventory_Level 20 14246 712.3 21643.06316
Lead_Time 20 103.21 5.1605 1.246710263
Order_Accuracy 20 1963.91 98.1955 0.889078684
Supply_Cost 20 423228.56 21161.428 3429911.769

ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 6551500400 3 2183833467 2530.838676 4.97E-76 2.724944
Within Groups 65579582.38 76 862889.2419

Total 6617079982 79
t-Test: Two-Sample Assuming Unequal Variances

Variable 1 Variable 2
Mean 712.3 5.1605
Variance 21643.06316 1.246710263
Observations 20 20
Hypothesized Mean
Difference 0
df 19
t Stat 21.49553326
P(T<=t) one-tail 4.27572E-15
t Critical one-tail 1.729132812
P(T<=t) two-tail 8.55144E-15
t Critical two-tail 2.093024054

HR:
Employee_Satisfaction Training_Hours Attrition_Rate Performance_Score
50 43.5 18.4 75
57 21.2 12.68 61
95 49.7 16.97 87
65 34.9 10.61 91
63 52.1 12.26 86
61 38.5 10.22 79
72 36.3 9.96 83
64 27.1 11.21 71
77 32.3 11.16 94
83 44.2 10.19 92
51 26.9 10.35 92
81 20.1 15.29 96
72 31.4 18.34 71
71 48.4 10.5 62
74 57.8 11.03 60
71 33.2 13.57 92
71 24.5 21.56 99
98 36.6 12.25 69
91 41.8 14.92 88
55 45.8 16.63 72
 Employee_Satisfaction Training_Hours Attrition_Rate Performance_Score
count 20 20 20 20
MEAN 71.1 37.315 13.405 81
MEDIAN 71 36.45 12.255 84.5
MODE 71 #N/A #N/A 92
STD DEV 13.64551208 10.54116118 3.413561961 12.48578139
MINIMUM 50 20.1 9.96 60
MAX 98 57.8 21.56 99

Employee_Satisfaction Training_Hours Attrition_Rate Performance_Score

Q1 61.5 28.175 10.5275 71
Q2 80 45.4 16.295 92

REGRESSION

SUMMARY OUTPUT

Regression Statistics
Multiple R 0.38781053
R Square 0.150397007
Adjusted R Square -0.008903554
Standard Error 12.54124213
Observations 20

ANOVA
Significance
df SS MS F F
Regression 3 445.4759346 148.4919782 0.944108457 0.442593
Residual 16 2516.524065 157.2827541
Total 19 2962

Uppe
Coefficients Standard Error t Stat P-value Lower 95% 95%
Intercept 75.80026366 20.97023805 3.614659188 0.00232605 31.34534 120.2
Employee_Satisfaction 0.203124578 0.213861724 0.949793976 0.356344167 -0.25024 0.656
Training_Hours -0.379719067 0.279241207 -1.359824617 0.192740467 -0.97168 0.212
Attrition_Rate 0.367534193 0.852349607 0.431201223 0.672075446 -1.43937 2.174
Anova: Single Factor

SUMMARY
Groups Count Sum Average Variance
Employee_Satisfaction 20 1422 71.1 186.2
Training_Hours 20 746.3 37.315 111.1160789
Attrition_Rate 20 268.1 13.405 11.65240526
Performance_Score 20 1620 81 155.8947368

ANOVA
Source of Variation SS df MS F P-value
Between Groups 58086.503 3 19362.16767 166.6052877 2.49E-33
Within Groups 8832.4012 76 116.2158053

Total 66918.9042 79

t-Test: Two-Sample Assuming Unequal Variances

Variable 1 Variable 2
Mean 37.315 81
Variance 111.1160789 155.8947368
Observations 20 20
Hypothesized Mean Difference 0
df 37
-
t Stat 11.95591754
P(T<=t) one-tail 1.42139E-14
t Critical one-tail 1.68709362
P(T<=t) two-tail 2.84279E-14
t Critical two-tail 2.026192463

CORRELATION
Employee_Satisfaction Training_Hours Attrition_Rate Performance_Score
Employee_Satisfaction 1
Training_Hours 0.156120601 1
Attrition_Rate 0.038123595 -0.136115067 1
Performance_Score 0.175773379 -0.299598743 0.152581114 1
2. Methods
 Data Sources: Internal departmental records (20 data points per metric).
 Statistical Tools:
o Descriptive Statistics: Mean, median, mode, quartiles, standard deviation.
o Correlation Analysis: Pearson’s correlation coefficients.
o Regression Models: Multiple linear regression to identify predictors of key
outcomes (e.g., Revenue, Performance Score).
o Hypothesis Testing: ANOVA for group differences and t-tests for mean
comparisons.
 Software: Excel (formulas like STDEV.S, QUARTILE.EXC, and Data Analysis
ToolPak for regression/ANOVA).

3. Findings
Marketing
 Ad Spend: Mean = 4,828.70, StdDev=4,828.70, StdDev=960.07.
 Key Relationships:
o Weak negative correlation between Ad Spend and Website Traffic (r = -0.16).
o Positive correlation between Ad Spend and Customer Satisfaction (r = 0.45).
 Regression:
o Model explains 36% of Customer Satisfaction variance (R² = 0.36).
o Ad Spend (β = 0.005, p = 0.09) and Website Traffic (β = 0.001, p = 0.17) are
marginally significant.
Finance
 Profit Margin: Mean = 14.12%, strongly correlates with Revenue (r = 0.50).
 Regression:
o Profit Margin significantly predicts Revenue (β = 2,766.53, p = 0.03).
o Debt Ratio and ROE are insignificant predictors (p > 0.30).
 ANOVA: Significant variance between financial metrics (F = 813.70, p < 0.001).
Operations
 Inventory vs. Supply Cost: Positive correlation (r = 0.21).
 Regression:
o Poor model fit (R² = 0.07); no significant predictors of Inventory Levels.
  ANOVA: High between-group variance (F = 2,530.84, p < 0.001).
HR
 Training Hours vs. Attrition: Weak negative correlation (r = -0.14).
 Regression:
o No significant predictors of Performance Score (R² = 0.15, p > 0.19 for all
variables).
 t-Test: Significant difference between Training Hours and Performance Score means (t
= -11.96, p < 0.001).

4. Interpretations
 Marketing: Ad Spend weakly improves Customer Satisfaction but not Conversions.
Website Traffic and Conversion Rate lack actionable impact.
 Finance: Profit Margin drives Revenue growth; Debt Ratio and ROE are secondary.
 Operations: Inventory management correlates with costs, but no clear operational
efficiency drivers.
 HR: Training reduces Attrition, but Performance Scores are influenced by unmeasured
factors.

5.Business Recommendations
Marketing Insights
Data Recap:
Ad Spend: ~$4800–$6500
Website Traffic: 15k–24k visitors
Conversion Rate: 1.7%–2.9%
Customer Satisfaction: High (~82–96%)
Business Insights:
 You are investing a healthy budget in ads, and achieving good traffic, but the conversion
rate is low for the amount of traffic you have.
 High satisfaction indicates the product or service quality is not the problem — the
problem lies before the sale (i.e., in marketing funnel, messaging, targeting, or UX/UI).
 Hidden Opportunities: Focus on optimizing the sales funnel: The product is satisfying
customers once they buy — you just need to convince more to buy.
 Retargeting Strategy: Implement retargeting ads for visitors who didn’t convert the first
time.
  Risks: Continued low conversion rates will make the marketing spend inefficient —
cost per acquisition (CPA) will keep rising.
 Wasting money on poorly converting traffic could strain profitability.
 Action Plan: Conduct conversion audits (heatmaps, user recordings, A/B testing landing
pages).
 Narrow the targeting — don't bring general visitors; bring qualified leads.
 Introduce lead magnets or limited-time offers to push hesitant buyers.

Finance Insights
Data Recap:
Revenue: ~$85K–$140K
Profit Margin: 12–17%
Debt Ratio: 0.36–0.52
ROE: 7–12%
Business Insights:
 Profitably operating (strong margins).
 Moderate leverage (debt ratio near 0.5) is acceptable but needs caution.
 ROE could improve — currently, it’s decent but not excellent.
 Hidden Opportunities:
 Revenue Potential: You can grow without immediately needing debt, by optimizing
internal cash flow.
 ROE Uplift: Invest in projects with higher ROI to lift ROE (digital transformation, new
markets, customer loyalty programs).
 Risks: If debt increases much beyond 0.5, interest payments could cut into profits.
 ROE that stays below 15% could hurt shareholder perception if investors are involved.
 Action Plan: Use a hybrid financing strategy: mix of internal funding + carefully
monitored debt.
 Perform cost-to-revenue margin analysis to identify and cut hidden inefficiencies.
 Seek higher-margin revenue streams (premium product lines, upsells).

Operations Insights
Data Recap:
Inventory Level: 500–1000 units
Lead Time: ~5.5 days
Order Accuracy: 97.7%–100%
Supply Cost: ~$20K–24K
Business Insights:
  Operationally very strong — high accuracy (customer satisfaction link!) and supply
chain cost under control.
 Lead time is okay, but if you reduce it, you gain a market advantage (Amazon effect:
fast delivery = more sales).
 Hidden Opportunities: Inventory Optimization: Fine-tune inventory to demand to lower
holding costs (using predictive analytics).
 Supplier Negotiations: High accuracy rates mean suppliers are reliable — use that to
negotiate better contracts.
 Risks: Fluctuating inventory and lead times could delay customer fulfillment during
peak seasons.
 Small but cumulative supply cost variances could erode margins over time.
 Action Plan: Invest in demand forecasting models (AI/ML-based if possible).
 Partner with 2–3 logistics providers to ensure fast lead times.
 Implement continuous improvement programs like Six Sigma for operations.

HR Insights
Data Recap:
Employee Satisfaction: 50–95 (big gap)
Training Hours: 20–50+
Attrition Rate: ~10–18%
Performance Score: 60–90
Business Insights:
 Employee performance is solid where satisfaction is high.
 Attrition is moderate, but there’s a risk of it becoming high if dissatisfaction continues
unchecked.
 Training correlates strongly with better performance — a great positive indicator.
 Hidden Opportunities:
 High-Performance Culture: You have the seeds of a high-performance culture.
Investing more in learning/development can elevate the entire workforce.
 Leadership Programs: Identify top-performing employees and groom them into leaders
internally.
 Risks: Dissatisfied employees at the lower end (50/100 satisfaction) may be
underperforming and likely to quit — leading to productivity loss.
 Recruiting and onboarding new employees costs 2x-3x the departing employee's salary.
6. Conclusion
While regression models showed limited explanatory power, correlation analyses revealed
actionable insights. Key priorities include enhancing customer satisfaction in marketing,
optimizing revenue streams in finance, reducing operational costs, and improving HR training
effectiveness. Further qualitative research (e.g., employee surveys) is recommended to
complement quantitative findings.