Mdcat preparations and Updates 2020-2021

28/10/2026

📊 **Stata Research Case Study | Digital Renaming, Legitimacy & Investor Response**

🔍 **Research Objective**
This study examined how US digitalization and differences in digital legitimacy relate to firms’ digital renaming decisions, and whether renaming is associated with changes in institutional, US, and domestic investors’ investment.

🛠️ **Statistical Analysis in Stata**

📈 **1. Digital Trends & Descriptive Statistics**
Standardized search-intensity trends from 2012–2018 showed changing patterns in China and the US. The correlation analysis (N = 6,338) indicated a positive association between US digitalization and digital renaming (r = 0.13, p < 0.01). Growing stage was also positively associated with renaming (r = 0.10, p < 0.01).

📉 **2. Random-Effects Logistic Regression**
During the growth stage, US digitalization was positively associated with digital renaming (β = 1.970, p < 0.001). After including digital legitimacy difference, the association remained significant (β = 2.455, p < 0.001), while legitimacy difference showed a negative association (β = −1.082, p < 0.05).

⚠️ The nascent-stage model could not be estimated under the original specification because too few firms renamed digitally.

🔬 3. Robustness & Sensitivity Analysis
When examining only firms’ first digital renaming, the US digitalization coefficients were no longer statistically significant. Alternative stage definitions produced significant growth-stage associations, but the nascent-stage results.

💼 **4. Investment Impact: Fixed-Effects Regression**
Firm fixed-effects models with year effects and firm-clustered standard errors examined institutional, US, and domestic investment.

• Institutional investment: the renaming × post-renaming coefficient was negative but insignificant in both growth and nascent stages.

• US investment: the interaction was negative and insignificant in both stages.

• Domestic investment: the interaction was also negative and insignificant in both stages.

🎯 Key Takeaway
Stata-based results suggest a strong association between US digitalization and digital renaming during the growth stage

📌 Software Expertise
Stata | RStudio | SPSS | EViews | Python | Jamovi | Minitab | Excel | JASP | SmartPLS | AMOS | NVivo | Dedoose | Tableau | Power BI | Gretl | Atlas.ti

📊 **My Professional Services**
✅ Quantitative & qualitative data analysis
✅ Regression, panel-data and econometric analysis
✅ Descriptive statistics, correlation and hypothesis testing
✅ Research interpretation, results and report writing
✅ Thesis, dissertation, research-paper and business analytics support
📩 For professional data-analysis and research-writing consultancy:

📱 WhatsApp: +923056302785 | +923177953330
📧 Email: [[email protected]](mailto:[email protected])
🏢 Platform: Axiom Data Consultants

28/10/2026

🔹Redistribution of reciprocal seats in med colleges - 22 x Seats of AJ&K candidates were planned to be reduced in KP on Friday, 24 October 2026.
▪️Through most concerned efforts of key stakeholders internal & external colleagues, the plan was revoked very kindly by KMU authorities, giving priority privileged status to AJ&K.
▪️34 x seats for AJ&K students remain intact, Alhamdolillah Shukr Alhamdolillah.

28/10/2026

27/10/2026

📊 **Stata Research Case Study | Digital Renaming, Legitimacy & Investor Response**

🔍 **Research Objective**
This study examined how US digitalization and differences in digital legitimacy relate to firms’ digital renaming decisions, and whether renaming is associated with changes in institutional, US, and domestic investors’ investment.

🛠️ **Statistical Analysis in Stata**

📈 **1. Digital Trends & Descriptive Statistics**
Standardized search-intensity trends from 2012–2018 showed changing patterns in China and the US. The correlation analysis (N = 6,338) indicated a positive association between US digitalization and digital renaming (r = 0.13, p < 0.01). Growing stage was also positively associated with renaming (r = 0.10, p < 0.01).

📉 **2. Random-Effects Logistic Regression**
During the growth stage, US digitalization was positively associated with digital renaming (β = 1.970, p < 0.001). After including digital legitimacy difference, the association remained significant (β = 2.455, p < 0.001), while legitimacy difference showed a negative association (β = −1.082, p < 0.05).

⚠️ The nascent-stage model could not be estimated under the original specification because too few firms renamed digitally.

🔬 3. Robustness & Sensitivity Analysis
When examining only firms’ first digital renaming, the US digitalization coefficients were no longer statistically significant. Alternative stage definitions produced significant growth-stage associations, but the nascent-stage results.

💼 **4. Investment Impact: Fixed-Effects Regression**
Firm fixed-effects models with year effects and firm-clustered standard errors examined institutional, US, and domestic investment.

• Institutional investment: the renaming × post-renaming coefficient was negative but insignificant in both growth and nascent stages.

• US investment: the interaction was negative and insignificant in both stages.

• Domestic investment: the interaction was also negative and insignificant in both stages.

🎯 Key Takeaway
Stata-based results suggest a strong association between US digitalization and digital renaming during the growth stage

📌 Software Expertise
Stata | RStudio | SPSS | EViews | Python | Jamovi | Minitab | Excel | JASP | SmartPLS | AMOS | NVivo | Dedoose | Tableau | Power BI | Gretl | Atlas.ti

📊 **My Professional Services**
✅ Quantitative & qualitative data analysis
✅ Regression, panel-data and econometric analysis
✅ Descriptive statistics, correlation and hypothesis testing
✅ Research interpretation, results and report writing
✅ Thesis, dissertation, research-paper and business analytics support
📩 For professional data-analysis and research-writing consultancy:

📱 WhatsApp: +923056302785 | +923177953330
📧 Email: [[email protected]](mailto:[email protected])
🏢 Platform: Axiom Data Consultants

27/10/2026

UHS VC Reviews MBBS/BDS Admissions 2026–27, Directs Colleges to Facilitate Selected Candidates

A meeting of principals of public sector medical and dental colleges was held today under the chairmanship of the Vice Chancellor, University of Health Sciences (UHS), Maj. Gen (retd) Prof Sohail Sabir, to review the admission process and timelines for MBBS/BDS admissions for the academic session 2026–27.

The Vice Chancellor directed all colleges to ensure maximum facilitation for selected candidates reporting for admission and joining formalities. It was decided that dedicated facilitation desks would be established at colleges to guide students and help ensure a smooth and hassle-free admission process.

Special arrangements were also directed for the newly established medical colleges in Mianwali and Mandi Bahauddin to ensure that incoming students receive the necessary guidance and support.

UHS remains committed to ensuring a transparent, well-coordinated and student-friendly admission process.

27/10/2026

*PUNJAB PUBLIC MEDICAL COLLEGES*
🎓 Merit-Wise List | MBBS Admissions

🏛️ *College Fee & Hostel Fee Comparison other . UHS fee which mention in prospectus is separate*

*🔰 King Edward Medical University (KEMU),* Lahore
🔖College Fee: Rs. 55,000
🔖Hostel Fee: Rs. 18,000

*🔰 Allama Iqbal Medical College (AIMC), Lahore*
🔖College Fee: Rs. 20,000 approx
🔖Hostel Fee: Rs. 66,000 approx

*🔰 Services Institute of Medical Sciences (SIMS), Lahore*
🔖College Fee: Rs. 33,000
🔖Hostel Fee: 48,000

*🔰 Nishtar Medical College, Multan*
🔖College Fee: Rs. 18,000
🔖Hostel Fee: Rs. 50,000

*🔰 Rawalpindi Medical University (RMU)*
🔖College Fee: Rs. 27,000
🔖Hostel Fee: Rs. 63,000

*🔰 Faisalabad Medical University (FMU)*
🔖College Fee: Rs. 28,000
🔖Hostel Fee: Rs. 57,000

*🔰 Quaid-e-Azam Medical College* (QAMC), Bahawalpur
🔖College Fee: Rs. 66,000
🔖Hostel Fee: Rs. 86,000

*🔰 Gujranwala Medical College (GMC)*
🔖College Fee: Rs. 23,000
🔖Hostel Fee: Rs. 39,000

*🔰 Sahiwal Medical College (SLMC)*
🔖College Fee: Rs. 33,000
🔖Hostel Fee: Rs. 46,000

*🔰 Khawaja Muhammad Safdar Medical College* (KMSMC), Sialkot
🔖College Fee: Rs. 31,000
🔖Hostel Fee: Not available

*🔰 Nawaz Sharif Medical College* (NSMC), Gujrat
🔖College Fee: Rs. 42,000
🔖Hostel Fee: Rs. 51,000

*🔰 Sargodha Medical College (SMC)*
🔖College Fee: Rs. 25,000
🔖Hostel Fee: Rs. 52,000

*🔰 Shaikh Zayed Medical College (SZMC), Rahim Yar Khan*
🔖College Fee: Rs. 40,000
🔖Hostel Fee: Rs. 50,000

*🔰 Narowal Medical College*
🔖College Fee: Rs. 33,000
🔖Hostel Fee: Rs. 50,000

26/10/2026

Intermediate 2nd Annual 2026 Date sheet

26/10/2026

25/10/2026

📢 NUMS Medical Colleges – 1st Year MBBS Fee Structure

Planning to pursue MBBS through NUMS-affiliated medical colleges? 🎓

Check the 1st Year MBBS Fee Structure of the following colleges:

1. CMH Medical College, Bahawalpur – Rs. 2,105,000
2. CMH Kharian Medical College – Rs. 2,000,000
3. CMH Lahore Medical College – Rs. 2,625,000
4. CMH Multan Medical College – Rs. 1,972,969
5. HITEC Institute of Medical Sciences, Taxila – Rs. 2,100,000
6. Karachi Institute of Medical Sciences (KIMS), Malir – Rs. 1,890,000
7. Quetta Institute of Medical Sciences (QIMS) – Rs. 1,240,080
8. Wah Medical College, Wah Cantt – Rs. 2,024,000
9. Bahria University Medical College, Karachi – Rs. 2,191,482
10. Bahria University College of Medicine, Islamabad – Rs. 2,000,000
11. Fazaia Medical College, Islamabad – Rs. 1,982,000
12. Fazaia Ruth Pfau Medical College, Karachi – Rs. 1,982,000
13. Foundation University Medical College, Islamabad – Rs. 2,022,000
14. NUST School of Health Sciences, Islamabad – Rs. 2,298,000
15. Army Medical College, Rawalpindi – Rs. 1,210,000

📌 Important: Fees are listed as provided and should be verified from the respective colleges’ official prospectuses, as charges and fee policies may vary.

25/10/2026

Data Analysis By using

📊 CPI Forecasting Using ARMA Time-Series Analysis | Research Case Study

📈 Understanding inflation trends through statistical modeling!

In this project, I analyzed monthly Consumer Price Index (CPI) data from 1991 to 2020 using the Box–Jenkins approach to identify a suitable forecasting model.

🔍 Key Analytical Steps
📊 Explored CPI trends and changes over time.
📉 Used ACF and PACF plots to identify AR and MA patterns.
🧮 Estimated ARMA(3,3) and compared an alternative model specification.
✅ Evaluated statistical significance, chi-square statistics, AIC/BIC, residual autocorrelations, and inverse roots.
🔮 Generated forecasts to examine future CPI behavior.

📌 Key Results
✔️ ARMA(3,3) was selected based on ACF/PACF analysis and reported model-selection criteria.
📈 AR and MA coefficients were statistically significant (p < 0.01).
📉 AIC: -3139.278 | BIC: -3123.745
📊 Sample size: 359 monthly observations.
✅ Diagnostic results supported the model’s suitability for the analyzed CPI series.
🔮 Forecasts suggested stabilization in the CPI series, according to the project’s reported interpretation.

💡 Research Insight
Time-series analysis helps researchers explore inflation patterns, evaluate historical trends, and develop forecasts for economic research and decision-making.

🛠️ Software Expertise & Analytical Techniques
💻 Stata | 📊 ARMA Modeling | 📈 Box–Jenkins Method | 📉 ACF/PACF Analysis | 🧮 AIC/BIC Model Selection | 🔍 Residual Diagnostics | 🔮 Time-Series Forecasting

📌 Professional Services

📊 Statistical Data Analysis Guru

I provide professional statistical data analysis and research consultancy services for students, researchers, and businesses worldwide.

📊 Quantitative & Qualitative Data Analysis
🎓 Thesis, Dissertation & Research Paper Analysis
📈 Econometric Modeling & Time-Series Forecasting
📝 Statistical Reports, Interpretation & Research Writing
📚 Academic Projects, Assignments & Business Analytics
💻 Online Consultancy, Zoom Meetings & Training

🛠️ Software Expertise:
📊 RStudio | SPSS | Stata | EViews
🐍 Python | Gretl | Jamovi | Minitab
📗 Excel | JASP | AMOS | SmartPLS
🧠 NVivo | Dedoose | Atlas.ti
📈 Tableau | Power BI

🌍 Supporting clients across the UK, USA, Canada, Australia, Europe, China, and other regions.

📲 WhatsApp for Consultancy & Project Discussions:
📱 +92 305 6302785
📱 +92 317 7953330

📩 Contact me for professional data analysis and research support.