Cardiac
- Munir Baragbah
- Married to Najahah Benyahya, and blessed with two children, Azim Danial Barakbah and Abyana Barakbah
Sep 30, 2026
Mastering the PhD Viva
Sep 29, 2026
20 Viva Voce Defense Questions
Sep 28, 2026
Form 4 PKSK Assessment
Sep 27, 2026
Kak Da Daughter's Wedding
congratulations to Kak Da and Abang Basri on welcoming a son-in-law into the family through the beautiful union of their daughter, Fathin Azizah and Muhammad Husaini... the wedding reception held at Dewan Astana Garden Glass, Level 3M, SkyPark Cyberjaya, was a joyous occasion filled with happiness, warmth, and cherished moments... it was especially meaningful to have the opportunity to meet Mak Chaq, the only surviving member of the late Mak’s family, making the gathering even more memorable and precious... the event also provided a wonderful occasion for family members and relatives to come together, reconnect, and celebrate this special milestone... to the newlyweds, Fathin Azizah and Muhammad Husaini, heartfelt congratulations and best wishes... may your marriage be blessed with love, understanding, patience, good health, happiness, and lasting companionship... may Allah SWT grant both of you a harmonious and prosperous life together, filled with countless blessings and beautiful memories in the years ahead...
Sep 26, 2026
Go Green: Together We Plant, Together We Grow
Sep 25, 2026
Mock Viva Voce
Sep 24, 2026
Business Ethics Tutorial
SPM Hifz Al-Quran Paper 2 Examination
Sep 23, 2026
Faculty Gotong-Royong Perdana
Sep 22, 2026
Saringan Akhir AKTAR Tilawah Remaja
Form 4 PKSK Assessment
Sep 21, 2026
SPM Maharat Al-Quran Oral Examination
Sep 20, 2026
History
Sep 19, 2026
R-Squared vs P-Value
R-Squared (R²) and P-value are 2 important statistical measures used in regression analysis, but they serve very different purposes... R-Squared, also known as the coefficient of determination, measures how well the independent variables explain the variation in the dependent variable... it indicates the proportion of variance in the outcome that can be predicted from the explanatory variables... for example, an R² value of 0.70 means that 70% of the variation in the dependent variable is explained by the model, while the remaining 30% is influenced by other factors not included in the analysis... consequently, a higher R² generally indicates a stronger explanatory power of the model, although it does not necessarily imply that the relationships are statistically significant or that the model is the best possible one...
the P-value is a measure of statistical significance... it helps researchers determine whether the relationship between variables is likely to be genuine or merely due to random chance... a commonly accepted threshold is 0.05, meaning that if the P-value is less than 0.05, the relationship is considered statistically significant... for example, if a predictor variable has a P-value of 0.001, there is strong evidence that the variable has a meaningful impact on the dependent variable... conversely, if the P-value exceeds 0.05, researchers typically conclude that there is insufficient evidence to support a significant relationship between the variables...
the key distinction is that R-Squared tells us how much of the variation is explained by the model, whereas the P-value tells us whether the observed relationship is statistically significant... a model may have a high R² but contain variables that are not statistically significant, indicating that the explanatory power is strong but some predictors may not contribute meaningfully... a model may have a low R² but significant P-values, suggesting that while the predictors significantly influence the outcome, they explain only a small portion of its total variability... therefore, researchers should evaluate both measures together rather than relying on either one in isolation.
for PhD Viva purposes, a simple explanation would be: "R-Squared answers the question, 'How much can my model explain?', while the P-value answers the question, 'Is the relationship statistically significant?' R-Squared measures explanatory power, whereas the P-value measures statistical evidence."
example:
suppose you are studying the effect of employee training on employee performance:
- R² = 0.65
- training explains 65% of the variation in employee performance
- P-value = 0.002
- The effect of training on performance is statistically significant
interpretation: training significantly influences employee performance, and the model explains a substantial proportion of performance differences among employees.
viva voce question: your model reports an R² of 0.65 and a P-value of 0.002... what does this mean?
suggested answer: the R² value of 0.65 indicates that 65% of the variance in the dependent variable is explained by the independent variables included in the model... meanwhile, the P-value of 0.002 is below the 0.05 threshold, indicating that the relationship is statistically significant and unlikely to have occurred by chance... therefore, the model demonstrates both explanatory power and statistical significance, although 35% of the variance remains influenced by factors not included in the study...
Sep 18, 2026
Skewness vs Kurtosis in Data A
- skewness = shape and symmetry of the distribution
- kurtosis = peakedness and likelihood of extreme values (outliers)
Sep 17, 2026
Sampling Methods
Sep 16, 2026
Research Gaps
identifying a research gap is a critical step in developing a meaningful and impactful study, as it helps researchers establish the originality and significance of their work... a knowledge gap exists when there is insufficient understanding of a particular phenomenon, concept, or issue, requiring further investigation to expand existing knowledge... an evidence gap occurs when available findings are limited, inconsistent, or inconclusive, making it difficult to draw definitive conclusions... a methodological gap arises when previous studies have relied heavily on certain research methods while neglecting alternative approaches that may offer deeper insights or more robust findings... similarly, an empirical gap exists when research lacks sufficient data or observations to support theoretical assumptions or explain real-world situations adequately...
a practical knowledge gap refers to situations where academic findings have not been effectively translated into practice, leaving organizations, industries, or policymakers without clear guidance for implementation... a theoretical gap emerges when existing theories fail to fully explain a phenomenon or when relationships between variables remain unclear, creating opportunities for theory development, extension, or integration... a population gap occurs when prior studies have focused on specific groups, geographical locations, or demographic segments while overlooking other populations that may exhibit different characteristics or behaviors... finally, a data gap exists when relevant, reliable, or up-to-date data are unavailable, limited, or inaccessible, restricting the ability to conduct comprehensive analyses... understanding these different types of research gaps enables researchers to position their studies effectively, justify the need for further investigation, and demonstrate how their work contributes to advancing knowledge, theory, methodology, and practice within their respective fields...




































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