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Fundamentals of Biostatistics by Khan and Khanum.zip: A Comprehensive and Easy-to-Understand Book on Biostatistics


Download Fundamentals of Biostatistics by Khan and Khanum.zip




If you are looking for a comprehensive and easy-to-understand book on biostatistics, you might want to download Fundamentals of Biostatistics by Khan and Khanum.zip. This book is written by Irfan A. Khan and Atiya Khanum, who are both professors of biostatistics at Aligarh Muslim University in India. They have extensive experience in teaching and research in biostatistics and have authored several books and papers on the subject.




download fundamentals of biostatistics by khan and khanum.zip


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In this article, we will tell you what biostatistics is and why it is important, what are the main topics covered in Fundamentals of Biostatistics by Khan and Khanum, how to download the book from various sources, and how to unzip the file using different software. By the end of this article, you will have all the information you need to get your hands on this valuable resource.


What is biostatistics and why is it important?




Biostatistics is a branch of statistics that deals with the collection, analysis, interpretation, and presentation of data related to biological sciences, especially health and medicine. Biostatisticians use mathematical tools and methods to design experiments, conduct surveys, evaluate treatments, test hypotheses, estimate risks, compare groups, identify patterns, draw conclusions, and communicate results.


Biostatistics is important because it helps us understand how biological phenomena work, how diseases spread, how treatments work, how populations change, how genes affect traits, how environmental factors affect health outcomes, etc. Biostatistics also helps us make informed decisions based on evidence rather than intuition or opinion.


Some examples of applications of biostatistics are:


  • Epidemiology: the study of the distribution and determinants of diseases and health conditions in populations.



  • Clinical trials: the study of the effects and safety of new drugs, devices, procedures, or interventions on human subjects.



  • Genetics: the study of the inheritance and variation of genes and traits in organisms.



  • Bioinformatics: the study of the structure, function, and evolution of biological molecules and systems using computational tools and methods.



  • Public health: the study of the prevention and control of diseases and health problems in communities and populations.



What are the main topics covered in Fundamentals of Biostatistics by Khan and Khanum?




Fundamentals of Biostatistics by Khan and Khanum is a book that covers the basic concepts and methods of biostatistics as well as some advanced topics that are relevant for biomedical research. The book is divided into five parts, each containing several chapters. The book also includes numerous examples, exercises, tables, figures, and references to help the readers understand and apply the concepts and methods.


The following is a summary of the main topics covered in each part of the book:


Basic concepts and methods of biostatistics




This part introduces the fundamental concepts and methods of biostatistics, such as:


  • Descriptive statistics: how to summarize and display data using measures of central tendency, dispersion, skewness, kurtosis, etc.



  • Probability: how to calculate and interpret the likelihood of events using rules of probability, conditional probability, Bayes' theorem, etc.



  • Sampling: how to select a representative subset of a population using different types of sampling techniques, such as simple random sampling, stratified sampling, cluster sampling, etc.



  • Hypothesis testing: how to test whether a claim or assumption about a population parameter is true or false using different types of tests, such as z-test, t-test, F-test, ANOVA, etc.



  • Confidence intervals: how to estimate the range of values that contains a population parameter with a certain level of confidence using different types of intervals, such as z-interval, t-interval, etc.



  • Correlation and regression: how to measure and describe the relationship between two or more variables using different types of correlation coefficients, such as Pearson's r, Spearman's rho, etc., and different types of regression models, such as linear regression, multiple regression, etc.



Statistical analysis of epidemiological data




This part covers the statistical analysis of data from epidemiological studies, such as:


  • Measures of disease frequency: how to quantify the occurrence of diseases or health conditions in populations using different types of measures, such as incidence rate, prevalence rate, mortality rate, etc.



  • Measures of association: how to quantify the strength of association between exposure factors and disease outcomes using different types of measures, such as relative risk, odds ratio, attributable risk, etc.



  • Measures of impact: how to quantify the impact or effect of exposure factors on disease outcomes using different types of measures, such as population attributable risk, number needed to treat, number needed to harm, etc.



  • Confounding and interaction: how to identify and control for confounding factors that distort the true association between exposure factors and disease outcomes using different methods, such as stratification, standardization, adjustment, etc., and how to detect and interpret interaction effects that modify the association between exposure factors and disease outcomes using different methods, such as additive model, multiplicative model, etc.



  • Cohort studies: how to design and analyze observational studies that follow a group of individuals who share a common exposure factor over time and compare their disease outcomes with another group who do not share the exposure factor using different methods, such as prospective cohort study, retrospective cohort study, etc.



  • Case-control studies: how to design and analyze observational studies that select a group of individuals who have a disease outcome (cases) and compare their exposure factors with another group who do not have the disease outcome (controls) using different methods, such as matched case-control study, nested case-control study, etc.



Statistical analysis of clinical trials




This part covers the statistical analysis of data from clinical trials, such as:


  • Randomization: how to assign subjects to different treatment groups randomly using different methods, such as simple randomization, block randomization, stratified randomization, etc.



Blinding: how to prevent bias due to prior knowledge or expectations of subjects or investigators about the treatments using different methods, Statistical analysis of clinical trials




This part covers the statistical analysis of data from clinical trials, such as:


  • Randomization: how to assign subjects to different treatment groups randomly using different methods, such as simple randomization, block randomization, stratified randomization, etc.