CCMAS Course Search

Research topic. Sampling techniques. Design of questionnaires Data gathering. Hypothesis formulation. Probability distribution. Normal Poisson and Binomial distribution Mean. Standard error. Standard deviation. Curve fitting. Chi-square test. Student T test. F-distribution. Regression. Correlation. Quantitative and Qualitative research models. Advanced statistical analysis.

The course is planned to equip the undergraduates in all the disciplines of health sciences with the necessary tools and skills for collecting, analysing, interpreting data quantitatively. Topics to be covered include: The central role of statistics in health sciences disciplines, data description, elements of probability. Description of random variables. Applications of the binomial and normal distributions. Estimation and confidence intervals. Contingency tables. Regression and variance analysis. Study design and hypothesis testing for practical purposes. Students are provided with specific data to work on and are also required to develop simple questionnaire protocols for analysis.

Variability in biological data: continuous and discontinuous variables. statistical sampling procedures. observations and problems of estimation. representation and summarisation of biological data. frequency distribution. measures of central tendency and dispersion. Probability theory. normal, binomial and Poisson distribution. t-test, f-test and chi-square test. analysis of variance (ANOVA) and covariance. principles of experimental design. correlation, linear and curvilinear regression and transformation.

The course is planned to equip the undergraduates in all the disciplines of health sciences with the necessary tools and skills for collecting, analysing, interpreting data quantitatively. Topics to be covered include: The central role of statistics in health sciences disciplines, data description, elements of probability, description of random variables, applications of the binomial and normal distributions, estimation and confidence intervals, contingency tables, regression and variance analysis, study design and hypothesis testing. For practical purposes, students are provided with specific data to work on and are also required to develop simple questionnaire protocols for analysis.

Review of basic statistical terminologies. Discrete/categorical and continuous data. Descriptive/quantitative and qualitative data. Parameters and statistics. Parametric and non parametric statistics. Review of descriptive statistics. Inferential statistics. Errors – type 1 and type 11. Hypothesis testing. Level of significance. Degree of freedom. Inferences and conclusion from statistical analysis. Statistical packages (such as SPSS latest Ed and many others). Data organisation and interpretation - frequency distribution tables, graphs, charts, cumulative frequencies and curves. Data communication and data-informed decision making. The nurse’s role in data collection and data quality.

Variability in biological data: Continuous and discontinuous variables; statistical sampling procedures – observations and problems of estimation; Representation and summarisation of biological data; Frequency distribution; Measures of central tendency and dispersion; Probability theory; Normal, binomial and Poisson distribution; t-test, F-test and chi-square test, Analysis of variance (ANOVA) and covariance; Principles of experimental design; Correlation, linear and curvilinear regression; Transformation.

Definition, types of data, classification and types of samples, sampling techniques data distribution descriptive and inferential statistics. Vital and health statistics. Use of computer based statistical packages – such as statistical package for social sciences (SPSS), epi-Info, epidata, strata, R, stacia, and many others. and many others Calculate rates: Attack rate, infection rate, contamination rate, Death rate, Crude Death Rate and many others.

Importance and the central role of statistics in research. Review – types of variables, scales of measurement, the concept of sampling, data collection. Reduction, summarization, and presentation of data. Probability. The Normal distribution. Sampling methods. Tests of hypothesis (significant difference, correlation, regression, and many others). Parametric and non-parametric tests. Factors for considerations in choosing a particular statistic such as Student-t-test. Mann- Whitney U, ANOA, MANOVA, Regression Analysis

Research topic. Sampling techniques. Design of questionnaires Data gathering. Hypothesis formulation. Probability distribution. Normal Poisson and Binomial distribution Mean. Standard error. Standard deviation. Curve fitting. Chi-square test. Student T test. F-distribution. Regression. Correlation. Quantitative and Qualitative research models. Advanced statistical analysis.

Variability in biological data: continuous and discontinuous variables. statistical sampling procedures. observations and problems of estimation. representation and summarization of biological data. frequency distribution. measures of central tendency and dispersion. Probability theory. normal, binomial and Poisson distribution. t-test, f-test and chi-square test. analysis of variance (ANOVA) and covariance. principles of experimental design. correlation, linear and curvilinear regression and transformation. 300 Level Courses to be taught at this level should include: Neuroembryology, Neuroanatomy, Neurohistology, Histochemistry, Human genetics, light microscopic techniques, Biostatistics and Entrepreneurship.