CCMAS Course Search
Browse BRIDGE's courses under the National Universities Commission's Core Curriculum Minimum Academic Standards (CCMAS) — Nigeria's unified benchmark curriculum for every accredited program. Search by course title, code, faculty or programme to see full descriptions, learning outlines and credit-hour loads.
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Faculty: Allied Health Sciences ×
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STA 201
2 Unit(s) (LH 30)
At the end of this course, students should be able to: 1. explain the scope for statistical methods in health science; 2. define the measures of location partition and dispersion; 3. explain the elements of probability,...
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Aims, characteristics and application of biostatistics in clinical and preventive medicine. Statistical
data in bio-medical science-samples, population, variables, frequency distribution, vital and
descriptive statistics, measurement of central tendencies-mean, median, mode, dispersion and
presentation of data probability distribution, Hypothetical tests of statistical significance. Analysis
of variance. Regression and correlation. Experimental designs and clinical trials.
300 Level
COM 315
2 Unit(s) (LH 30)
At the end of the course, students should be able to: 1. design a research proposal; 2. review and critique research papers in journals; 3. develop a research question, and identify appropriate designs to address researc...
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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.
PHS 202
2 Unit(s) (LH 30)
1 institution need this
At the end of the course, students should be able to: 1. demonstrate knowledge of interval estimation and hypothesis testing; 2. apply the correct statistical method to analyse one or more variables; 3. interpret statist...
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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.
PST 514
2 Unit(s) (LH 30)
At the end of the course, students should be able to: 1. discuss the importance of statistics to research; 2. describe scientific methods of collating and summarizing data; and 3. select and apply appropriate statistical...
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Importance and central role of statistics in research. Review – types of variables, scales of
measurement, concept of sampling, data collection. Reduction, summarisation 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 statistics such as Student-t test; Mann-Witney
U, ANOA, MANOVA, Regression Analysis
BIO 208
2 Unit(s) (LH 30)
1 institution need this
At the end of the lectures in this course, students should be able to: 1. differentiate between continuous and discontinuous data; 2. explain sampling procedures in biology; 3. summarise and present biological data; 4. d...
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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.
PHE 403
1 Unit(s) (LH 15)
At the end of this course, the students should be able to: 1. define basic statistical terminologies; 2. explain various types of statistics and their uses; 3. differentiate between descriptive, qualitative and quantitat...
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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.
BIO 208
2 Unit(s) (LH 30)
1 institution need this
At the end of the lectures in this course, students should be able to: 1. differentiate between continuous and discontinuous data; 2. explain sampling procedures in biology; 3. summarise and present biological data; 4. d...
View learning outline
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.
PHS 202
2 Unit(s) (LH 30)
At the end of the course, students should be able to: 1. demonstrate knowledge of interval estimation and hypothesis testing; 2. apply the correct statistical method to analyse one or more variables; 3. interpret statist...
View learning outline
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.
EHS 212
2 Unit(s) (LH 30)
At the end of the course, students should be able to: 1. define the following terms: Statistic, statistics and data; 2. enumerate the types of data and their classification; 3. illustrate measures of central tendency; 4....
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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.
DNT 413
2 Unit(s) (LH 30)
At the end of the course, students should be able to: 1. discuss the importance of statistics to research; 2. describe scientific methods of collating and summarizing data; and 3. select and apply appropriate statistical...
View learning outline
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