CCMAS Course Search

Sources of starch; Chemical composition and structure; Physico-chemical modification and degradation of starch; Enzymic degradation of starch; Fermentation products from starch; The technology of corn wet milling; Other products that can potentially be prepared from starch e.g; glucose syrups and flakes; fuels; alcohols; organic acids; penicillins; enzymes; amino acids and microbial polysaccharides

Uses of computers in statistical computing. Introduction to various statistical packages. Use of statistical packages in solving problems in statistics. spread sheet applications. Such as SPSS, STATA, MINITAB. can be used to demonstrate statistical methodology.

Population and samples. Asymptotics. Statistical models and methodologies. Random sampling distributions. Elementary time series analysis. Index numbers. Demographic measures. Estimation (point and interval) and tests of hypotheses concerning population mean and proportion (one and two sample cases). Regression and correlation. Programming in Python computer language. Computation of mean, variance and correlation. Sorting and ranking of data. Data Step Processing. Preparing Data for Analysis. Evaluating Quantitative Data. Sample Size Estimation. Basic statistical computing in regression analysis and the analysis of designed experiments. Introduction to Monte Carlo methods. Use of statistical packages like SPSS, SAS, Minitab, GENSTAT, EPI-INFO, SYSTAT. Lab work: Practical experiments on statistical models and methodologies. Practical exercises on random sampling distribution methods. Practicals on test of hypothesis, population, mean, proportion, regression and correlation analysis. Exercise on how to sort and data from different data set. Use of SPSS for data analysis and computation.

The design and use of existing statistical software; methods of simulation of random processes; numerical methods of fitting linear models; multivariate analysis; methods for nonlinear modeling; Introduction of key aspects of statistical consulting and data analysis activities; report writing and presentation

Conditioning; distribution theory; approximation to distributions; modes of convergence; limit theorems; statistical models; parameter estimation; properties of estimators; confidence sets; theory of hypothesis tests; introduction to Bayesian inference and nonparametric estimation

Population and samples. Random sampling distributions, estimation (point and interval) and tests of hypotheses concerning population mean and proportion (one and two large sample cases). Regression and correlation. Elementary time series analysis.

Population and samples. Random sampling distributions, estimation (Point and interval) and Tests of hypotheses concerning population mean and proportion (one and two large sample cases). Regression and correlation. Elementary time series analysis.

The nature of statistical methods, Frequency distributions, measures of central tendencies – Mean, Mode and Media, Measures of Dispersion – Range, Variance, Standard deviation, Elementary Probability Theory, Binomial, Normal and Poisson Distributions. Sources of statistical data in Nigeria. Tests of Hypotheses; small sample tests – t - test, x2 – test and F – test; Regression analysis; Analysis of Variance; Time series analysis; Index numbers; Sources of Statistical Data in Nigeria.

Index numbers: meaning and uses of index numbers, problems of construction index numbers, un-weighted index- simple aggregate index, mean of price relatives. Weighted index numbers – use of Laspeyre, Paasche, Fisher Ideal, Marshal Edgeworth. Correlation analysis: meaning of correlation, types of correlation (simple, partial and multiple correlation), nature of correlation (positive, zero and negative) meaning of correlation coefficient and its determination and interpretation with reference to simple correlation, partial correlation and multiple correlation. Spearman’s and Kendall Tau Rank Correlation Coefficient, Spearman’s Product Moment Correlation Coefficient. Coefficient of determination (meaning and interpretation) Regression analysis: meaning of regression, types of regression simple, multiple and exponential regression. Simple or linear regression, exponential regression, multiple regression and coefficient of determination. Time analysis: meaning of time series, basic component, methods of measuring trend (graphical, moving averages, least squares, semi- averages, method for determining seasonal indices (average percentage, moving averages link relative, ratio trend and smoothening. Probability: definition of probability, measurement (addition and multiplication laws applied to mutually exclusive, independent and conditional events) mathematical expectation, permutations and combinations, probability distribution – binomial, hyper geometric, multinomial, Poisson, normal and their applications in health information management. Estimate and significance testing. Define confidence interval, confidence interval for population mean and proportion based on large and small samples. Meaning of hypothesis, type I and type II error, level of significance, test concerning population means and proportions including small and large samples.

Role of statistics in Human Biology Medicine and Health Information Management. Analysis of variance: one-way, classification, test for equality of several variance, multiple range test; two- way, classification (single observation per cell) two – way classification (several observation in one cell). Test for independence, runs and sign test, Wilcoxon two-sample test, Wilcoxon test for paired observations, goodness of fit test, Kolmogrov-sminov one/two sample test, fisher exact probability test. The Friedman test, the Kruskal-walls test, the Mann-Witney test.