{"id":68,"date":"2020-08-31T11:57:51","date_gmt":"2020-08-31T06:27:51","guid":{"rendered":"http:\/\/maths.jfn.ac.lk\/?page_id=68"},"modified":"2020-09-29T14:38:11","modified_gmt":"2020-09-29T09:08:11","slug":"statistics","status":"publish","type":"page","link":"https:\/\/maths.jfn.ac.lk\/index.php\/statistics\/","title":{"rendered":"Statistics"},"content":{"rendered":"

Level \u2013 1<\/h3>\n

Course units effective from academic year 2016\/2017 to date<\/h4>\n
\n
<\/span>STA101G3: Probability Theory <\/div>
\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Course Code<\/strong><\/td>\nSTA101G3<\/strong><\/td>\n<\/tr>\n
Course Title<\/strong><\/td>\nProbability Theory<\/td>\n<\/tr>\n
Academic Credits<\/strong><\/td>\n03 (45 hours of lectures and tutorials)<\/td>\n<\/tr>\n
Objectives:<\/strong><\/td>\n<\/tr>\n
\n
    \n
  • Provide an introduction to the probability theory<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
\n
    \n
  • Demonstrate the concepts probability and conditional probability<\/li>\n
  • Apply tree Diagrams for the problems that involve conditional probability and Bayes\u2019 theorem<\/li>\n
  • Define random variables<\/li>\n
  • Apply the knowledge of basic probability distributions in real world issues<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Syllabus Outline<\/strong><\/td>\n<\/tr>\n
Contents: <\/strong><\/td>\n<\/tr>\n
\n
    \n
  • Introduction to Probability: <\/strong>Permutations, combinations, Venn diagram, events, sample space, equally likely events, mutually exclusive events, axioms of probability, laws of probability, conditional probability, independence.<\/li>\n
  • Bayes\u2019 Theorem and Applications: <\/strong>Partition, total probability theorem, Bayes\u2019 theorem, tree diagram.<\/li>\n
  • Random Variable: <\/strong>Discrete and continuous random variables, probability mass function, probability density function, expectation, moments, mean and variance, moment generating functions, probability generating functions.<\/li>\n
  • Probability Distributions: <\/strong>Discrete uniform, Bernoulli, binomial, Poisson, geometric, uniform, exponential and normal distributions, applications of the normal distribution,<\/li>\n<\/ul>\n

    sampling distribution of the sample means.<\/p>\n

      \n
    • Joint Distributions: <\/strong>Joint distributions, marginal distribution, conditional distributions, conditional expectation and variance.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Teaching Methods:<\/strong><\/td>\n<\/tr>\n
\n
    \n
  • Lecture demonstration by Lecturer and Tutorial discussions by Instructors<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
\n
    \n
  • In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 30%<\/li>\n
  • End of course Examination\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Recommended Readings:<\/strong><\/td>\n<\/tr>\n
\n
    \n
  • Fundamentals of Probability with Stochastic Processes, Saeed Ghahramani, 3rd<\/sup> Edition, 2005.<\/li>\n
  • Probability and Statistics for Engineers and Scientists, Walpole R.E., Myers R.H., Myers S.L., Ye K.E., 9th<\/sup> Edition, 2010.<\/li>\n
  • Schaum\u2019s outline of Statistics, Murray R. Spiegel, 5th<\/sup> Edition, 2014.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
    <\/span>STA102G2<\/strong>: Introduction to Statistics<\/div>
    \n
    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Course Code<\/strong><\/td>\nSTA102G2<\/strong><\/td>\n<\/tr>\n
    Course Title<\/strong><\/td>\nIntroduction to Statistics<\/td>\n<\/tr>\n
    Academic Credits<\/strong><\/td>\n02 (30 hours of lectures and tutorials)<\/td>\n<\/tr>\n
    Objectives:<\/strong><\/td>\n<\/tr>\n
    \n
      \n
    • Provide fundamental knowledge in basic statistical concepts<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
    Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
    \n
      \n
    • Explain basic concepts and principles of statistics<\/li>\n
    • \u00a0Examine data sets using summary statistics and graphical methods<\/li>\n
    • Apply simple random sampling method in real-world issues<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
    Syllabus Outline<\/strong><\/td>\n<\/tr>\n
    Contents:<\/strong><\/td>\n<\/tr>\n
    \n
      \n
    • Fundamentals of Statistics: <\/strong>Types of data, population and sample, descriptive and inferential statistics, parameter and statistic, data collection methods.<\/li>\n
    • Descriptive Statistics: <\/strong>Frequency distribution, pie chart, bar chart, histogram, ogive, frequency polygon and curve, measures of central tendency and dispersion, measures of relative standings, skewness and kurtosis, box-plot, five number summary statistics, outliers.<\/li>\n
    • Introduction to Sampling Methods: <\/strong>Introduction to sampling; sampling unit, sampling frame, sampling and non-sampling errors, probability and non-probability sampling. simple random sampling; estimation of mean, total and proportions and its variance in samples from finite population, calculation of sample size.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
    Teaching Methods:<\/strong><\/td>\n<\/tr>\n
    \n
      \n
    • \u00a0Lecture demonstration by Lecturer, Tutorial discussions and laboratory practical by Instructors<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
    Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
    \n
      \n
    • In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 30%<\/li>\n
    • End of course Examination\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
    Recommended Readings:<\/strong><\/td>\n<\/tr>\n
    \n
      \n
    • Introduction to the Practice of Statistics, Moore, McCabe, Craig, 6th<\/sup> Edition, 2009.<\/li>\n
    • Exploratory Data Analysis in Business and Economics, Thomas Cleff, 2014.<\/li>\n
    • Statistical Methods, Rudolf J. Freund, William J. Wilson, 2nd<\/sup> Edition, 2003.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
      <\/span>STA103G3: Basic Statistical Inference<\/div>
      \n
      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      Course Code<\/strong><\/td>\nSTA103G3<\/strong><\/td>\n<\/tr>\n
      Course Title<\/strong><\/td>\nBasic Statistical Inference<\/td>\n<\/tr>\n
      Academic Credits<\/strong><\/td>\n03 (45 hours of lectures and tutorials)<\/td>\n<\/tr>\n
      Objectives:<\/strong><\/td>\n<\/tr>\n
      \n
        \n
      • Provide fundamental knowledge in Inferential Statistics<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
      Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
      \n
        \n
      • Demonstrate the concept of Inferential Statistics<\/li>\n
      • Develop the knowledge in different sampling distributions<\/li>\n
      • Outline the different methods of parameter estimation in Statistics and interpret confidence intervals<\/li>\n
      • Explain the principles of hypothesis testing with applications<\/li>\n
      • Determine alternative statistical methods when normality assumption is not met<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
      Syllabus Outline<\/strong><\/td>\n<\/tr>\n
      Contents:<\/strong><\/td>\n<\/tr>\n
      \n
        \n
      • Introduction: <\/strong>Define: population; sample; parameter; and statistic, distinguish between descriptive statistics and inferential statistics.<\/li>\n
      • Sampling Distributions: <\/strong>Distributions of sample means, sample variances and sample proportions, unbiasedness, normal distribution, central limit theorem, theory of Student- t<\/em>, , and F<\/em> distributions.<\/li>\n
      • Point and Interval Estimation: <\/strong>Method of moments, maximum likelihood estimation, confidence intervals for one-sample, two-sample population characteristics, sample size calculation for parameter estimation, interpretation of confidence intervals.<\/li>\n
      • Testing Hypotheses: <\/strong>Steps in hypothesis testing, level of significance, Type-I and Type \u2013 II errors, p-value, power of test, Z-test, t<\/em> \u2013test,test, and F<\/em>-test, goodness of fit test, sample size calculation for hypothesis testing.<\/li>\n
      • Non-parametric Tests: <\/strong>Sign test, Wilcoxon Signed-Rank test, Wilcoxon Rank-Sum test (Mann-Whitney U test), contingency tables.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
      Teaching Methods:<\/strong><\/td>\n<\/tr>\n
      \n
        \n
      • Lecture demonstration by Lecturer, Tutorial discussions and, Laboratory practical by Instructors<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
      Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
      \n
        \n
      • In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0 30%<\/li>\n
      • End of course Examination\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
      Recommended Readings:<\/strong><\/td>\n<\/tr>\n
      \n
        \n
      • Schaum\u2019s outline of Statistics, Murray R. Spiegel, 5th<\/sup> Edition, 2014.<\/li>\n
      • Probability and Statistics for Engineers and Scientists, Walpole R.E., Myers R.H., Myers S.L., Ye K.E., 9th<\/sup> Edition, 2010.<\/li>\n
      • Applied Statistical Inference with Minitab, Sally A. Lesik, 2009.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
        <\/span>STA104G2: Applied Statistics I<\/div>
        \n
        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
        Course Code<\/strong><\/td>\nSTA104G2<\/strong><\/td>\n<\/tr>\n
        Course Title<\/strong><\/td>\nApplied Statistics I<\/td>\n<\/tr>\n
        Academic Credits<\/strong><\/td>\n02 (30 hours of lectures and tutorials)<\/td>\n<\/tr>\n
        Objectives:<\/strong><\/td>\n<\/tr>\n
        \n
          \n
        • Provide knowledge in the application of statistics<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
        Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
        \n
          \n
        • Describe correlation between the two variables and develop simple linear regression models<\/li>\n
        • Identify components of time series and apply basic time series models for forecasting<\/li>\n
        • Construct index numbers for real world issues<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
        Syllabus Outline<\/strong><\/td>\n<\/tr>\n
        Contents:<\/strong><\/td>\n<\/tr>\n
        \n
          \n
        • Correlation and Regression: <\/strong>Correlation, simple linear regression, least square estimation, interpretation of regression parameters.<\/li>\n
        • Time Series Analysis: <\/strong>Construction of time series plots and interpretation, components of time series, decomposition of time series components, additive and multiplicative models, moving average, exponential smoothing, forecasting.<\/li>\n
        • Index Numbers: <\/strong>Simple and weighted averages of price relative indices, construction of Paache, Laspeyres and Fisher indices, consumer price index, applications of index numbers.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
        Teaching Methods:<\/strong><\/td>\n<\/tr>\n
        \n
          \n
        • \u00a0Lecture demonstration by lecturer, Tutorial discussions and Laboratory practical by Instructors.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
        Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
        \n
          \n
        • \u00a0In-course assessments\u00a0 \u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a030%<\/li>\n
        • End of course Examination \u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
        Recommended Readings:<\/strong><\/td>\n<\/tr>\n
        \n
          \n
        • Applied Regression Analysis: A Research Tool, Rawlings, J. O., Wadsworth. 1988.<\/li>\n
        • \u00a0Linear regression analysis: Theory and computing, Yan, X., Su, X., & World Scientific (Firm). Singapore: World Scientific Pub. Co., 2009.<\/li>\n
        • Business Statistics: For Contemporary Decision Making Ken Black, 9th Edition: For Contemporary Decision Making: Wiley Global Education, 2009.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n<\/div>\n

          Level – 2<\/h3>\n

          Course units effective from academic year 2016\/2017 to date<\/h4>\n
          \n
          <\/span>STA201G3: Statistical Theory <\/div>
          \n
          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
          Course Code<\/strong><\/td>\nSTA201G3<\/strong><\/td>\n<\/tr>\n
          Course Title<\/strong><\/td>\nStatistical Theory<\/strong><\/td>\n<\/tr>\n
          Credit Value<\/strong><\/td>\n03 <\/strong><\/td>\n<\/tr>\n
          \u00a0<\/strong><\/p>\n

          Hourly Breakdown<\/strong><\/td>\n

          		Theory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
          45<\/strong><\/td>\n—<\/strong><\/td>\n105<\/strong><\/td>\n<\/tr>\n
          Objectives:<\/strong><\/p>\n

          Provide a sound knowledge in general theory of statistical distributions and its applications<\/em><\/td>\n<\/tr>\n

          Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
          \n
            \n
          • \u00a0Recall fundamental knowledge of probability concepts<\/li>\n
          • Apply theory of various discrete and continuous distributions in real world issues<\/li>\n
          • Determine moments of random variables using the knowledge of generating functions and characteristic functions<\/li>\n
          • Derive probability distributions of function of random variables using transformation technique<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
          Course Contents:<\/strong><\/td>\n<\/tr>\n
          Fundamentals of Statistics: <\/strong>Random variable, Probability function, Cumulative distribution function, Mathematical expectation and variance.<\/p>\n

          Probability Distributions: <\/strong>Discrete Distributions: Uniform, Bernoulli and Binomial, Poisson, Poisson approximation to Binomial, Geometric, Negative binomial, Poisson approximation to Negative binomial, Hypergeometric, Binomial approximation to Hypergeometric, Multinomial distribution.<\/p>\n

          Continuous Distributions:<\/strong> Uniform, Exponential,\u00a0 Normal, Gamma ,\u00a0 Beta , Chi-Square, Weibull, Lognormal, Student-t, F distribution, Cauchy.<\/p>\n

          Functions of Random variables: <\/strong>Probability generating function, Moment generating function, Cumulant generating function,\u00a0 Characteristic function , Convolution, Distributions of functions of random variables<\/p>\n

          Joint Distributions: <\/strong>Joint Distribution, Marginal Distribution, Conditional Distribution, Conditional Expectation and Variance, Independence, Correlation, Bivariate Normal distribution, Transformations of random variables, Order statistics.<\/td>\n<\/tr>\n

          Teaching Methods: <\/strong><\/td>\n<\/tr>\n
          Lecture demonstration and Tutorial discussions<\/td>\n<\/tr>\n
          Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
          \n
            \n
          • In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 30%<\/li>\n
          • End of course Examination\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
          Recommended Readings:<\/strong><\/td>\n<\/tr>\n
          \n
            \n
          • R.E.Walpole, R.H.Myers, S.L.Myers, K.E.Ye, \u201cProbability and Statistics for Engineers and Scientists\u201d, 9th<\/sup> Edition, 2010.<\/li>\n
          • John E. Freund, \u201cMathematical Statistics with applications\u201d, 8th<\/sup> edition,2014.<\/li>\n
          • R.V. Hogg &A.T.Craig, \u201cIntroduction to Mathematical Statistics\u201d, 4th<\/sup>edition, 1978.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
            <\/span>STA202G2: Sampling Techniques<\/div>
            \n
            \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
            Course Code<\/strong><\/td>\nSTA202G2<\/strong><\/td>\n<\/tr>\n
            Course Title<\/strong><\/td>\nSampling Techniques<\/strong><\/td>\n<\/tr>\n
            Credit Value<\/strong><\/td>\n02 <\/strong><\/td>\n<\/tr>\n
            Prerequisite:<\/strong> STA102G2<\/td>\n<\/tr>\n
            \u00a0<\/strong><\/p>\n

            Hourly Breakdown<\/strong><\/td>\n

            		Theory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
            30<\/strong><\/td>\n—<\/strong><\/td>\n70<\/strong><\/td>\n<\/tr>\n
            Objectives:<\/strong><\/td>\n<\/tr>\n
            \n
              \n
            • Introduce the concept of methods of sampling<\/li>\n
            • Gain knowledge in ratio and regression estimators<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
            Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
            \n
              \n
            • Illustrate the properties of stratification, stratified random sampling, proportional allocation and optimum allocation<\/li>\n
            • Apply the stratified random sampling in real world data<\/li>\n
            • Discuss the ratio and regression estimators<\/li>\n
            • Utilize the ratio and regression methods for estimating population parameters<\/li>\n
            • Discuss the concept of systematic sampling and cluster sampling<\/li>\n
            • Apply the systematic sampling and cluster sampling in real world data<\/li>\n
            • Evaluate the efficiency of estimators<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
            Course Contents:<\/strong><\/td>\n<\/tr>\n
            Preliminaries:<\/strong> simple random sampling: with and without replacement<\/p>\n

            Stratified Sampling: <\/strong>\u00a0stratification, advantages and disadvantages, stratified random sampling with associated mathematical background, estimation of mean, total and proportions and its variance in samples from finite population, <\/strong>confidence interval of estimators, <\/strong>models for cost function, <\/strong>allocation of sample size: proportional allocation, optimum allocation, Neyman allocation<\/p>\n

            Ratio and Regression methods:<\/strong> estimation of population ratio in simple random sampling and stratified random sampling, ratio and regression estimators of population mean and total in simple random sampling and stratified random sampling, variance, bias and mean square error of estimators, confidence interval of estimators, efficiency of estimators<\/p>\n

            Systematic sampling:<\/strong>\u00a0 design of systematic sampling, estimation of mean, total and its variance in sample from finite population, inter class correlation coefficient<\/p>\n

            Cluster sampling:<\/strong> Introduction of cluster sampling, estimation of mean, total and its variance in sample from finite population<\/td>\n<\/tr>\n

            Teaching Methods:<\/strong><\/td>\n<\/tr>\n
            \n
              \n
            • Lecture demonstration and tutorial discussions<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
            Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
            \n
              \n
            • \u00a0In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 30%<\/li>\n
            • End of course Examination \u00a070%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
            Recommended Readings:<\/strong><\/td>\n<\/tr>\n
            \n
              \n
            • William G. Cochran,\u00a0 Sampling Techniques, Third Edition, 2008.<\/li>\n
            • Sharon L. Lohr, Sampling: Design and Analysis, Second Edition, 2010.<\/li>\n
            • \u00a0R. Lyman Ott, Elements Survey Sampling. Sixth Edition,2006.<\/li>\n
            • S.R.S Rao, Sampling methodologies with Application, 2000.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
              <\/span>STA203G3: Design and Analysis of Experiments <\/div>
              \n
              \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
              Course Code<\/strong><\/td>\nSTA203G3<\/strong><\/td>\n<\/tr>\n
              Course Title<\/strong><\/td>\nDesign and Analysis of Experiments<\/strong><\/td>\n<\/tr>\n
              Credit Value<\/strong><\/td>\n03<\/strong><\/td>\n<\/tr>\n
              \u00a0<\/strong><\/p>\n

              Hourly Breakdown<\/strong><\/td>\n

              		Theory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
              40<\/strong><\/td>\n10<\/strong><\/td>\n100<\/strong><\/td>\n<\/tr>\n
              Objectives:<\/strong><\/td>\n<\/tr>\n
              Provide an introduction to the design and analysis of statistical experiments<\/td>\n<\/tr>\n
              Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
              \n
                \n
              • Analyze the experimental data<\/li>\n
              • Interpret the results of a statistical experiment<\/li>\n
              • Explain the difference between the experimental designs<\/li>\n
              • \u00a0Examine the suitability of the models for different experimental situations<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
              Course Contents:<\/strong><\/td>\n<\/tr>\n
              Analysis of Variance: <\/strong>Observational and Experimental studies, Factor, Levels, Treatment, Experimental unit, Randomization, Replication. Models and assumptions, fixed and random effect models, Decomposing the variance, One-way classification, Degrees of freedom, F-test, ANOVA table, Model adequacy checking, Further analysis: LSD method, Tuckey\u2019s method, Duncan\u2019s multiple range method.<\/p>\n

              Factorial Experiments: <\/strong>Two-way classification, Interaction, Diagrammatic explanation of interaction, Three-way classification, fixed, random and mixed models, Completely Randomized Design(CRD), Randomized Complete Block Design(RCBD), Latin Square Design(LSD), Nested Design, Nested and Crossed Design, Split Plot Design.<\/td>\n<\/tr>\n

              Teaching Methods:<\/strong><\/td>\n<\/tr>\n
              Lecture demonstration, Tutorial discussions and laboratory practical.<\/td>\n<\/tr>\n
              Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
              \n
                \n
              • In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 30%<\/li>\n
              • End of course Examination\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
              Recommended Readings:<\/strong><\/td>\n<\/tr>\n
              \n
                \n
              • Douglas C. Montgomery, Design and Analysis of Experiments, Wiley WSeries, 2012.<\/li>\n
              • H.R. Lindman, Analysis of Variance in Experimental Design, Springer Series, 1992.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
                <\/span>STA204G2: Statistical Inference<\/div>
                \n
                \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                Course Code<\/strong><\/td>\nSTA204G2<\/strong><\/td>\n<\/tr>\n
                Course Title<\/strong><\/td>\nStatistical Inference<\/strong><\/td>\n<\/tr>\n
                Credit Value<\/strong><\/td>\n02 <\/strong><\/td>\n<\/tr>\n
                Prerequisite:<\/strong> STA103G3 and STA201G3<\/strong><\/td>\n<\/tr>\n
                \u00a0<\/strong><\/p>\n

                Hourly Breakdown<\/strong><\/td>\n

                		Theory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
                30<\/strong><\/td>\n—<\/strong><\/td>\n70<\/strong><\/td>\n<\/tr>\n
                Objectives:<\/strong><\/td>\n<\/tr>\n
                \n
                  \n
                • Introduce the fundamental concepts of statistical inference<\/li>\n
                • Acquire and apply the Bayesian inferential procedure in rigorous way<\/li>\n
                • Enable to solve the statistical inference problems<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
                \n
                  \n
                • Determine the sufficiency and minimal sufficiency<\/li>\n
                • Apply the factorization criterion to find the sufficient statistic<\/li>\n
                • Determine whether a distribution belongs to an exponential family<\/li>\n
                • Derive the methods of moment estimator and the maximum likelihood estimator<\/li>\n
                • Apply the ideas of bias, unbiased and minimum variance unbiased estimators<\/li>\n
                • Prove the Rao- Blackwell theorem and Cramer-Rao inequality<\/li>\n
                • Evaluate Cramer-Rao lower bounds<\/li>\n
                • Utilize the estimation criteria to select the best estimators<\/li>\n
                • Determine the asymptotic distributions of given estimators<\/li>\n
                • Recall the simple and composite hypotheses and confidence intervals<\/li>\n
                • Apply Neyman-Pearson lemma to find the most powerful and uniformly most powerful tests<\/li>\n
                • Utilize likelihood ratio tests and maximum likelihood ratio tests to determine critical regions<\/li>\n
                • Formulate the Bayesian analysis for a range of standard statistical problems<\/li>\n
                • Distinguish classical and Bayesian inferential paradigms<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                Course Contents:<\/strong><\/td>\n<\/tr>\n
                \n
                  \n
                • Sufficiency principle: <\/strong>Sufficient statistics and factorization criterion, minimal sufficient statistics, exponential families of distributions, complete sufficient statistics.<\/li>\n<\/ul>\n
                    \n
                  • Point estimation: <\/strong>Methods of estimation;method of moment, maximum likelihood. Estimation criteria; bias, mean squared error, unbiasedness, relative efficiency, minimum variance unbiased estimators, Rao- Blackwell theorem, Cramer-Rao lower bound, efficiency, consistency.Asymptotic behaviour of MLEs.<\/li>\n<\/ul>\n
                      \n
                    • Interval estimation:<\/strong>Methods of estimation; inverting test statistics, pivots and approximate maximum likelihood.<\/li>\n<\/ul>\n
                        \n
                      • Hypothesis testing: –<\/strong>Simple and composite hypotheses, types of error, power, most powerful tests, uniformly most powerful tests, Neyman-Pearson lemma, likelihood ratio tests, maximumlikelihood ratio tests, sequential analysis, sequential likelihood ratio tests.<\/li>\n
                      • Introduction to Bayesian theory: <\/strong>Prior and posterior distributions; Bayesian estimators, tests and intervals for parameters.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                Teaching Methods:<\/strong><\/td>\n<\/tr>\n
                \n
                  \n
                • Teaching methods consist of lectures and tutorial exercises.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
                \n
                  \n
                • In-course Assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 30%<\/li>\n
                • End-of-course Examination 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                Recommended Readings:<\/strong><\/td>\n<\/tr>\n
                \n
                  \n
                • \u00a0G. Casella, and R.L.Berger, Statistical Inference. 2nd ed. Belmont, CA: Duxbury Press, 2001.<\/li>\n
                • \u00a0R.V. Hoggand E.A.Tanis, Probability and Statistical Inference, 5th ed. Upper Saddle River, NJ: Prentice Hall, 1997.<\/li>\n
                • \u00a0R.V. Hogg, J.W.McKean, and A.T.Craig, Introduction to Mathematical Statistics, 6th ed. Upper Saddle River, NJ: Pearson Prentice Hall, 2005.<\/li>\n
                • \u00a0D.D.Wackerly, W. Mendenhall, and R.L.Scheaffer, Mathematical Statistics with Applications, 5th ed. Belmont, CA: Duxbury Press, 1996.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n<\/div>\n

                  Level \u2013 3<\/h3>\n

                  Course units effective from academic year 2016\/2017 to date<\/h4>\n
                  \n
                  <\/span>STA301G3: Regression Analysis <\/div>
                  \n
                  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                  Course Code<\/strong><\/td>\nSTA301G3<\/strong><\/td>\n<\/tr>\n
                  Course Title<\/strong><\/td>\nRegression Analysis<\/strong><\/td>\n<\/tr>\n
                  Credit Value<\/strong><\/td>\n03 <\/strong><\/td>\n<\/tr>\n
                  Prerequisite<\/strong><\/td>\nSTA103G3 <\/strong><\/td>\n<\/tr>\n
                  Hourly Breakdown <\/strong><\/td>\nTheory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
                  40 <\/strong><\/td>\n10 <\/strong><\/td>\n100 <\/strong><\/td>\n<\/tr>\n
                  Objective:<\/strong><\/p>\n

                  Provide knowledge and techniques in fitting regression models to real world data <\/em><\/td>\n<\/tr>\n

                  Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
                  \n
                    \n
                  • Compare deterministic and stochastic relationships<\/li>\n
                  • Distinguish \u00a0linear and nonlinear models<\/li>\n
                  • Estimate the parameters of \u00a0linear regression models<\/li>\n
                  • \u00a0Construct Analysis of Variance table to make inference about linear regression model<\/li>\n
                  • \u00a0Apply diagnostic checks to identify possible violations of the model assumptions<\/li>\n
                  • Choose the best fitting model for prediction<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                  Course Contents:<\/strong><\/td>\n<\/tr>\n
                  \n
                    \n
                  • Introduction:\u00a0<\/strong>Response variable, Explanatory variable, deterministic relationship, stochastic\/probabilistic relationship, scatter plot, linear and non-linear relationship, correlation coefficient<\/li>\n
                  • Simple Linear Regression:\u00a0<\/strong>Simple linear regression model, Assumptions of Simple linear regression, Linear and non-linear models, Estimation of parameters: Least squares and Maximum likelihood estimation, Properties of least square estimators, Statistical Inference on regression coefficients, Diagnostic checking of simple linear regression model and possible remedies, measure of goodness of fit, prediction, Analysis of Variance approach, Lack of fit, simple linear regression model using matrix approach, Polynomial regression models.<\/li>\n
                  • Multiple Linear Regression:\u00a0<\/strong>Multiple linear regression model\u00a0 and its parameter estimation, Assumptions of multiple regression model, Matrix approach of multiple linear regression , Analysis of variance approach, Use of Dummy variables, Choice of variables,\u00a0 Transformation of variables, Gauss Markov Theorem,\u00a0 Sequential and partial regression sum of squares; Regression residuals diagnostics and model selection procedures, Prediction, Multicollinearity and its impacts .<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                  Teaching Methods: <\/strong><\/td>\n<\/tr>\n
                  Lecture demonstration,\u00a0 Tutorial discussions, and Laboratory practical<\/td>\n<\/tr>\n
                  Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
                  \n
                    \n
                  • \u00a0In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0 30%<\/li>\n
                  • End of course Examination\u00a0\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                  Recommended Readings:<\/strong><\/td>\n<\/tr>\n
                  \n
                    \n
                  • Norman R. Draper, Harry Smith., \u201cApplied Regression Analysis\u201d, 3rd<\/sup> Edition, Willey, 1998.<\/li>\n
                  • Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining .,\u201cIntroduction to Linear Regression Analysis\u201d, 5th<\/sup> Edition, Willey, 2013.<\/li>\n
                  • C.R. Rao, H. Toutenburg, Shalabh, and C. Heumann.,\u201d Linear Models and Generalizations – Least Squares and Alternatives\u201d, Springer, 2008.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
                    <\/span>STA302G3: Stochastic Processes<\/div>
                    \n
                    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                    Course Code<\/strong><\/td>\nSTA302G3<\/strong><\/td>\n<\/tr>\n
                    Course Title<\/strong><\/td>\nStochastic Processes<\/strong><\/td>\n<\/tr>\n
                    Credit Value<\/strong><\/td>\n03<\/strong><\/td>\n<\/tr>\n
                    Prerequisite\u00a0\u00a0\u00a0 <\/strong><\/td>\nSTA101G3 and STA201G3<\/strong><\/td>\n<\/tr>\n
                    Hourly Breakdown<\/strong><\/td>\nTheory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
                    \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 45 Hours<\/strong><\/td>\n_<\/strong><\/td>\n105 Hours<\/strong><\/td>\n<\/tr>\n
                    Objectives:<\/strong><\/td>\n<\/tr>\n
                    \n
                      \n
                    • \u00a0Introduce basic concepts and theory of stochastic processes<\/li>\n
                    • Familiarize with standard stochastic processes and their properties<\/li>\n
                    • Acquaint with stochastic modelling and applications<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                    Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
                    \n
                      \n
                    • Explain the basic characteristics of stochastic processes<\/li>\n
                    • Evaluate the important properties of random variables<\/li>\n
                    • \u00a0Apply relevant stochastic models for randomly varying dynamic systems<\/li>\n
                    • \u00a0Determine the waiting time and confidence interval for mean of Poisson processes<\/li>\n
                    • Test for the Markov property<\/li>\n
                    • Apply Chapman-Kolmogorov equation to determine the transition probabilities<\/li>\n
                    • Find the stationary distribution of a Markov chain<\/li>\n
                    • Classify the states of a Markov chain<\/li>\n
                    • Evaluate the probability distribution of a random walk<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                    Course Contents<\/strong>:<\/strong><\/td>\n<\/tr>\n
                    \n
                      \n
                    • Introduction – <\/strong>Basic properties and examples of stochastic processes, Stationary process, Independent increments, Expectation and Covariance functions.<\/li>\n
                    • Standard Stochastic Processes – <\/strong>The Bernoulli, Normal and the Wiener Processes, Counting Processes; Poisson process, Non-homogeneous, Generalized and Compound Poisson processes. Inter-arrival times and waiting times distributions. Filtered Poisson processes, Renewal processes.<\/li>\n
                    • Markov Processes – <\/strong>Markov property, Markov chains, Transition probability matrices, Chapman-Kolmogorov equation, Classification of states, Decomposition of Markov chains, Stationary distribution, Limiting distribution.<\/li>\n
                    • Random walk – <\/strong>Unrestricted random walk, Symmetric random walk.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                    Teaching Methods:<\/strong><\/td>\n<\/tr>\n
                    \n
                      \n
                    • Lectures, Tutorials, Handouts, Problem solving, e-resources<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                    Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
                    \n
                      \n
                    • In-course Assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 30%<\/li>\n
                    • End-of-course Examination\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                    Recommended Readings:<\/strong><\/td>\n<\/tr>\n
                    \n
                      \n
                    • E. Parzen, \u201cStochastic Processes\u201d, SIAM Edition; Society for Industrial and Applied Mathematics Philadelphia, 1999.<\/li>\n
                    • \u00a0Sheldon M. Ross, \u201cIntroduction to Probability Models\u201d, 10th<\/sup> edition, Academic Press Elsevier, 2013.<\/li>\n
                    • P. W. Jones & P. Smith, \u201cStochastic Processes An Introduction\u201d, 1st<\/sup> \u00a0edition, Oxford University Press Inc, New York, 2001.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
                      <\/span>STA303G2: Quality Control<\/div>
                      \n
                      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                      Course Code<\/strong><\/td>\nQuality Control<\/strong><\/td>\n<\/tr>\n
                      Course Title<\/strong><\/td>\nSTA303G2<\/strong><\/td>\n<\/tr>\n
                      Credit Value<\/strong><\/td>\n02<\/strong><\/td>\n<\/tr>\n
                      Prerequisite<\/strong><\/td>\nSTA101G3 and STA102G2<\/strong><\/td>\n<\/tr>\n
                      Hourly Breakdown<\/strong><\/td>\nTheory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
                      25 Hours<\/strong><\/td>\n10 Hours<\/strong><\/td>\n65 Hours<\/strong><\/td>\n<\/tr>\n
                      Objective:<\/strong><\/td>\n<\/tr>\n
                      \n
                        \n
                      • Introduce the fundamentals of statistical quality control<\/li>\n
                      • Provide the quality control methods and tools<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                      Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
                      \n
                        \n
                      • Explain key concepts in statistical process control<\/li>\n
                      • Construct control charts to improve the process quality<\/li>\n
                      • Distinguish \u00a0variable charts \u00a0and attribute charts<\/li>\n
                      • Design appropriate acceptance sampling plans<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                      Course Contents:<\/strong><\/td>\n<\/tr>\n
                      \n
                        \n
                      • Introduction <\/strong>to the Concept of Quality Control<\/strong>: <\/strong>Fundamental concepts of quality and quality improvement<\/li>\n
                      • Methods of Statistical Process\u00a0 and Product Control: <\/strong>Process control, Product control, Causes of quality variation, Control charts: basic principles, choices of control limits, sample size and sampling frequency, analysis of pattern on control charts.<\/li>\n
                      • Control Charts for Variables: <\/strong>Chart (Mean Chart), -chart (Range chart), Estimation of population mean and population standard deviation, Relationship between population standard deviation and range, Lack of control, Tests for lack of control, Interpretation of \u00a0and \u00a0charts.<\/li>\n
                      • \u00a0<\/strong>Control Charts for Attributes: <\/strong>Control Chart for Nonconforming items; -chart and \u00a0– chart, Control Chart of Nonconformities; -chart and -chart<\/span><\/li>\n
                      • Acceptance Sampling For Attributes<\/strong>: <\/strong>Operating characteristic curve, Type A and Type B OC-curve, Average Outgoing Quality Limit (AOQL), Average Total Inspection, Acceptable Quality Level (AQL), Lot Tolerance Fraction Defective (LTFD), Producer\u2019s Risk and Consumer\u2019s Risk, Single sampling plan, Double sampling plan, Average Sample Number (ASN).<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                      Teaching Methods:<\/strong><\/td>\n<\/tr>\n
                      Lecture demonstration, Tutorial discussions, and laboratory practical<\/td>\n<\/tr>\n
                      Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
                      \n
                        \n
                      • In-course assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 30%<\/li>\n
                      • End of course Examination\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                      Recommended Readings:<\/strong><\/td>\n<\/tr>\n
                      \n
                        \n
                      • D.C. Montogomery, \u201cIntroduction to Statistical Quality Control\u201d, 6th<\/sup> edition, John Wiley & Sons, 1993.<\/li>\n
                      • E. L. Grant, S. Richard & S. Leavenworth, \u201cStatistical Quality Control\u201d, 7th<\/sup> edition, 1996.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
                        <\/span>STA304G2: Applied Statistics II<\/div>
                        \n
                        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                        Course Code<\/strong><\/td>\nSTA304G2<\/strong><\/td>\n<\/tr>\n
                        Course Title<\/strong><\/td>\nApplied Statistics II<\/strong><\/td>\n<\/tr>\n
                        Credit Value<\/strong><\/td>\n02 <\/strong><\/td>\n<\/tr>\n
                        Prerequisite\u00a0\u00a0\u00a0 <\/strong><\/td>\nSTA101G3, STA103G3 and STA201G3<\/strong><\/td>\n<\/tr>\n
                        Hourly Breakdown<\/strong><\/td>\nTheory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
                        \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 25 Hours<\/strong><\/td>\n10 Hours <\/strong><\/td>\n65 Hours<\/strong><\/td>\n<\/tr>\n
                        Objectives:<\/strong><\/td>\n<\/tr>\n
                        \n
                          \n
                        • Provide profound knowledge in statistical modeling<\/li>\n
                        • Introduce hypothesis testing procedures for non-normal data<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                        Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
                        \n
                          \n
                        • Recall the standard probability distributions<\/li>\n
                        • Identify appropriate probability distribution for a given real life problem<\/li>\n
                        • Examine the suitability of the model for a given data set<\/li>\n
                        • Apply appropriate non-parametric statistical test for non-normal data<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                        Course Contents:<\/strong><\/td>\n<\/tr>\n
                        \n
                          \n
                        • Statistical Modelling: <\/strong>Standard distributions; Binomial, Poisson, Geometric, Hypergeometric, Negative Binomial, Multinomial, Exponential, Gamma, Weibull, Normal, Chi-square, Beta, Pareto, and their use in modelling, Fitting parametric models, Assessing Goodness of fit<\/li>\n
                        • Non-parametric tests:<\/strong>Tests for normality, Contingency tables, single sample and paired samples non-parametric tests,\u00a0 Run test,\u00a0 Kruskal-Wallis test, Rank correlation<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                        Teaching Methods:<\/strong><\/td>\n<\/tr>\n
                        \n
                          \n
                        • Lecture demonstration, Tutorial discussions and laboratory practical<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                        Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
                        \n
                          \n
                        • In-course Assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 30%<\/li>\n
                        • End-of-course Examination\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                        Recommended Readings:<\/strong><\/td>\n<\/tr>\n
                        \n
                          \n
                        • Lee. J. Bain and Max Engelhardt, \u201cIntroduction to Probability and Mathematical Statistics\u201d, Kent Publishing Company, 2014.<\/li>\n
                        • R. E. Walpole, R. H. Myers, S. L. Myers and K.E. Ye, \u201cProbability and Statistics for Engineers and Scientists\u201d, Prentice Hall, 2014.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n
                          <\/span>STA305G2: Statistical Computing<\/div>
                          \n
                          \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
                          Course Title<\/strong><\/td>\nStatistical Computing<\/strong><\/td>\n<\/tr>\n
                          Course Code<\/strong><\/td>\nSTA305G2<\/strong><\/td>\n<\/tr>\n
                          Credit Value<\/strong><\/td>\n02 <\/strong><\/td>\n<\/tr>\n
                          Hourly Breakdown<\/strong><\/td>\nTheory<\/strong><\/td>\nPractical<\/strong><\/td>\nIndependent Learning<\/strong><\/td>\n<\/tr>\n
                          –<\/strong><\/td>\n60 Hours<\/strong><\/td>\n40 Hours<\/strong><\/td>\n<\/tr>\n
                          Objective:<\/strong><\/td>\n<\/tr>\n
                          Provide fundamental knowledge and skills in statistical computing using statistical software<\/td>\n<\/tr>\n
                          Intended Learning Outcomes:<\/strong><\/td>\n<\/tr>\n
                          \n
                            \n
                          • Analyze data sets using statistical software<\/li>\n
                          • Describe data types and data structures<\/li>\n
                          • Apply build-in functions to import and manipulate data sets<\/li>\n
                          • Calculate summary statistics for given data sets<\/li>\n
                          • Utilize build-in functions for probability distributions and simulation<\/li>\n
                          • Make use of statistical software to write simple functions for given statistical problems<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                          Course Contents:<\/strong><\/td>\n<\/tr>\n
                          \n
                            \n
                          • Introduction to the software: <\/strong>History, user community, online helps and resources, saving datasets.<\/li>\n
                          • Data types and data structures:<\/strong> Numerical and text values, vectors, factors, matrices and arrays, lists and data frame.<\/span><\/li>\n
                          • Importation and manipulation:<\/strong> Read text, Excel, SPSS and Minitab datasets into software and build-in datasets, operations, manipulation and extraction.<\/span><\/li>\n
                          • Descriptive statistics: <\/strong>Tables, summary statistics, graphical representation.<\/span><\/li>\n
                          • Probability distributions and simulation:<\/strong> Build-in functions for probability distributions, simulation, plotting probability distributions.<\/span><\/li>\n
                          • Inferential statistics: <\/strong>Confidence intervals and hypothesis tests, simple and multiple linear regressions, analysis of variance.<\/span><\/li>\n
                          • Simple function: <\/strong>Writing simple functions to automate regular statistical tasks.<\/span><\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                          Teaching Methods:<\/strong><\/td>\n<\/tr>\n
                          Laboratory practical, group assignments and e-resources<\/td>\n<\/tr>\n
                          Assessment\/ Evaluation Details:<\/strong><\/td>\n<\/tr>\n
                          \n
                            \n
                          • In-course practical assessments\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 30%<\/li>\n
                          • End of course practical Examination\u00a0\u00a0\u00a0\u00a0 70%<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
                          Recommended Readings:<\/strong><\/td>\n<\/tr>\n
                          \n
                            \n
                          • P. L. Micheaux, R. Drouilhet, & B. Liquet, B, \u201cThe R software: Fundamentals of programming and statistical analysis\u201d, Springer, 2013.<\/li>\n
                          • J. Braun & D. J. Murdoch, \u201cA first course in statistical programming with R\u201d. Second edition, Cambridge University Press, 2007.<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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