Course Description
Measures of central tendency: Mean, Median and Mode and their implications; Measures of Dispersion: Range, Mean deviation, Standard deviation, Coefficient of Variation ( C.V.) , Skewness, Kurtosis. Time series analysis: Concept, Additive and Multiplicative models, Components of time series, Trend analysis: Least Square method - Linear and Non-Linear equations, Applications in business decision-making. Index Numbers:- Meaning , Types of index numbers, uses of index numbers, Construction of Price, Quantity and Volume indices:- Fixed base and Chain base methods. Correlation analysis; Regression analysis; Linear simple and multiple regressions, Linear Probability Model (LPM); Non-linear regressions; Cobb-Douglas production function, Dummy Variables; Logit model and Probit model. Concept of probability and its uses in business decision-making; Addition and multiplication theorems; Bayes’Theorem and its applications. Probability Theoretical Distributions: Concept and application of Binomial; Poisson and Normal distributions, standard normal distribution, Z-scores and their applications. Probability:. Estimation Theory and Hypothesis Testing: Sampling theory; Formulation of Hypotheses; Application of Z test, t-test, F-test and Chi-Square test. Computer application in statistical data processing and analysis
Course Description
Hypothesis Testing; Introduction to Hypothesis; One sample, Independent and paired Z-Test and T-test. One Way ANOVA. Chi Square; Goodness of fit and Contingency table analysis. Linear Regression and Correlation; Scatter plots, Pearson's Correlation coefficient and Spearmans rank Correlation coefficient. Simple linear regression. Properties of regression, Error of estimation, Coefficient of determination, Hypothesis testing involving the slope, Interpreting computer output of multiple regression. Time series analysis; Introduction, Structure of a time series, Components of a time series. Time series smoothing, Forecasting. Non-Paramentric tests; Sign tests, The tests of randomness, Wilcoxon signed rank tests, The Mann – Whitney U tests. The Kruskal – Wallis Test. Kolmogorov-Smirnov Test for Normality.Course Description
This is a Demo of how quantitative units may be assessed at the end of semester. The ideas may be polished further. For the sake of the demo, the access restrictions (Password and use of safe exam browser) have been removed.
- Teacher: Prof J. M. Kihoro
Course Description
The Normal Curve, Inferential Statistics; Introduction to Inferential Statistics: Sampling and the Sampling Distribution. Estimation and Hypothesis Testing: The One-Sample Case, The Two-Sample Case, Analysis of Variance, Chi Square, Bivariate Measures of Association: Nominal Level, Ordinal Level, Interval-Ratio Level, Multivariate Techniques. Index Numbers; Simple and weighted index numbers, Laspeyres and Paasche indices, Index numbers in practice. Time series analysis; Components of a time series, Time series smoothing and Forecasting.
- Teacher: John Kihoro
- Teacher: Prof J. M. Kihoro
Course Description
History and nature of Operations Research. Linear Programming; simplex method, solution and its interpretation. application areas; transportation model; using Northwest method, least cost method, vogel approximation method (VAM). Assignment model; formulation solution. Network model; deterministic, critical path analysis/critical path method, probabilistic model, programme evaluation review technique, crashing, resource leveling. Queuing model, single server and multiserver systems. Simulation, Game theory; pure and mixed strategies.
Course Purpose
This is a module meant for any student who is struggling to understand the basic concepts in statistics. The course creates a forum for giving feedback to students on the basis of their statistical questions. The main approach will be through guided online exercises which are system managed but the facilitator will from time to time revise the exercises and sometimes engage the learner directly. Face to face sessions may be arranged when it becomes absolutely necessary.
Outline (Not all concepts are currently implemented)
- Measures of central tendency, location and dispersion; arithmetic mean, median, mode, standard deviation, skewness, quartiles, percentiles.
- Frequency distributions
- Basic probability theory; event, compound events, conditional and joint probability
- Probability distributions and mathematical expectation
- Standard probability distributions.
- The standard normal distribution and Z-Scores
- Sampling distributions and applications and use of standard statistical tables
- Hypothesis Testing; Introduction to Hypothesis; One sample, Independent and paired Z-Test and T-test
- One Way ANOVA
- Chi Square; Goodness of fit and Contingency table analysis
- Linear Regression and Correlation; Pearson's Correlation coefficient and Spearmans rank Correlation coefficient.
- Simple linear regression. Properties of regression, Error of estimation, Coefficient of determination, Hypothesis testing involving the slope, Interpreting computer output of multiple regression.
- Index Numbers; Simple and weighted index numbers, Laspeyres and Paasche indices, Index numbers in practice.
- Time seriesn analysis; Introduction, Structure of a time series, Components of a time series. Time series smoothing, Forecasting.
- Non-Paramentric tests; Sign tests, The tests of randomness, Wilcoxon signed rank tests, The Mann – Whitney U tests. The Kruskal – Wallis Test. Kolmogorov-Smirnov Test for Normality.
Introduction to data matrix and arrays. Distribution of the mean vector, the sample mean and related inferences. Multivariate normal Distribution and its properties. Partial and Multiple correlation coefficient, Multiple regression and derivation of regression coefficients.
Hottellings T-distribution and Roys union-intersection. Wisharts distribution and its properties. Infrences relating to mean and covariance matrix. Test of independence of sets of variates. Multivariate analysis of variance (MANOVA). Discriminant, Cluster, Principal component and factor analysis. Canonical variable and correlations.
References
1. Applied Multivariate statistical Analysis by Rich. A Johnson
and Dean W. Wichern
2. Multivariate Statistical methods by Donard F. Morrison
- Teacher: John Kihoro
Course Description
Index numbers and applications to business; Review of distribution theory and applications; Random variables, expectation of random variables, parametric families of univariate distributions. Probability and sampling distributions. Parametric Inferences; Populations and samples, sampling distribution of statistics, Essence of estimation: method of moments and maximum likelihood, properties of good estimators. Testing of hypotheses: Concepts of testing and associated errors. Survey sampling; economic data, methods of data collection Sampling techniques and applications: Criteria for selecting sampling technique and determination of sample size. Non-Parametric Methods; Sign tests for single and paired samples, Wilcoxon signed rank and rank sum tests, Runs Test for randomness, Kruskal-Wallis test and Rank correlation-test, Chi-square tests for single samples and contingency tables. Correlation and regression analysis; Simple and partial correlation. Regression analysis: assumption underlying classical multiple regression model, Least squares estimation and properties of estimators, confidence intervals, hypothesis testing for individual coefficients. Decision Analysis, Certainty and uncertainty, analysis of decision problems.