24135 Methods of Statistics (2)

Course Description

Topics include: Join distributions( the scatter diagram, covariance, linear regration and the method of least squares, regration analysis of grouped data, correlation analysis, coefficient of rank correlation ,multiple linear regression, multiple correlation analysis, standard error of estimates of regression coefficients), random vectors and matrices, simple linear regression model in matrix terms, least square estimation of regression parameters, fitted values under regression parameters, analysis of variance result.

Learning Objective

  • At the end of this course, students are able to:-
  • 1. Discuss how sample data can often be used to estimate certain unknown population parameters (eg. Mean, standard deviation, proportions), indicate how the central limit theorem is used, to set up (1-α)% confidence intervals, and determine the correct size of a sample for a given allowable error.
  • 2. Analyze how sample data can be used to reject or accept the null hypothesis, discuss the type I and type II error, and distinguish between tests concerning means, difference between means and proportions.
  • 3. Compute excepted frequencies, analyze contingency tables and discuss a Chi-Square test statistic to test goodness of fit.
  • 4. Set up ANOVA charts, analyze how one-way or two- way ANOVA’s are used when comparing several sample means or when two factors may affect the sample mean. Set up confidence intervals for regression estimates the null hypothesis H_0 that β_1 is zero or not.

Credits

    3

Books for this Course

  • 1. An Introduction to Statistical Mehtods, CB Gupta&Vijay Gupta
  • 2. Applied Linear Statisticsl Methods, John Neter, William Wasser and Michael H.Kutner

Times Offered

  • September
  • January

Course Prerequisite

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