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MATH 4511 - Statistics for Geomatics

Mathematics Part-time Studies Course

School of Computing and Academic Studies

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Course Details

Data organization, estimation, hypothesis testing, propagation of measurement error, error ellipses and bivariate normally distributed data as used in least squares adjustments.


MATH 3511.



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Learning Outcomes

Upon successful completion, the student will be able to:

  • Produce histograms and frequency polygons from raw, observed survey data.
  • Calculate the mean and median of a data set.
  • Compute the variance and standard deviation of samples of survey measurement.
  • Use Chebyshev's theorem to illustrate the relationship between the spread of the data and the standard deviation of the data.
  • Calculate the weighted mean of a data set.
  • Define the basic laws of probability.
  • Connect probability to the randomness of error in measurement.
  • Define discrete probability distributions, their means and variances.
  • Employ the definition of discrete probability distribution functions to define the binomial distribution.
  • Define continuous probability functions, their means and variances.
  • Link the definitions of pdfs to well-defined pdfs such as the normal, student's-t, chi-square and F-distributions.
  • Use the normal distribution to determine probabilities of observing defined ranges of measurement in survey projects.
  • Define, intuitively, the Central Limit Theorem.
  • Use the Central Limit Theorem and knowledge of the normal distribution to develop the ideas of confidence interval estimation.
  • Estimate confidence intervals for the mean of large and small samples.
  • Apply confidence interval estimation to engineering and cadastral surveys and the analysis of error in least-square adjustment problems.
  • Calculate confidence intervals for variances and standard deviations.
  • Apply variance interval estimation to EDM measurements.
  • Set up various hypotheses tests.
  • Recognize type I and type II errors.
  • Use hypothesis testing in analyzing the outcomes of least square adjustment problems such as determining if the variance factor is satisfactory.
  • Investigate error propagation in survey calculations.
  • Calculate propagated in geometric studies such as in triangulation problems.
  • Apply error propagation to determine the statistical error in a traverse.
  • Use error propagation as an assist to preliminary investigation in survey design given final design parameters.
  • Calculate the principal parameters of bivariate data: the correlation coefficient and covariance.
  • Relate adjustment of observations, variance-covariance matrices and correlation.
  • Test the correlation coefficient for significance.
  • Relate the ellipse of error to the bivariate normal distribution.
  • Link covariance matrices with the ellipse of error.
  • Use the ellipse of error in analysis of adjustment problems.

Effective as of Fall 2003


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