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• Lecture - Error Propagation (2)

  In the previous video we have seen examples of how different types of errors propagate into the estimated parameters. In...

• Reading - 4.2 Summary

  Best linear unbiased estimation BLUE is an acronym for Best Linear Unbiased Estimation. Although it is based on a fundamentally...

• Reading - 3.2 Summary

  Weighted Least Squares Estimation In ordinary least squares estimation, we assume that all observations are equally important. In many cases...

• Reading - 2.3 Summary

  Mathematical model The mathematical model in observation theory is the combination of the functional and stochastic model. The functional model...

• Reading - Learning objectives: estimation

  In the previous modules you learned how to estimate the parameters of interest based on the principles of (weighted) least squares or...

• Reading - 1.2.1 Summary

  Quality and Types of Errors Observations or measurements are random or stochastic variables. Notation: \(\underline{y}\). The quality of the observations is...

• Reading - 1.2.2 Summary

  Quality and Types of Errors Distribution of random errors In absence of systematic biases and outliers, the individual random errors are...

• Reading - 6.1 Short-time: introduction

  In this module, heat conduction will be discussed further . In this first subsection, the concepts of unsteady heat conduction...

• Reading - 5.1 Heat transfer coefficient: introduction

  Welcome to the 5th module! Great to see that you are still with us.  Have you ever wondered how long...

• Reading - 5.1 Heat transfer coefficient: ending

  Another chapter is at its end: you have finished chapter 5.1. Great job! In this chapter, you have applied Newton's law of cooling...