# Where Theory Meets Practice Archives

## Transformation of Observations, Part 3

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This entry is part 13 of 34 in the series Where Theory Meets Practice

Above: Typical and low-distortion map projections. Using a Project Factor In Part 1 of this series of articles (September 2014 issue), I explained how to transform surface observations into a geocentric coordinate system so that it could be compared to GNSS baseline vectors. In Part 2 (December 2014) I explained how the creation of a […]

## Transformation of Observations, Part 4

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This entry is part 14 of 34 in the series Where Theory Meets Practice

Single Project Factor Here concludes a four-part series of articles about transformation of observations, spanning from September and December 2014 to March 2015. Part 1 covers how to transform surface observations into a geocentric coordinate system so that they can be compared to GNSS baseline vectors. Part 2 is about how the creation of a […]

## I Don’t Need No Stinkin’ Statistics

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This entry is part 15 of 34 in the series Where Theory Meets Practice

Surveying Statistics, Part 1 Do you perform GNSS surveys? Do you use OPUS or perform RTK surveys simply because you don’t understand your software’s output? Do you always use the compass-rule adjustment for traverse data simply because that’s the way you have always done it? Do you ever have a situation where you know something […]

## The Normal Distribution, Part 1

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This entry is part 16 of 34 in the series Where Theory Meets Practice

Errors in observations can be classified as systematic or random. Systematic errors are errors that follow physical laws and can be mathematically corrected or removed by following proper field procedures with instruments. For example, the expansion or contraction of a steel tape caused by temperatures that differ from the tape’s standard temperature is a systematic […]

## Surveying Statistics

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This entry is part 17 of 34 in the series Where Theory Meets Practice

Part 2: The Normal Distribution Part 1 of this series appeared in the May 2015 issue. Errors in observations can be classified as systematic or random. Systematic errors follow physical laws and can be mathematically corrected or removed by following proper field procedures with instruments. For example, the expansion or contraction of a steel tape […]

## Surveying Statistics

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This entry is part 18 of 34 in the series Where Theory Meets Practice

The t Distribution, Part 1 In my previous article I discuss the normal distribution and how its properties can be used to isolate blunders in observations. Recall that the normal distribution is based on an infinite number of observations. However, in practice we never collect a population of data but rather a small sample from […]