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Environmental Data Analysis with MatLab

Environmental Data Analysis with MatLab

Environmental Data Analysis with MatLab. Lecture 24: Confidence Limits of Spectra; Bootstraps. Housekeeping. This is the last lecture The final presentations are next week The last homework is due today. SYLLABUS.

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467 views • 46 slides
What is the correct number of break points hidden in a climate record?

What is the correct number of break points hidden in a climate record?

What is the correct number of break points hidden in a climate record?. Defining breaks. Consider the differences of one station compared to a reference. (Kriged ensemble of surrounding stations) Breaks are defined by abrupt changes in the station-reference time series. Internal variance

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434 views • 43 slides
What is the correct number of break points hidden in a climate record?

What is the correct number of break points hidden in a climate record?

What is the correct number of break points hidden in a climate record?. Ralf Lindau Victor Venema Bonn University. Defining breaks. Consider the differences of one station compared to a reference. (Kriged ensemble of surrounding stations)

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449 views • 39 slides
What is the correct number of break points hidden in a climate record?

What is the correct number of break points hidden in a climate record?

What is the correct number of break points hidden in a climate record?. Ralf Lindau Victor Venema Bonn University. Defining breaks. Consider the differences of one station compared to a reference. (Kriged ensemble of surrounding stations)

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466 views • 38 slides
First Results of the Daily Stew Project

First Results of the Daily Stew Project

First Results of the Daily Stew Project. Ralf Lindau. First Steps. Data: Climate stations of DWD with daily data (or even 7, 14, 21 h) Although the project is focused on weather indices and extremes, we consider initially a much easier parameter: Monthly mean temperature

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323 views • 23 slides
10. Basic Regressions with Times Series Data

10. Basic Regressions with Times Series Data

10. Basic Regressions with Times Series Data. 10.1 The Nature of Time Series Data 10.2 Examples of Time Series Regression Models 10.3 Finite Sample Properties of OLS Under Classical Assumptions 10.4 Functional Form, Dummy Variables, and Index Numbers 10.5 Trends and Seasonality.

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390 views • 21 slides

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