Sentences with phrase «compare different data sets»

Now that the sun is shining in the north and we have these wonderful views, we can begin to compare the different data sets and tease out what Titan's lakes are doing near the north pole.»

This new breed of HR employee will integrate data sets, interpret findings and compare different data sets to make new discoveries.

Not exact matches

Even if you don't work with a seasonal client, you can still set a date range and compare it to historical data to learn how different terms are growing.

Comparing the DNA sequences of similarly shaped proteins in various organisms produces a geneaology of all life on earth that matches those created from completely different data sets.

By studying such a large data set — over 200,000 galaxies in 21 different wavelengths, or colors of light, from ultraviolet to infrared — astronomers compared the energy emissions from galaxies across a wide swath of space and time to read the history of the universe.

Silverman calls up a set of raw data from her team's recent experiments: bar charts comparing sex differences in six different strains of mice across various tests intended to detect behaviors relevant to autism.

By comparison, only two of the 15 genes were not significantly different when comparing the eye field to PNP or LE data sets (Table S1).

The range (due to different data sets) of global surface warming since 1979 is 0.16 °C to 0.18 °C per decade compared to 0.12 °C to 0.19 °C per decade for MSU estimates of tropospheric temperatures.

Each evaluation study often uses a different model and / or data set, making it impossible to directly compare the performance and computational efficiency of various approaches that simulate the same aerosol process.

Students might repeat this activity on another day with a different set of pennies to see how the two batches of data compare.

There is a «model» which has a certain sensitivity to 2xCO2 (that is either explicitly set in the formulation or emergent), and observations to which it can be compared (in various experimental setups) and, if the data are relevant, models with different sensitivities can be judged more or less realistic (or explicitly fit to the data).

While herein lies the problem, if you want to compare trends between different data sets, then it is best to make sure that they cover the same period.

The range (due to different data sets) of global surface warming since 1979 is 0.16 °C to 0.18 °C per decade compared to 0.12 °C to 0.19 °C per decade for MSU estimates of tropospheric temperatures.

This is either misleading or has the potential to be, as different sets of data (different stations) are being compared on the same graph of the temperature trend over the last 100 + years as if it's the same data source.

When one compares the different global temperature data sets correctly, one result emerges more strongly than any other: that they agree.

Using the interactive below, you can compare three different time series data sets collected from Station Mauna Loa and Station Aloha in the Pacific Ocean.

The range (due to different data sets) of the global mean tropospheric temperature trend since 1979 is 0.12 °C to 0.19 °C per decade based on satellite - based estimates (Chapter 3) compared to a range of 0.16 °C to 0.18 °C per decade for the global surface warming.

In science, the term «trick» is slang for a clever (and legitimate) technique, in this case Michael E. Mann's technique for comparing two different data sets.

Any scientist knows you can not reliably compare data sets obtained by such widely different methods unless you have a reliable means of validating them.

Aren't we still left with two data sets for the two periods compared in Webster et al. which are analysed with different methods?

How does this amount compare with the heating of the different data sets and the reanalysis?

The method I used was a way to compare long term data sets of different record lengths, and how adding stations distorts the average.

Secondly, we conducted the same set of analyses without imputing the missing data, finding no substantively different results compared to findings based on imputed data.