Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 2000-2013 for each pixel.

A sieve analysis (or gradation test) is a practice or procedure commonly used in civil engineering to assess the particle size distribution (also called gradation) of a granular material.

As part of the Sediment Analysis and Geo-App (SAGA) a series of data processing web services are available to assist in computing sediment statistics based on results of sieve analysis. The Standard Deviation first computes the percentiles for D5, D16, D35, D84,D95 and then uses the formula, (D16-D84)/4)+(D5-D95)/6

Percentiles can also be computed for classification sub-groups: Overall (OVERALL), <62.5 um (DS_FINE), 62.5-250um (DS_MED), and > 250um (DS_COARSE)

Parameter #1: Input Sieve Size, Percent Passing, Sieve Units.

• Semi-colon separated. ex: 75000, 100, um; 50000, 100, um; 37500, 100, um; 25000,100,um; 19000,100,um
• A minimum of 4 sieve sizes must be used. Units supported: um, mm, inches, #, Mesh, phi
• All sieve sizes must be numeric

Parameter #2: Subgroup

• Options: OVERALL, DS_COARSE, DS_MED, DS_FINE
• The statistics are computed for the overall sample and into Coarse, Medium, and Fine sub-classes
• Coarse (> 250 um) DS_COARSE
• Medium (62.5 – 250 um) DS_MED
• Fine (< 62.5 um) DS_FINE
• OVERALL (all records)

Parameter #3: Outunits

• Options: phi, m, um

Generate a table with autofill, and use absolute cell reference to calculate variance and standard deviation.

The signature of Dansgaard-Oeschger events - millennial-scale abrupt climate oscillations during the last glacial period - is well established in ice cores and marine records (Labeyrie, 2000, doi:10.1126/science.290.5498.1905; Blunier and Brook, 2001, doi:10.1126/science.291.5501.109: Bond et al., 2001, doi:10.1126/science.1065680). But the effects of such events in continental settings are not as clear, and their absolute chronology is uncertain beyond the limit of 14C dating and annual layer counting for marine records and ice cores, respectively. Here we present carbon and oxygen isotope records from a stalagmite collected in southwest France which have been precisely dated using 234U/230Th ratios. We find rapid climate oscillations coincident with the established Dansgaard-Oeschger events between 83,000 and 32,000 years ago in both isotope records. The oxygen isotope signature is similar to a record from Soreq cave, Israel (Bar-Mathews et al., 2000, doi:10.1016/S0009-2541(99)00232-6), and deep-sea records (Bond et al., 1993, doi:10.1038/365143a0; Shackleton and Hall, 2001, doi:10.1029/2000PA000513), indicating the large spatial scale of the climate oscillations. The signal in the carbon isotopes gives evidence of drastic and rapid vegetation changes in western Europe, an important site in human cultural evolution. We also find evidence for a long phase of extremely cold climate in southwest France between 61.2 +/-0.6 and 67.4 0.9 kyr ago.

1.Colour patterns are used by many species to make decisions that ultimately affect their Darwinian fitness. Colour patterns consist of a mosaic of patches that differ in geometry and visual properties. Although traditionally pattern geometry and colour patch visual properties are analysed separately, these components are likely to work together as a functional unit. Despite this, the combined effect of patch visual properties, patch geometry, and the effects of the patch boundaries on animal visual systems, behaviour and fitness are relatively unexplored.

2.Here we describe Boundary Strength Analysis (BSA), a novel way to combine the geometry of the edges (boundaries among the patch classes) with the receptor noise estimate (ΔS) of the intensity of the edges. The method is based upon known properties of vertebrate and invertebrate retinas. The mean and SD of ΔS (mΔS, sΔS) of a colour pattern can be obtained by weighting each edge class ΔS by its length, separately for chromatic and ac...

High School Heights Dataset

You will find three datasets containing heights of the high school students.

All heights are in inches.

The data is simulated. The heights are generated from a normal distribution with different sets of mean and standard deviation for boys and girls.

There are 500 measurements for each gender.

Here are the datasets:

• hs_heights.csv: contains a single column with heights for all boys and girls. There's no way to tell which of the values are for boys and which ones are for girls.

• hs_heights_pair.csv: has two columns. The first column has boy's heights. The second column contains girl's heights.

• hs_heights_flag.csv: has two columns. The first column has the flag is_girl. The second column contains a girl's height if the flag is 1. Otherwise, it contains a boy's height.

To see how I generated this dataset, check this out: https://github.com/ysk125103/datascience101/tree/main/datasets/high_school_heights

Image by Gillian Callison from Pixabay

Description of results of SD-TOPSIS application (combination A) using selected moment characteristics.

We present a simple, spreadsheet-based method to determine the statistical significance of the difference between any two arbitrary curves. This modified Chi-squared method addresses two scenarios: A single measurement at each point with known standard deviation, or multiple measurements at each point averaged to produce a mean and standard error. The method includes an essential correction for the deviation from normality in measurements with small sample size, which are typical in biomedical sciences. Statistical significance is determined without regard to the functionality of the curves, or the signs of the differences. Numerical simulations are used to validate the procedure. Example experimental data are used to demonstrate its application. An Excel spreadsheet is provided for performing the calculations for either scenario.

Storm event mean concentrations (EMC’s), fluxes and loads of trace metal, polycyclic aromatic hydrocarbons (PAHs), bacteria and nutrients in urban stormwater runoff from specific land-uses and mass emission sites. Station identification, chemistry, and microbiology tables. Supplemental flow and precipitation tables to calculate EMC’s and ME.

Project Page: http://archive.sccwrp.org/ResearchAreas/Stormwater/TimeVariableStormwaterPollutantRunoffFromWatershed.aspx

Techical Information: Quality code: BDL - below detection limit, NQE - Analyte not quantitatively extracted from filter matrixAir volume used to calculate the below presented detection limit for each species is 1400 m**3. Where data are identified as BDL, the concentration given is 75% of the detection limit calculated using the actual air volume for each sample.

Techical Information: Radar did not detect plume when below about 2.9 km due to mountain ranges and curvature of the Earth.Date/time (UTC) refer to the end of the time interval.

During the last deglaciation, the opposing patterns of atmospheric CO2 and radiocarbon activities (D14C) suggest the release of 14C-depleted CO2 from old carbon reservoirs. Although evidences point to the deep Pacific as a major reservoir of this 14C-depleted carbon, its extent and evolution still need to be constrained. Here we use sediment cores retrieved along a South Pacific transect to reconstruct the spatio-temporal evolution of D14C over the last 30,000 years. In ~2,500-3,600 m water depth, we find 14C-depleted deep waters with a maximum glacial offset to atmospheric 14C (DD14C = -1,000 per mil). Using a box model, we test the hypothesis that these low values might have been caused by an interaction of aging and hydrothermal CO2 influx. We observe a rejuvenation of circumpolar deep waters synchronous and potentially contributing to the initial deglacial rise in atmospheric CO2. These findings constrain parts of the glacial carbon pool to the deep South Pacific.

We present a new record of eolian dust flux to the western Subarctic North Pacific (SNP) covering the past 27000 years based on a core from the Detroit Seamount. Comparing the SNP dust record to the NGRIP ice core record shows significant differences in the amplitude of dust changes to the two regions during the last deglaciation, while the timing of abrupt changes is synchronous. If dust deposition in the SNP faithfully records its mobilization in East Asian source regions, then the difference in the relative amplitude must reflect climate-related changes in atmospheric dust transport to Greenland. Based on the synchronicity in the timing of dust changes in the SNP and Greenland, we tie abrupt deglacial transitions in the 230Th-normalized 4He flux record to corresponding transitions in the well-dated NGRIP dust flux record to provide a new chronostratigraphic technique for marine sediments from the SNP. Results from this technique are complemented by radiocarbon dating, which allows us to independently constrain radiocarbon paleoreservoir ages. We find paleoreservoir ages of 745 ± 140 yr at 11653 yr BP, 680 ± 228 yr at 14630 yr BP and 790 ± 498 yr at 23290 yr BP. Our reconstructed paleoreservoir ages are consistent with modern surface water reservoir ages in the western SNP. Good temporal synchronicity between eolian dust records from the Subantarctic Atlantic and equatorial Pacific and the ice core record from Antarctica supports the reliability of the proposed dust tuning method to be used more widely in other global ocean regions.

Scores ranged from 1 to 3, with higher scores indicating likelihood of committing the bias. Stars indicate self- and other- rating score comparisons.*** p < .001.Means (standard deviations) for the self- and other-rating scores for each bias in Experiment 2.

This dataset is about: (Appendix 1) Percentage sand, mean grain size, standard deviation and modal data of sediment core CRP-2. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.510762 for more information. Sample spacing = 0.5 - 1.0 m

Analysis of ‘Walmart Dataset (Retail)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rutuspatel/walmart-dataset-retail on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Dataset Description :

This is the historical data that covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

Store - the store number

Date - the week of sales

Weekly_Sales - sales for the given store

Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

Temperature - Temperature on the day of sale

Fuel_Price - Cost of fuel in the region

CPI – Prevailing consumer price index

Unemployment - Prevailing unemployment rate

Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Analysis Tasks

Basic Statistics tasks

1) Which store has maximum sales

2) Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3) Which store/s has good quarterly growth rate in Q3’2012

4) Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5) Provide a monthly and semester view of sales in units and give insights

Statistical Model

For Store 1 – Build prediction models to forecast demand

Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

Change dates into days by creating new variable.

Select the model which gives best accuracy.

--- Original source retains full ownership of the source dataset ---

[ Derived from parent entry - See data hierarchy tab ]

This is the Baltic and North Sea Climatology (BNSC) for the Baltic Sea and the North Sea in the range 47 ° N to 66 ° N and 15 ° W to 30 ° E. It is the follow-up project to the KNSC climatology. The climatology was first made available to the public in March 2018 by ICDC and is published here in a slightly revised version 2. It contains the monthly averages of mean air pressure at sea level, and air temperature, and dew point temperature at 2 meter height. It is available on a 1 ° x 1 ° grid for the period from 1950 to 2015. For the calculation of the mean values, all available quality-controlled data of the DWD (German Meteorological Service) of ship observations and buoy measurements were taken into account during this period. Additional dew point values were calculated from relative humidity and air temperature if available. Climatologies were calculated for the WMO standard periods 1951-1980, 1961-1990, 1971-2000 and 1981-2010 (monthly mean values). As a prerequisite for the calculation of the 30-year-climatology, at least 25 out of 30 (five-sixths) valid monthly means had to be present in the respective grid box. For the long-term climatology from 1950 to 2015, at least four-fifths valid monthly means had to be available. Two methods were used (in combination) to calculate the monthly averages, to account for the small number of measurements per grid box and their uneven spatial and temporal distribution: 1. For parameters with a detectable annual cycle in the data (air temperature, dew point temperature), a 2nd order polynomial was fitted to the data to reduce the variation within a month and reduce the uncertainty of the calculated averages. In addition, for the mean value of air temperature, the daily temperature cycle was removed from the data. In the case of air pressure, which has no annual cycle, in version 2 per month and grid box no data gaps longer than 14 days were allowed for the calculation of a monthly mean and standard deviation. This method differs from KNSC and BNSC version 1, where mean and standard deviation were calculated from 6-day windows means. 2. If the number of observations fell below a certain threshold, which was 20 observations per grid box and month for the air temperature as well as for the dew point temperature, and 500 per box and month for the air pressure, data from the adjacent boxes was used for the calculation. The neighbouring boxes were used in two steps (the nearest 8 boxes, and if the number was still below the threshold, the next sourrounding 16 boxes) to calculate the mean value of the center box. Thus, the spatial resolution of the parameters is reduced at certain points and, instead of 1 ° x 1 °, if neighboring values are taken into account, data from an area of 5 ° x 5 ° can also be considered, which are then averaged into a grid box value. This was especially used for air pressure, where the 24 values of the neighboring boxes were included in the averaging for most grid boxes. The mean value, the number of measurements, the standard deviation and the number of grid boxes used to calculate the mean values are available as parameters in the products. The calculated monthly and annual means were allocated to the centers of the grid boxes: Latitudes: 47.5, 48.5, ... Longitudes: -14.5, -13.5, … In order to remove any existing values over land, a land-sea mask was used, which is also provided in 1 ° x 1 ° resolution. In this version 2 of the BNSC, a slightly different database was used, than for the KNSC, which resulted in small changes (less than 1 K) in the means and standard deviations of the 2-meter air temperature and dew point temperature. The changes in mean sea level pressure values and the associated standard deviations are in the range of a few hPa, compared to the KNSC. The parameter names and units have been adjusted to meet the CF 1.6 standard.

This dataset is about: (Appendix 2) Percentage sand, mean grain size, standard deviation and modal data of sediment core CRP-3. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.510762 for more information. Sample spacing = 0.5 - 1.0 m

A set of 4 criteria for the needs of multi-criteria evaluation ‐ combinations B1– B5.

Snow samples were taken on a daily basis along a 100 m wind-parallel transect at the EastGRIP ice core deep drilling site. The snow was collected in the morning at 11 positions with 10 m spacing into four cumulative samples – each for one depth interval. The depth intervals are top 0.5 cm , top 1 cm , top 2 cm and top 5 cm . Each day, undisturbed snow was sampled and the exact sample location marked to avoid sampling disturbed snow during the next sampling event. The samples were shipped frozen to the Alfred-Wegener-Institut and stored at -25°C. Prior to measurements the samples were melted in the sample bags at room temperature. For the measurement of the isotopic composition the instruments Picarro L2120-i and Picarro L2140-i were used. The measurement set-up followed the Van-Geldern Protocol. Each sample was injected four times and the standard deviation is computed. We calculate the average over all the standard deviations as a measure of uncertainty. We find this average to be 0.01 permil for δ18O (with stdev 0.01) and 0.08 permil for δD (stdev 0.1). The maximum standard deviation within the data set was found to be 0.07 for δ18O and 0.9 for δD. As a measure of accuracy the off-set between the defined and measured value of the quality check standard for each measurement run is provided. We calculate the average of this off-set for the whole data set and obtain a value of -0.06 permil for δ18O (stdev 0.02) and -0.69 permil for δD (stdev 0.18). The maximum off-set found within the data set was -0.1 permil for δ18O and -1.12 permil for δD.