show the average age corresponds to the arithmetic mean off all ages of the total of the population .The media age show the age the individual in a population with 50% of the population being younger and 50% being older than the individual .

BackgroundThe accurate measurement of educational attainment is of great importance for population research. Past studies measuring average years of schooling rely on strong assumptions to incorporate binned data. These assumptions, which we refer to as the standard duration method, have not been previously evaluated for bias or accuracy.MethodsWe assembled a database of 1,680 survey and census datasets, representing both binned and single-year education data. We developed two models that split bins of education into single year values. We evaluate our models, and compare them to the standard duration method, using out-of-sample predictive validity.ResultsOur results indicate that typical methods used to split bins of educational attainment introduce substantial error and bias into estimates of average years of schooling, as compared to new approaches. Globally, the standard duration method underestimates average years of schooling, with a median error of -0.47 years. This effect is especially pronounced in datasets with a smaller number of bins or higher true average attainment, leading to irregular error patterns between geographies and time periods. Both models we developed resulted in unbiased predictions of average years of schooling, with smaller average error than previous methods. We find that one approach using a metric of distance in space and time to identify training data, had the best performance, with a root mean squared error of mean attainment of 0.26 years, compared to 0.92 years for the standard duration algorithm.ConclusionsEducation is a key social indicator and its accurate estimation should be a population research priority. The use of a space-time distance bin-splitting model drastically improved the estimation of average years of schooling from binned education data. We provide a detailed description of how to use the method and recommend that future studies estimating educational attainment across time or geographies use a similar approach.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

All statistics are significant at the 95% c.l. Short rains season statistics for 1993 are missing.

Mean, median and modal ages at death in the UK and its constituent countries, 2001 to 2003 and 2016 to 2018.

Sierra Leone SL: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 2.860 % in 2018. Sierra Leone SL: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 2.860 % from Dec 2018 (Median) to 2018, with 1 observations. Sierra Leone SL: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sierra Leone – Table SL.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2011-2016 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Percentage of statistically significant effect size estimates, median number of effect sizes and median of average sample size per homogeneous subset, and mean and median of effect size estimates when the subsets were analyzed with random-effects meta-analysis, p-uniform, and random-effects meta-analysis based on the 10% most precise observed effect sizes.

Greece GR: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -8.350 % in 2015. Greece GR: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -8.350 % from Dec 2015 (Median) to 2015, with 1 observations. Greece GR: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

In 2023, the real median household income in the state of Alabama was 60,660 U.S. dollars. The state with the highest median household income was Massachusetts, which was 106,500 U.S. dollars in 2023. The average median household income in the United States was at 80,610 U.S. dollars.

[Updated 28/01/25 to fix an issue in the ‘Lower’ values, which were not fully representing the range of uncertainty. ‘Median’ and ‘Higher’ values remain unchanged. The size of the change varies by grid cell and fixed period/global warming levels but the average difference between the 'lower' values before and after this update is 0.13°C.]What does the data show? This dataset shows the change in annual temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Note, as the values in this dataset are averaged over a year they do not represent possible extreme conditions.The dataset uses projections of daily average air temperature from UKCP18 which are averaged to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare annual average temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.

PeriodDescription 1981-2000 baselineAverage temperature (°C) for the period 2001-2020 (recent past)Average temperature (°C) for the period 2001-2020 (recent past) changeTemperature change (°C) relative to 1981-2000 1.5°C global warming level changeTemperature change (°C) relative to 1981-2000 2°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-2000 3°C global warming level changeTemperature change (°C) relative to 1981-2000 4°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Annual Average Temperature Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Annual Average Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?This data contains a field for the 1981-2000 baseline, 2001-2020 period and each warming level. They are named 'tas annual change' (change in air 'temperature at surface'), the warming level or historic time period, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tas annual change 2.0 median' is the median value for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas annual change 2.0 median' is named 'tas_annual_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas annual change 2.0°C median’ values.What do the 'median', 'upper', and 'lower' values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Annual Average Temperature Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

The mean is the median (synonym: 50. percentile, central value). It is the value above or below which 50% of all cases of a data group are located. The calculation is carried out on outlier-adjusted data collectives. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 20 mg/kg for sand, 40 mg/kg for clay, silt and very silty sand and 60 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

Average and median market, total and after-tax income of individuals by visible minority group, Indigenous group and immigration status, Canada and provinces.

What does the data show?

The dataset is derived from projections of seasonal mean wind speeds from UKCP18 which are averaged to produce values for the 1981-2000 baseline and two warming levels: 2.0°C and 4.0°C above the pre-industrial (1850-1900) period. All wind speeds have units of metres per second (m / s). These data enable users to compare future seasonal mean wind speeds to those of the baseline period.

What is a warming level and why are they used?

The wind speeds were calculated from the UKCP18 local climate projections which used a high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g., decades) for this scenario, the dataset is calculated at two levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), so this dataset allows for the exploration of greater levels of warming.

The global warming levels available in this dataset are 2°C and 4°C in line with recommendations in the third UK Climate Risk Assessment. The data at each warming level were calculated using 20 year periods over which the average warming was equal to 2°C and 4°C. The exact time period will be different for different model ensemble members. To calculate the seasonal mean wind speeds, an average is taken across the 20 year period. Therefore, the seasonal wind speeds represent those for a given level of warming.

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world in the future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected under current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate; the warming level reached will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

What are the naming conventions and how do I explore the data?

The columns (fields) correspond to each global warming level and two baselines. They are named 'windspeed' (Wind Speed), the season, warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. For example, ‘windspeed winter 2.0 median’ is the median winter wind speed for the 2°C projection. Decimal points are included in field aliases but not field names; e.g., ‘windspeed winter 2.0 median’ is ‘ws_winter_20_median’.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, seasonal mean wind speeds were calculated for each ensemble member and then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.

Data source

The seasonal mean wind speeds were calculated from daily values of wind speeds generated from the UKCP Local climate projections; they are one of the standard UKCP18 products. These projections were created with a 2.2km convection-permitting climate model. To aid comparison with other models and UK-based datasets, the UKCP Local model data were aggregated to a 5km grid on the British National grid; the 5km data were processed to generate the seasonal mean wind speeds.

Useful links

Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal.

This dataset includes statistics about durations between two consecutive subtitles in 5,000 top-ranked IMDB movies. The dataset can be used to understand how dialogue is used in films and to develop tools to improve the watching experience. This notebook contains the code and data that were used to create this dataset.

Dataset statistics:

• Average duration between subtitles
• Average duration between subtitles with a duration greater than 10, 30, 60, 120, and 300 seconds
• Maximum duration between subtitles
• Percentage of duration between subtitles from the runtime

Dataset use cases:

• Understanding how dialogue is used in movies, such as the average duration of a dialogue scene and how the duration of dialogue varies between different genres
• Developing tools to improve the watching experience by adjusting the playback speed of dialogue scenes
• Evaluating the effectiveness of tools like the VLC extension mentioned below

Data Analysis:

The next histogram shows the distribution of movie runtimes in minutes. The mean runtime is 99.903 minutes, the maximum runtime is 877 minutes, and the median runtime is 98.5 minutes.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3228936%2F5c78e4866f203dfe5f7a7f55e41f69d0%2Ffig%201.png?generation=1696861842737260&alt=media" alt="">

Figure 1: Histogram of the runtime in minutes

The next histogram shows the distribution of the percentage of gaps (duration between two consecutive subtitles) out of all the movie runtime. The mean percentage of gaps is 0.187, the maximum percentage of gaps is 0.033, and the median percentage of gaps is 327.586.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3228936%2F235453706269472da11082f080b1f41d%2Ffig%202.png?generation=1696862163125288&alt=media" alt="">

Figure 2: Histogram of the percentage of gaps (duration between two consecutive subtitles) out of all the movie runtime

The next histogram shows the distribution of the total movie's subtitle duration (seconds) between two consecutive subtitles. The mean subtitle duration is 4,837.089 seconds and the median subtitle duration is 2,906.435 seconds.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3228936%2F234d31e3abaf6c4d174f494bf5cb86fa%2Ffig%203.png?generation=1696862309880510&alt=media" alt="">

Figure 3: Histogram of the total movie's subtitle duration (seconds) between two consecutive subtitles

Example use case:

The Dynamic Adjustment of Playback Speed (DAPS), a VLC extension, can be used to save time while watching movies by increasing the playback speed between dialogues. However, it is essential to choose the appropriate settings for the extension, as increasing the playback speed can impact the overall tone and impact of the film.

The dataset of 5,000 top-ranked movie subtitle durations can be used to help users choose the appropriate settings for the DAPS extension. For example, users who are watching a fast-paced action movie may want to set a higher minimum duration between subtitles before speeding up, while users who are watching a slow-paced drama movie may want to set a lower minimum duration.

Additionally, users can use the dataset to understand how the different settings of the DAPS extension impact the overall viewing experience. For example, users can experiment with different settings to see how they affect the pacing of the movie and the overall impact of the dialogue scenes.

Conclusion

This dataset is a valuable resource for researchers and developers who are interested in understanding and improving the use of dialogue in movies or in tools for watching movies.

This section presents a discussion of the research data. The data was received as secondary data however, it was originally collected using the time study techniques. Data validation is a crucial step in the data analysis process to ensure that the data is accurate, complete, and reliable. Descriptive statistics was used to validate the data. The mean, mode, standard deviation, variance and range determined provides a summary of the data distribution and assists in identifying outliers or unusual patterns. The data presented in the dataset show the measures of central tendency which includes the mean, median and the mode. The mean signifies the average value of each of the factors presented in the tables. This is the balance point of the dataset, the typical value and behaviour of the dataset. The median is the middle value of the dataset for each of the factors presented. This is the point where the dataset is divided into two parts, half of the values lie below this value and the other half lie above this value. This is important for skewed distributions. The mode shows the most common value in the dataset. It was used to describe the most typical observation. These values are important as they describe the central value around which the data is distributed. The mean, mode and median give an indication of a skewed distribution as they are not similar nor are they close to one another. In the dataset, the results and discussion of the results is also presented. This section focuses on the customisation of the DMAIC (Define, Measure, Analyse, Improve, Control) framework to address the specific concerns outlined in the problem statement. To gain a comprehensive understanding of the current process, value stream mapping was employed, which is further enhanced by measuring the factors that contribute to inefficiencies. These factors are then analysed and ranked based on their impact, utilising factor analysis. To mitigate the impact of the most influential factor on project inefficiencies, a solution is proposed using the EOQ (Economic Order Quantity) model. The implementation of the 'CiteOps' software facilitates improved scheduling, monitoring, and task delegation in the construction project through digitalisation. Furthermore, project progress and efficiency are monitored remotely and in real time. In summary, the DMAIC framework was tailored to suit the requirements of the specific project, incorporating techniques from inventory management, project management, and statistics to effectively minimise inefficiencies within the construction project.

Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -2.590 % in 2021. Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -2.590 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of -2.590 % in 2021 and a record low of -2.590 % in 2021. Colombia CO: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Water temperature (degrees Celsius) and specific conductance (microsiemens per centimeter at 25 degrees Celsius; TC) were measured at U.S. Geological Survey streamgage 05536995, located at Chicago Sanitary and Ship Canal near the Electrical Dispersal Barrier System in Romeoville, Illinois. The TC data were measured every five minutes at four gage height levels above the gage datum (P1 = 21 feet, P2 = 17 feet, P3 = 13 feet, P4 = 9 feet). The gage datum is 551.76 feet above the North American Vertical Datum of 1988 (NAVD 88). Daily mean and five-minutes water temperature and specific conductance data were downloaded from the National Water Information System (NWIS) database and stored in Comma Separated Values (CSV) files. Daily mean data are stored in a CSV file, "05536995-WY2020-temp-speccond-dailymean.csv" and the data measured every five minutes in "05536995-WY2020-temp-speccond-five-minute.csv." The 7-day moving average of daily mean values was calculated in a custom Python script. For each day, the 7-day moving average was calculated as the average of daily mean value for that day and the six previous days. This spreadsheet was saved as a CSV file, "05536995-WY2020-temp-speccond-7daymovavg.csv." The water year 2020 annual minimum, maximum, mean, median, and standard deviation were calculated for each of the four temperature probes and the four specific conductance probes in a custom Python script and saved as a CSV file, "05536995-WY2020-temp-speccond-annualstats.csv". The CSV files containing five-minute, daily mean, 7-day moving average, and annual statistics (minimum, maximum, mean, median, and standard deviation) for water year 2020 can be downloaded from this page.

Graph and download economic data for Real Median Personal Income in the United States (MEPAINUSA672N) from 1974 to 2023 about personal income, personal, median, income, real, and USA.

Water temperature (degrees Celsius) and specific conductance (microsiemens per centimeter at 25 degrees Celsius; TC) were measured at U.S. Geological Survey streamgage 05536995, located at Chicago Sanitary and Ship Canal near the Electrical Dispersal Barrier System in Romeoville, Illinois. The TC data were measured every five minutes at four gage height levels above the gage datum (P1 = 21 feet, P2 = 17 feet, P3 = 13 feet, P4 = 9 feet). The gage datum is 551.76 feet above the North American Vertical Datum of 1988 (NAVD 88). Daily mean and five-minutes water temperature and specific conductance data were downloaded from the National Water Information System (NWIS) database and stored in Comma Separated Values (CSV) files. Daily mean data are stored in a CSV file, "05536995-WY2018-temp-speccond-dailymean.csv" and the data measured every five minutes in "05536995-WY2018-temp-speccond-five-minute.csv." The 7-day moving average of daily mean values was calculated in a custom Python script. For each day, the 7-day moving average was calculated as the average of daily mean value for that day and the six previous days. This spreadsheet was saved as a CSV file, "05536995-WY2018-temp-speccond-7daymovavg.csv." The water year 2018 annual minimum, maximum, mean, median, and standard deviation were calculated for each of the four temperature probes and the four specific conductance probes in a custom Python script and saved as a CSV file, "05536995-WY2018-temp-speccond-annualstats.csv". The CSV files containing five-minute, daily mean, 7-day moving average, and annual statistics (minimum, maximum, mean, median, and standard deviation) for water year 2018 can be downloaded from this page.