DATA ANALYSI S Integrate data, calculate sums Calculate summary - PowerPoint PPT Presentation

DATA ANALYSI S • Integrate data, calculate sums • Calculate summary statistics (baywide and by region) • Focusing on detects • Plot data (box and whiskers and maps) SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

DATA ANALYSI S Silver in Sediment 1991 2001 2011 • Integrate data, calculate sums 1 0.9 • Calculate summary statistics (baywide Cumulative Frequency 0.8 and by region) 0.7 0.6 • Focusing on detects 0.5 0.4 • Plot data (box and whiskers and maps) 0.3 0.2 • Exploratory analysis (identify data issues) 0.1 0 0 2 4 6 8 Silver (mg/kg) SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

DATA ANALYSI S • Integrate data, calculate sums • Calculate summary statistics (baywide and by region) • Focusing on detects • Plot data (box and whiskers and maps) • Exploratory analysis (identify data issues) • Comparison to screening values Adverse effects in aquatic organisms (invertebrates and fish) Effects Range Low ( ERL) : Concentration at which effects rarely observed Effects Range Median ( ERM) : Concentration above which effects are frequently observed SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

DATA ANALYSI S • Integrate data, calculate sums • Calculate summary statistics (baywide and by region) • Focusing on detects • Plot data (box and whiskers and maps) • Exploratory analysis (identify data issues) • Comparison to screening values • Statistical analysis (by region and time) • Geospatial analysis to illustrate notable trends SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

DATA ANALYSI S • Integrate data, calculate sums • Calculate summary statistics (baywide and by region) • Focusing on detects • Plot data (box and whiskers and maps) • Exploratory analysis (identify data issues) • Comparison to screening values • Statistical analysis (by region and time) • Geospatial analysis to illustrate notable trends • Comparison to rest of Gulf of Maine SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

RESULTS: 2 0 1 0 -2 0 1 1 DATA BOX PLOTS “Outliers” or extreme values 75 th percentile (3/ 4ths of data fall Whiskers show the Concentration below this line) full range of the data, w/ o outliers Half of the data fall within this range 25 th percentile (1/ 4ths of data fall Median (half the data below this line) below and half above) SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

RESULTS: 2 0 1 0 -2 0 1 1 DATA SEDI MENT GRAI N SI ZE SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

RESULTS: 2 0 1 0 -2 0 1 1 DATA EXAMPLE METALS * * * * * * SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) * : statistically significant differences SEPTEMBER 14, 2016 between regions

RESULTS: 2 0 1 0 -2 0 1 1 DATA SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

RESULTS: 2 0 1 0 -2 0 1 1 DATA PAHS * * * SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) * : statistically significant differences SEPTEMBER 14, 2016 between regions

SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016 RESULTS: 1 9 9 1 -2 0 1 1 CASCO BAY

RESULTS: 1 9 9 1 -2 0 1 1 CASCO BAY METALS * * * * SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) * : statistically significant trend in SEPTEMBER 14, 2016 concentration by time

SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016 LEAD I N CASCO BAY, 1 9 9 1 -2 0 1 1

DATA ANALYSI S Integrate data, calculate sums Calculate summary - PowerPoint PPT Presentation

DATA ANALYSI S Integrate data, calculate sums Calculate summary statistics (baywide and by region) Focusing on detects Plot data (box and whiskers and maps) SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

SI SIMULATION DESI SIGN AND ANALYSI SIS S OF ENERGY- EF EFFICIEN ENT DATA A RED REDISTRI

BANK MARKETING ETING DATA ANALYSI SIS Instructor: Professor Soon Ae Chun Subject Name: BDA761

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BA BAO measure rement fro rom joint anal analysi ysis s of f gal galax axies es and and

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PERENNI AL PROBLEM OF EARTHEN BUND OF W ELLI NGDON RESERVOI R. ANALYSI S OF CAUSES AND REMEDI AL

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DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

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Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Data Preparation Data cleaning Discretization (Data preprocessing) Data

DATA ANALYSI S Integrate data, calculate sums Calculate summary - PowerPoint PPT Presentation

DATA ANALYSI S Integrate data, calculate sums Calculate summary statistics (baywide and by region) Focusing on detects Plot data (box and whiskers and maps) SEDIMENT ASSESSMENT OF CASCO BAY (1991-2011) SEPTEMBER 14, 2016

SI SIMULATION DESI SIGN AND ANALYSI SIS S OF ENERGY- EF EFFICIEN ENT DATA A RED REDISTRI

BANK MARKETING ETING DATA ANALYSI SIS Instructor: Professor Soon Ae Chun Subject Name: BDA761

Using Fully y Homomorphic Encryp yption for St Statistical Analysi sis s of Categori rical,

CRA The M e Mark rket eting ng Plan P n Process ss Situat uation A on Analysi ysis

BA BAO measure rement fro rom joint anal analysi ysis s of f gal galax axies es and and

ICT CT Fra rame mework works for Analysi ysing ng, , Op Optimiz imizing ing and Building

PERENNI AL PROBLEM OF EARTHEN BUND OF W ELLI NGDON RESERVOI R. ANALYSI S OF CAUSES AND REMEDI AL

Ar Arch chitectu tural An Analysi sis f s for or Security ( y (AAF AAFS) S) Jungwoo

Two wo Approa proach ches s to to In Inte terproc procedura dural l Data ta Flow w

Using big data to analyse the global &amp; USA market Charlie Hockless Lead Analyst Offshore

PRINCIPLES FOR THE DESIGN OF MANUALS : A N foundation for PAC is the distinction promoted

A udience Analysis B rainstorm O utline U YoUr Delivery T ech transfer

Consumption value of health &amp; its evolution over time James K. Hammitt Harvard ard Center

Testi ting an and Evol olving g TypeScr Script De Declaration on Files with P Progr

PRE-KICK OFF LOCAL MEETING Inter erdisciplinar disciplinary short t cycle programs ms in

LECTURE 5- FIRMS GROWT H &amp; PROFITABILITY THE GILBRAT LAW Konstantinos Kounetas School of

Ma Machine chine Lear arning ning for r Auton tonomous mous Dr Driving ving Nasser r

Data Preparation Data cleaning Data integration and transformation (Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Data Preparation Data cleaning Discretization (Data preprocessing) Data

Using big data to analyse the global & USA market Charlie Hockless Lead Analyst Offshore

Consumption value of health & its evolution over time James K. Hammitt Harvard ard Center

LECTURE 5- FIRMS GROWT H & PROFITABILITY THE GILBRAT LAW Konstantinos Kounetas School of