_{®}Salary Report

# Salary Documentation - Flash - Designer - Los Angeles, CA

## Salary Report Documentation

### Information Technology Job

Adobe Flash

Designer

### United States Location

Los Angeles -

- Riverside - Orange County

CA, USA Metropolitan Area

###
Competitive Position_{®} Salary Report

#### Source Documentation

##### Web Site Want Ads

The Salary Report is based on a sample of Want Ads listed in the career web sites:

- CareerBuilder.com
- ComputerJobs.com
- Dice.com
- Craigslist
- Monster.com
- USA Jobs.

Duplicate Want ads found in the same month were eliminated.

Please Note: The career web sites only act as venues for job postings and are not responsible for the content of the want ads. They are not associated with and do not endorse this Salary Report.

#### Adobe - Flash - Designer

##### Collected Past 3 Years

The sample was collected in the 158 weeks between:

- Tuesday April 1, 2008
- Thursday March 31, 2011.

##### Large Number of Want Ads

3,036 Want Ads were collected.

Each want ad listed:

- Salary
- Required Experience
- Qualifications for an Adobe - Flash - Designer
- Location in a US City.

#### Geographic Adjustment

Los Angeles, CA

##### Collected Past 3 Years

The sample was collected in the 158 weeks between:

- Tuesday April 1, 2008
- Thursday March 31, 2011.

##### Large Number of Want Ads

21,383 Want Ads were collected.

Each want ad listed:

- Salary
- Required Experience
- Qualifications for an IT Job
- Location within the Los Angeles, CA metropolitan area.

The metropolitan area as defined by the U.S. Office of Management and Budget (OMB) is applied.

United States of America

##### Collected Past 3 Years

The sample was collected in the 158 weeks between:

- Tuesday April 1, 2008
- Thursday March 31, 2011.

##### Large Number of Want Ads

235,987 Want Ads were collected.

Each want ad listed:

- Salary
- Required Experience
- Qualifications for an IT Job
- Location in a US City.

#### Statistical Documentation

Adobe - Flash - Designer

##### Regression Analysis

The regression equation of the average salary is derived.

The regression equation minimizes the variance of the salaries across the sample of want ads.

##### The Regression Equation

Salary for Adobe - Flash - Designer is lowest at entry level, increases rapidly with the first years of experience and approaches a ceiling as experience matures.

When Microsoft ASP is required Salary is Lower.

When New York-Northern New Jersey-Long Island, NY-NJ-CT-PA is required Salary is Higher.

$25,878 Entry Level Salary average

- $5,352 with Microsoft ASP required

+ $2,967 with New York-Northern New Jersey-Long Island, NY-NJ-CT-PA required

+ ( $22,554 ∗ ln(Number of Years of Experience) )

= Salary Average.

Note: Since the natural logarithm, 'ln', is not defined at zero, +1 is always added to the number of years of required experience.

##### Collinearity Tested

The variables of the regression equation are determined to be independent by a 95% one-tailed t-distribution test.

##### Residuals

Many want ads state a salary that is either greater or less than the Salary Average.

A residual is equal to the difference between the salary offered in a want ad and the salary as calculated by the regression equation for the want ad.

The variance is the sum of the squared residuals for the entire sample of want ads.

##### R Squared Statistic

The R Squared statistic is a measure of the 'goodness of fit' of the regression equation.

It states the percent of the sum of the squared salaries in the sample of want ads calculated by the regression equation:

- 17.07% = R Squared.

The remaining percentage is explained by the variance:

- 82.93% = Sum of Squared Residuals.

An R Squared statistic of 100% would indicate that all want ads offered the average salary. A reasonable degree of variability should be expected due to the many factors influencing individual want ads.

##### t-Distribution Statistical Tests

The t-Distribution is applied to test if a variable within the salary regression equation is equal to zero.

A variable can be insignificant if its standard deviation is too large.

The t-Distribution multilpied by a variable's standard deviation determines the 95% Confidence Interval and the probability the variable is equal to zero salary.

Significant confidence is placed in a regression equation variable when the low point of the 95% Confidence Interval is above zero. Even more confidence is placed when there is little probability that the variable is equal to zero salary.

1.9607 is the factor of the t-Distribution where only 2.5% of the sample of 3,036 want ads have higher values.

The Salary Average, Standard Deviation, 95% Probability Range and Probability of zero salary for each variable:

- Entry Level Salary Average = $25,878
- Standard Deviation = $1,211

- 95% Probability Range = $23,504 to $28,252
- there is less than a one-hundredth of one percent ( < 00.01 % ) probability that the Entry Level salary is equal to zero

- Microsoft ASP = - $5,352
- Standard Deviation = $836

- 95% Probability Range = - $6,990 to - $3,714
- there is less than a one-hundredth of one percent ( < 00.01 % ) probability that the Microsoft ASP qualification determines zero salary

- New York-Northern New Jersey-Long Island, NY-NJ-CT-PA = + $2,967
- Standard Deviation = $956

- 95% Probability Range = $1,093 to $4,841
- there is a 2 tenth of a percent ( 00.2 % ) probability that the New York-Northern New Jersey-Long Island, NY-NJ-CT-PA qualification determines zero salary

- Experience = $22,554 ∗ ln(Number of Years of Experience + 1)
- Standard Deviation = $861

- 95% Probability Range = $20,866 to $24,242
- there is less than a one-hundredth of one percent ( < 00.01 % ) probability that Experience determines zero salary.

##### F-Distribution Statistical Test

The F-Distribution probability considers whether the Salary Average regression equation is statistically equivalent to an equation set to zero.

The regression equation can be insignificant if its standard deviation is too large.>

The lower the F-Distribution probability the more confidence is given to the regression equation:

- The Adobe - Flash - Designer Salary Average equation has less than a one-hundredth of one percent ( < 00.01 % ) probability that it is equal to zero.

##### Heteroscedasticity Correction

The Salary Average regression equation required a correction for Heteroscedasticity.

The residuals are not uniform for all job characteristics:

- the residuals increase at an increasing rate as the years of Experience increase
- the residuals are smaller when Microsoft ASP is required

This additional information was factored into the analysis by dividing each want ad by its level of variance found in the heteroscedasticity regression equation:

- (e^(3.4870 + 1.0654ln(Years)- 0.7288ASP))^.5.

The heteroscedasticity regression equation is verified to have an F-Distribution probability of less than 1 tenth of 1 percent chance ( < 00.10 % ) of not existing.

Each job characteristic of the heteroscedasticity regression equation is verified to have a t-Distribution probability of less than a 1 percent chance of not existing.

##### Standard Deviation

The Standard Deviation is the average residual found in a want ad.

$21,279 = Standard Deviation.

##### Salary Range

The Salary Ranges are calculated by adding ±(Standard Deviation ∗ t-Distribution Statistic) to the Salary Average.

The Salary Range factors are:

- $21,279 = Standard Deviation
- ∗
- t-Distribution Statistics for the 3,036 want ads of the sample =
- 0.1257 for 10% Salary Range
- 0.3187 for 25% Salary Range
- 0.4308 for 33% Salary Range
- 0.6746 for 50% Salary Range
- 0.9676 for 67% Salary Range
- 1.2818 for 80% Salary Range
- 1.6454 for 90% Salary Range
- 1.9607 for 95% Salary Range

##### Experience

The 95% range of Experience is calculated by adding ±(Standard Deviation ∗ t-Distribution Statistic) to the Experience Average:

- 1 Year and 10 Months = Standard Deviation of Experience
- 1.9607 = t-Distribution statistic for 95% Experience Range.

#### Geographic Adjustment

##### City ÷ USA

The Salaries determined by the Adobe - Flash - Designer regression equation are adjusted to the Los Angeles, CA information technology job market.

The Salaries are multiplied by a Geographic Adjustment ratio. This ratio compares the local computer job market to that of the United States of America.

The Geographic Adjustment Ratio is calculated at the Experience requirement of the Salary Report.

##### Ratio

At 7 Years & 7 Months of Experience:

93,793 Salary Average for Los Angeles, CA

÷

93,935 Salary Average for United States of America

99.85 % = Geographic Adjustment Ratio

##### Regression Analysis

A regression equation is derived for both Los Angeles, CA and the United States of America.

The regression equation of the average salary is derived for each location.

Each regression equation minimizes the variance of the salaries across the sample of want ads.

##### IT Job 'Types'

IT Job 'types' are tested as variables in each US City and USA regression equation.

The IT Jobs of the Salary Report are grouped by similar characteristics into IT Job 'types':

Application Developer

- Adobe - ActionScript - Developer
- Adobe - ColdFusion - Developer
- Adobe - Flash - Designer
- AJAX-Javascript - Developer
- ASIC-FPGA-Verilog-VHDL - Design Professional
- C - UNIX - Developer
- Objective-C - Apple iOS-MacOS - Developer
- C - Embedded - Developer
- C++ - Embedded - Developer
- C++ - Windows - Developer
- C++ - UNIX & Windows - Developer
- C++ - UNIX - Developer
- C++ & Java - Windows - Developer
- C++ & Java - UNIX & Windows - Developer
- C++ & Java - UNIX - Developer
- C++ & Java & C# - Windows - Developer
- C++ & Java & C# - UNIX & Windows - Developer
- IBM - COBOL - AIX-System i-z/OS - Developer
- IBM - RPG - System i-iSeries-AS400 - Developer
- IT - Business Systems Analyst
- IT - Software Quality Assurance - Professional
- Java - ME-Android-Mobile - Developer
- Java & JSF-JSP-HTML-XML User Interface - Developer
- Java & JDBC-SQL Database - Developer
- Java & JDBC-SQL DB & JSF-JSP-HTML-XML UI - Developer
- Microsoft - C# - Developer
- Microsoft - C# & Silverlight-Flash-Javascript UI - Developer
- Microsoft - C# & VB.Net - Developer
- Microsoft - C# & VB.Net & Silverlight-Flash-Javascript UI - Developer
- Microsoft - C# & C++ - Developer
- Microsoft - C# & C++ & VB.Net - Developer
- Microsoft C# & Java - Developer
- Microsoft - VB.Net - Developer
- Microsoft - VB.Net & Silverlight-Flash-Javascript UI - Developer
- Microsoft VB.Net & Java - Developer
- Microsoft - ASP.Net - Developer
- Microsoft ASP.Net & Java - Developer
- Perl - Developer
- PHP - Developer
- PHP & Flash - Developer
- Python - Developer
- Ruby on Rails - Developer

System Professional

- Cisco - Network Professional
- Citrix - Server Professional
- EMC NetWorker - Storage Professional
- IBM - WebSphere - Server Professional
- IBM - System i-iSeries-AS400 - System Professional
- IBM - z/OS-OS/390-MVS - System Professional
- IT Change-Configuration-Release Management - Professional
- IT Security - Professional
- IT - Software Trainer
- Microsoft - SharePoint - Server Professional
- Microsoft Windows - Desktop Support Professional
- Microsoft Windows - System Professional
- Microsoft Windows & Cisco - System Professional
- Microsoft Windows & UNIX - System Professional
- Microsoft Windows & UNIX & Cisco - System Professional
- Microsoft Windows & Novell - System Professional
- UNIX - System Professional

Lead System Professional

- System Professional Job with a Manager or Team Lead qualification
- Director - IT Help Desk Support Center

Database Professional

- Informatica - ETL Professional
- IBM - DB2-UDB - Database Professional
- Microsoft - SQL Server - Database Professional
- Microsoft - Access - Database Professional
- MySQL - Database Professional
- Oracle - Database Professional
- Sybase - Database Professional

Business System Professional

- IBM - Cognos - Business Intelligence Professional
- IBM - Lotus Notes - Application Professional
- Microsoft - SQL Server & SSAS-SSIS-SSRS - BI Professional
- MicroStrategy - Business Intelligence Professional
- Oracle - Application Developer
- SAP - Application Professional
- SAP - Business Objects - Business Intelligence Professional
- SAP - Crystal Reports - Business Intelligence Professional
- SAS - Business Intelligence Professional

Information Technology Director

- Director - Information Technology
- Director - IT Application Development

##### IT Job

The top IT Jobs in demand are tested as variables in each US City regression equation.

##### Collinearity Tested

The variables of the regression equation are determined to be independent by a 95% one-tailed t-distribution test.

#### Statistical Documentation

#### Los Angeles, CA

##### IT Job 'Types'

The IT Job 'types' for Los Angeles, CA:

- 60% of want ads = Application Developer
- 8% of want ads = Lead Application Developer
- 14% of want ads = System Professional
- 2% of want ads = Lead System Professional
- 5% of want ads = Database Professional
- 8% of want ads = Business System Professional
- 3% of want ads = Information Technology Director

##### IT Jobs

The top IT Jobs in demand for Los Angeles, CA:

- 7% of want ads = Microsoft C# and Silverlight/AdobeFlash/Javascript UI - Developer
- 6% of want ads = PHP - Developer
- 4% of want ads = Adobe Flash - Designer
- 6% of want ads = IT Software Quality Assurance Professional
- 5% of want ads = Java with JDBC/SQL Database and JSF/JSP/HTML/XML UI - Developer

##### The Regression Equation

Salary for Los Angeles, CA is lowest at entry level, increases rapidly with the first years of experience and approaches a ceiling as experience matures.

$36,091 Entry Level Salary average

+ ( $26,841 ∗ ln(Number of Years of Experience) )

= Salary Average.

Note: Since the natural logarithm, 'ln', is not defined at zero, +1 is always added to the number of years of required experience.

##### R Squared Statistic

The R Squared statistic is a measure of the 'goodness of fit' of the regression equation.

It states the percent of the sum of the squared salaries in the sample of want ads calculated by the regression equation:

- 17.56% = R Squared.

The remaining percentage is explained by the variance:

- 82.44% = Sum of Squared Residuals.

An R Squared statistic of 100% would indicate that all want ads offered the average salary. A reasonable degree of variability should be expected due to the many factors influencing individual want ads.

##### F-Distribution Statistical Test

The F-Distribution probability considers whether the Salary Average regression equation is statistically equivalent to an equation set to zero.

The regression equation can be insignificant if its standard deviation is too large.

The lower the F-Distribution probability the more confidence is given to the regression equation:

- The Los Angeles, CA Salary Average equation has less than a one-hundredth of one percent ( < 00.01 % ) probability that it is equal to zero.

##### Heteroscedasticity Correction

The Salary Average regression equation required a correction for Heteroscedasticity.

The residuals are not uniform for all job characteristics:

- the residuals are larger with the qualifications of an Information Technology Director

This additional information was factored into the analysis by dividing each want ad by its level of variance found in the heteroscedasticity regression equation:

- (e^(4.5526 + 1.6448DirectorPosition))^.5.

The heteroscedasticity regression equation is verified to have an F-Distribution probability of less than 1 tenth of 1 percent chance ( < 00.10 % ) of not existing.

Each job characteristic of the heteroscedasticity regression equation is verified to have a t-Distribution probability of less than a 1 percent chance of not existing.

##### t-Distribution Statistical Tests

The t-Distribution is applied to test if a variable within the salary regression equation is equal to zero.

A variable can be insignificant if its standard deviation is too large.

The t-Distribution multilpied by a variable's standard deviation determines the 95% Confidence Interval and the probability the variable is equal to zero salary.

Significant confidence is placed in a regression equation variable when the low point of the 95% Confidence Interval is above zero. Even more confidence is placed when there is little probability that the variable is equal to zero salary.

1.9601 is the factor of the t-Distribution where only 2.5% of the sample of 21,383 want ads have higher values.

The Salary Average, Standard Deviation, 95% Probability Range and Probability of zero salary for each variable:

- Entry Level Salary Average = $36,091
- Standard Deviation = $679

- 95% Probability Range = $34,760 to $37,421
- there is less than a one-hundredth of one percent ( < 00.01 % ) probability that the Entry Level salary is equal to zero

- Experience = $26,841 ∗ ln(Number of Years of Experience + 1)
- Standard Deviation = $397

- 95% Probability Range = $26,062 to $27,619
- there is less than a one-hundredth of one percent ( < 00.01 % ) probability that Experience determines zero salary.

#### Statistical Documentation

#### United States of America

##### Locations Included

United States of America metropolitan areas:

- Albany - Schenectady - Troy, NY
- Albuquerque, NM
- Allentown - Bethlehem - Easton, PA
- Atlanta, GA
- Austin - San Marcos, TX
- Bakersfield, CA
- Baton Rouge, LA
- Binghamton, NY
- Birmingham, AL
- Boise City, ID
- Boston - Worcester - Lawrence - Lowell - Brockton, MA - NH
- Buffalo - Niagara Falls, NY
- Burlington, VT
- Charleston - North Charleston, SC
- Charlotte - Gastonia - Rock Hill, NC - SC
- Chicago - Gary - Kenosha, IL - IN - WI
- Cincinnati - Hamilton, OH - KY - IN
- Cleveland - Akron, OH
- Colorado Springs, CO
- Columbia, SC
- Columbus, OH
- Dallas - Fort Worth, TX
- Dayton - Springfield, OH
- Denver - Boulder - Greeley, CO
- Des Moines, IA
- Detroit - Ann Arbor - Flint, MI
- El Paso, TX
- Fresno, CA
- Grand Rapids - Muskegon - Holland, MI
- Greenville - Spartanburg - Anderson, SC
- Harrisburg - Lebanon - Carlisle, PA
- Hartford, CT
- Honolulu, HI
- Houston - Galveston - Brazoria, TX
- Huntsville, AL
- Indianapolis, IN
- Jacksonville, FL
- Kansas City, MO - KS
- Knoxville, TN
- Las Vegas, NV - AZ
- Little Rock - North Little Rock, AR
- Los Angeles - Riverside - Orange County, CA
- Louisville, KY - IN
- Madison, WI
- Melbourne - Titusville - Palm Bay, FL
- Memphis, TN - AR - MS
- Miami - Fort Lauderdale, FL
- Milwaukee - Racine, WI
- Minneapolis - St. Paul, MN - WI
- Nashville, TN
- New Orleans, LA
- New York - Northern New Jersey - Long Island, NY - NJ - CT - PA
- Norfolk - Virginia Beach - Newport News, VA - NC
- Oklahoma City, OK
- Omaha, NE - IA
- Orlando, FL
- Palm Beach (West) - Boca Raton, FL
- Philadelphia - Wilmington - Atlantic City, PA - NJ - DE - MD
- Phoenix - Mesa, AZ
- Pittsburgh, PA
- Portland - Salem, OR - WA
- Providence - Warwick - Pawtucket, RI
- Raleigh - Durham - Chapel Hill, NC
- Richmond - Petersburg, VA
- Rochester, NY
- Sacramento - Yolo, CA
- Salinas - Monterey, CA
- Salt Lake City - Ogden, UT
- San Antonio, TX
- San Diego, CA
- San Francisco - Oakland - San Jose, CA
- Santa Barbara - Santa Maria - Lompoc, CA
- Sarasota - Bradenton, FL
- Scranton - Wilkes-Barre - Hazleton, PA
- Seattle - Tacoma - Bremerton ,WA
- Springfield, MA
- St. Louis, MO - IL
- Stockton - Lodi, CA
- Syracuse, NY
- Tampa - St. Petersburg - Clearwater, FL
- Toledo, OH
- Tucson, AZ
- Tulsa, OK
- Washington - Baltimore, DC - MD - VA - WV
- Wichita, KS
- Winston-Salem - Greensboro - High Point, NC
- Youngstown - Warren, OH

Metropolitan areas are defined by the U.S. Office of Management and Budget (OMB).

The statistical reports of the U.S. Census Bureau and other U.S. Departments, Bureaus and Agencies apply these metropolitan areas.

##### IT Job 'Types'

The IT Job 'types' for United States of America:

- 51% of want ads = Application Developer
- 7% of want ads = Lead Application Developer
- 20% of want ads = System Professional
- 2% of want ads = Lead System Professional
- 6% of want ads = Database Professional
- 11% of want ads = Business System Professional
- 3% of want ads = Information Technology Director

##### The Regression Equation

Salary for the United States of America is lowest at entry level, increases rapidly with the first years of experience and approaches a ceiling as experience matures.

$34,742 Entry Level Salary average

+ ( $27,534 ∗ ln(Number of Years of Experience) )

= Salary Average.

Note: Since the natural logarithm, 'ln', is not defined at zero, +1 is always added to the number of years of required experience.

##### R Squared Statistic

The R Squared statistic is a measure of the 'goodness of fit' of the regression equation.

It states the percent of the sum of the squared salaries in the sample of want ads calculated by the regression equation:

- 16.65% = R Squared.

The remaining percentage is explained by the variance:

- 83.35% = Sum of Squared Residuals.

An R Squared statistic of 100% would indicate that all want ads offered the average salary. A reasonable degree of variability should be expected due to the many factors influencing individual want ads.

##### F-Distribution Statistical Test

The F-Distribution probability considers whether the Salary Average regression equation is statistically equivalent to an equation set to zero.

The regression equation can be insignificant if its standard deviation is too large.

The lower the F-Distribution probability the more confidence is given to the regression equation:

- The United States of America Salary Average equation has less than a one-hundredth of one percent ( < 00.01 % ) probability that it is equal to zero.

##### Heteroscedasticity Correction

The Salary Average regression equation required a correction for Heteroscedasticity.

The residuals are not uniform for all job characteristics:

- the residuals are larger with the qualifications of an Information Technology Director

This additional information was factored into the analysis by dividing each want ad by its level of variance found in the heteroscedasticity regression equation:

- (e^(4.7474 + 1.5344DirectorPosition))^.5.

The heteroscedasticity regression equation is verified to have an F-Distribution probability of less than 1 tenth of 1 percent chance ( < 00.10 % ) of not existing.

Each job characteristic of the heteroscedasticity regression equation is verified to have a t-Distribution probability of less than a 1 percent chance of not existing.

##### t-Distribution Statistical Tests

The t-Distribution is applied to test if a variable within the salary regression equation is equal to zero.

A variable can be insignificant if its standard deviation is too large.

The t-Distribution multilpied by a variable's standard deviation determines the 95% Confidence Interval and the probability the variable is equal to zero salary.

Significant confidence is placed in a regression equation variable when the low point of the 95% Confidence Interval is above zero. Even more confidence is placed when there is little probability that the variable is equal to zero salary.

1.96 is the factor of the t-Distribution where only 2.5% of the sample of 235,987 want ads have higher values.

The Salary Average, Standard Deviation, 95% Probability Range and Probability of zero salary for each variable:

- Entry Level Salary Average = $34,742
- Standard Deviation = $220

- 95% Probability Range = $34,311 to $35,173
- there is less than a one-hundredth of one percent ( < 00.01 % ) probability that the Entry Level salary is equal to zero

- Experience = $27,534 ∗ ln(Number of Years of Experience + 1)
- Standard Deviation = $128

- 95% Probability Range = $27,284 to $27,784
- there is less than a one-hundredth of one percent ( < 00.01 % ) probability that Experience determines zero salary.