Regression analysis is the most elementary tool for statistical analysis.  Typically taken for granted because of its widespread availability as open source or installable as an Excel Add-in under Analysis ToolPak, regression analysis should not be underestimated or undervalued.  By linearly fitting the residual of values to the mean as weighted coefficients, anyone can understand that at some point, certain X variables are correlated to certain Y variables.

Take brand assessment as an example.  Despite data limitations, it is still possible to empirically assess an overall perception of a brand despite not knowing when, where, or how where a brand is valuable.  With regression analysis, we can imply the worth of a brand by assessing its Y variable through controlling variables clustered around Y.  This procedure answers what and why a brand is worth some set of variables.

For some purposes, knowing what and why a brand is valuable is sufficient to answer and provide batteries of questions and solutions.  What brand is worth the most?  What can one do with a brand worth at some value?  What would happen if a brand was removed from the market?  Why is a brand valuable?  Why do people consider brand A more valuable than brand B? Why do people purchase brand A when brand B provides more utility?

For other purposes, knowing what and why a brand is valuable is not sufficient.  Knowing when a brand should be introduced to a market, knowing where a brand should be placed in a market, or knowing how a brand should be supplied to a market are very necessary questions to complete the marketing operation chain.

This goes to say in conservative American lingo：“every tool has a place.”

To demonstrate how regression analysis can assess the worth of a brand, I provide my notes of continuing study on the United States Department of Commerce, Bureau of the Census, Foreign Trade Division TPIS Database：USHS and 2012-2016 American Community Survey 5-Year Estimates.  I examine 2013-2017 exports of the states of Texas and California.  These two states, alone, powerhouse the United States of America accumulating 28% of America’s two most exporting states.  For interest, the following three most exporting states are New York, Washington, and Illinois accumulating 15% of all of America’s exports.  This is a total of five states exporting 43% of all of America’s exports, with forty-five states selling the remaining 57% of America’s exports.

Consider America’s ethnic groups as brands.  There is a “White” person brand, “Black or African American” brand, “American Indian and Alaskan Native” brand, “Asian Indian” brand, “Chinese” brand, “Filipino” brand, “Japanese” brand, “Korean” brand, “Vietnamese” brand, “Other Asian” brand, “Native Hawaiian and other Pacific Islander” brand, “Some other race” brand, “Two or more races” brand, and “Total Pop” American brand.  With these major identifiable “brands” accounted for, the remaining fixed intercept represents all other brands aggregated to a state.

Although these lenses are considered politically incorrect and offensive to some people and organizations, the science of regressing America’s population, ethnic group populations, and remaining state brand must not be ignored.  President Donald Trump and his mutually profitable support teams have not ignored the potential money made from his presidency.  Media, including social, broadcast, commercial, entertainment, and other forms of media, as an industry held very profitable and expansive operations during President Obama’s controversiality with current “MAGA” or Make America Great Again voters and politicians.  The media certainly has not held back in exploiting these market segments that criticized President Obama’s presidency, and the media certainly is not holding back in exploiting these market segments now supporting and operating like President Trump’s Gestapo through night raids and violent takeovers.  As long as America’s media industries do not hold back, I see no reason for any other American to hold back against prolific media industries farming American’s despair and anger as literal cash crops.

When considering the total state population and its ethnic groups as brands in the United States of America, a multivariate regression with linear assumption（i.e. the probability for an additional dollar of export in the state is linear to its population）calculates California with a +5,964（positive）intercept and Texas with a -1,077（negative）intercept.  This suggests these two states have a total of 7,041 brand unit magnitude with California’s brand 5.5 times more valuable than Texas’s brand（conservatively from zero, rather than unit size）.  What these units represent could be seen as cash value as scaled by export dollars, or some other possible unit of measure for brands.  However, comparing total state population with its ethnic group populations confound ethnic groups and total state population because these ethnic groups are also members of the state population, and vis-à-vis the state population contains members of these ethnic groups.  Removing consideration for state population as a brand, Texas’s brand intercept is even lower with -15,560（negative）intercept and California’s reversal to -6,119（negative）intercept.  Confounding exports is the brand variable “Two or more races,” where in California being two or more race has a -525（negative）unit effect on exports and Texas a +4,211（positive）effect on exports.  Removing consideration for “Two or more races” as a brand and focus only on ethnic groups, California’s intercept is -2469（negative）and Texas’s intercept is +6436（positive）.  However, removing “Some other race”（note “White” are classified as “having origins in any of the original peoples of Europe, the Middle East, or North Africa” in the U.S., such as American, Egyptian, Hispanic, and Arab）, California’s intercept is +775（positive）and Texas’s intercept is +439（positive）with California 1.77 times higher brand value than Texas when controlling for race or ethnicity brands.

Considering the most rigorous of observations with minimal confounding factors, California’s state, White ethnic and Chinese ethnic brands have positive effects on exports.  Texas’s state, Black or African American ethnic and Vietnamese ethnic brands have positive effects on exports.  On the contrary to some models, in Texas White ethnic brand and Asian ethnic brand have negative effects on state exports; and in California Filipino ethnic brand and Vietnamese ethnic brand have negative effects on state exports.  The regression models exclusively with identifiable ethnic groups are provided below.

Texas export（in billions） = 439 thousand intercept brand – 486 thousand per White brand unit + 2634 thousand per Black or African American brand unit – 1404 thousand per Asian Indian brand unit + 6660 thousand per Vietnamese brand unit

California export（in billions） = 775 thousand intercept brand + 32 thousand per White brand unit + 691 thousand per Chinese brand unit – 1112 thousand per Filipino brand unit – 1531 thousand per Vietnamese brand unit

Utilizing alternative lenses, instead of a linear relationship between state exports and ethnic group populations, what if there is a convergent relationship between state exports and ethnic group populations?  Illustrated, modeling a value chain, where there are more people working near the beginning of a value chain, there are fewer people working near the ending of a value chain.  Although this is naïve in 2018 due to automated industries and its manufactories, any sustainable human social governance is structured in a competitive manner such that resource accessibility and value creation are ordered by time or aging resource constraints.  So, what if we scaled the model by f ：ln（x）?  The results are follows.

Texas export（in billions） = -4615 ln（x）intercept brand – 933 ln（x）per White brand unit – 1479 ln（x）per Korean brand unit + 965 ln（x）per Vietnamese brand unit + 2126 ln（x）per Native Hawaiian and other Pacific Islander brand unit

California export（in billions） = 27841 ln（x）intercept brand - 900 ln（x）per American Indian and Alaskan Native brand unit + 33 ln（x）Asian Indian brand unit – 674 ln（x）Japanese brand unit – 626 ln（x）Vietnamese brand unit

Both linear and convergent perspectives in relation to state exports to ethnic group population perceive the state of California to have positive brand effects when accounting for ethnic group brands.  The state of Texas is perceived to have a negative brand effect when accounting for ethnic group brands in a convergent perspective.  A possible explanation for this discrepancy：California still significantly utilizes a sustainable ‘pyramid’ model for its workforce despite its reputation seen to host some of America’s most advanced and futuristic cities with assumed widespread use of automated industries and manufactories.  As such, Texas utilizes a more ‘flat’ model for its workforce.  But either way, California’s export brand is seen as more valuable than Texas’s export brand when accounting for ethnic group brands.

Forewarned, these results only help answer what and why one state’s brand export is worth more or less than another state when accounting for ethnic group populations.  On one measure, we do not know which set of variables within ethnic groups are associated with differences in state exports.  It is hopeful to believe 100% of the variables between ethnic group populations and export qualities are explained within a one-year time span.  The moment an ethnic group population increases by one, export amounts also increase, and vice versa the moment an ethnic group population decreases by one, export amounts also decrease.  Such hardworking parents immediately after a child in their ethnic group is born are not necessarily what is increasing or decrease exports.  Illustrated, at observation time（t）, it might be due to children born a year ago（t-1）, or two years ago（t-2）, or maybe even five years ago（t-5）and beyond.  On a different measure, we do not know whether a state’s ethnic group brands cause state branding to be observable.  It is tempting to conclude that, when a state is categorized with ethnic groups, both state brand and ethnic group brand can be observed.  Unfortunately, it may also be a different set of variables causing the error-means association, such as community development of ethnic-dominated industries or foreign-purchaser confidence in buying from state exports when they see more of their family or kin reside in the USA.

Take caution with regression analysis as the descriptiveness of models can significantly, and sometimes dramatically, change.  But for the purposes of this article, snapshots of 28% of America’s exports over five years and two states are incremental contributions to our understanding of the marketing process in business.  As President Donald Trump says, America has a lot of bad trade deals and him and his administration are aiming to ensure America receives a fairer end of the trade deals.  What is driving our nation’s trade is probably misunderstood.  President Trump has a real estate business background that involves purchasing or renting a property at low price, refurbishing the property with as minimal cost as possible, and marketing a property at a higher price than purchased and refurbished.

Relying on President Trump’s real estate experience with America’s successful trade is short-sighted.  As of 2016, no vocal American was completely sure about what was driving our trade exports.  Maybe people just want to purchase Made in America?  Thirty years ago, maybe people have completely forgotten about Made in Japan for high-tech products?  Even nowadays, maybe people do not consider Made in Mexico for nondurable goods a threat to the American economy?  Heck, maybe people are sick of Made in China for manufactured goods?  At least one aspect we do know about President Trump’s administration on trade：in 2015 America lost 119 billion U.S. in exports, and in 2016 lost another 52 billion in exports.  In 2017, America recovered 95 billion US in exports.  We do not know the amount of exports recovered or lost due to President Trump’s administration.  What also happened in 2015 is 4,279 Americans renouncing their citizenships to another nation, and in 2016 5,411 Americans renounced their citizenship, in addition to changes in ethnic group populations.  How much of these changes are actually causing exports we do not know, as a limitation of regression analysis.  Suppose a modern-day thought problem：person A holds weapons that can eliminate person B, and person B is not familiar with person A.  Person A does not know what person B wants, but when person B asks for goods from person A, person A sells such goods to person B.  What factors should we consider?  （i）Rationally, person B should avoid supporting person A as much as possible because person A can eliminate person B at any time.  （ii）Person B does not know person A’s intentions despite person A’s abilities to eliminate person B.  （iii）There are goods that person B wants but cannot attain from anyone other than person A.  （iv）Thus, person B needs to consider what would not make person A eliminate person B while acquiring goods only purchasable from person A.  The situation does not look good for person B.  As such, how can person A seek a mutually beneficial relationship with person B（as the most beneficial ideal situation）, especially when person A can eliminate person B at any time? A mutually beneficial relationship is ideal because person A can benefit from person B’s production and person B’s production can additionally support person A in the long run.  （i）Person A provides apparent handicaps to help reduce paranoia from person B.  （ii）Person A attempts to be more transparent or provides acceptable intentions that are acceptable to work with for person B.  （iii）Person A seeks some benefit such that eliminating person B would not be in person A’s best interests.  （iv）Thus, person A needs to consider what image and transactions would make person B productive and beneficial to person A so person A has an additional layer of stability.  We do not yet know the effects of President Trump’s administration, but we can logically project his actions.  By providing obstacles to non-profitable trade deals at America’s expense, we know President Trump is either operating under a perception that（a）person B no longer cares to be eliminated by person A,（b）person B has capabilities that can eliminate person A, or（c）person A gives up trying to seek a mutually beneficial relationship with person B.  There is always the dunce explanation, but for reason’s sake let us assume people operate in their own or programmer’s best interests.  To balance this equation, it is clear a third party person C, that is China, has been brought into America’s political machinations in person A’s attempt to redirect person B to person C.  Thus, although exports from America have, on average, plateaued, most of the world’s multinational enterprises are hosted by Americans such that exports in a foreign nation can still be owned by America.  America’s stock exchanges still host the world’s most valuable organizations no matter where these organizations conduct business.

In conclusion, the use of regression analysis is still a useful tool to help answer what and why, such as an assessment of brand value in marketing.  In combination with other tools, the questions of when, where, and how too can be answered.