The 4 P’s of Marketing include product, place, promotion, and price. “Place” encompasses not only the physical location where products are sold but also the distribution channels and logistics involved in delivering the product to the consumer.

Creating cross-functional teams for the development of products so that they can be properly manufactured and serviced is known as the voice of customer. 

The current Asian / Asian American population in the U.S. is approximately 24.8 million and growing. This group’s purchasing power is $1.3 trillion and growing. As the top Marketing executive for General Motors (a U.S.-based automobile manufacturer and distributor), describe the steps you would take to create a new marketing / advertising campaign to target this specific customer group (to sell more cars and trucks to this group).

_____________ ____________ is the amount of money that individuals have available to spend or save after taxes have been deducted from their gross income.

The buying power of the LGBTQ+ community is approaching $1.5 trillion. This demographic is known to be tech savvy, early adopters, brand loyal, and having higher discretionary income. As a key executive with Samsung Electronics (in its U.S. operations), what specific steps will you take to produce new, innovative products to attract this important consumer group?

Multiculturalism in the U.S. has contributed to increased purchasing power among diverse communities.

If we have two groups of numbers with each having 200 elements and the same average, but different variance, what does it say about the two groups?

The ratio of the group’s percentage of the population and their percentage of the total market share or buying power is also known as their: ______________________________.

A few rows from a dataset are shown below. Assume that the house_price represents the price in dollars (ignore the fact that these prices are unrealistic in today’s market) | square_feet| house_price|property_type |location_grade | |———–:|———–:|:————-|:————–| | 2282| 2604|Townhome |A | | 1624| 1103|Townhome |C | | 2189| 2670|Condo |C | | 1737| 1747|House |C | | 1905| 2428|House |A | | 1549| 1456|Townhome |A | The standard deviation of the column house_price has units of and it is the of the variance.

A few rows from a dataset are shown below. Assume that the house_price represents the price in dollars. (Ignore the fact that these prices are unrealistic in today’s market) | square_feet| house_price|property_type |location_grade | |———–:|———–:|:————-|:————–| | 2282| 2604|Townhome |A | | 1624| 1103|Townhome |C | | 2189| 2670|Condo |C | | 1737| 1747|House |C | | 1905| 2428|House |A | | 1549| 1456|Townhome |A | For the above, I built a model with house_price as the response variable and property_type and square_feet as the explanatory variables. I then computed different variances and standard deviations as shown below. The column names are self-explanatory. | model_var| house_price_sd| residual_sd| residual_var| model_sd| house_price_var| |———:|————–:|———–:|————:|——–:|—————:| | 434547.5| 877.2213| 578.7656| 334969.7| 659.2022| 769517.1|   Based on the above, how much of the variability in the response variable does the model explain? Enter a number between 0 and 1 correct to two decimal places.