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.

When we increase the confidence level (say from 95% to 99%) the width of the confidence interval

Which three of the following do we generally calculate to determine the explanatory power of a model (and therefore also how much the model leaves unexplained)? 

The first figure below shows the distribution of annual incomes of people in some country in the form of a continuous probability density function.  The shaded region corresponds to the area under the curve for x values above $24,960 (average income). A label shows the numeric value of this area.The second figure shows the same probability density function, but with the area under the curve for x-values above $15,700 shaded. Using these figures, what is the probability that someone earns between 15700 and 24960? Write in a number between 0 and 1 correct to two decimal places?  

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