Question

The manufacturer of a light fixture believes that the dollars spent on advertising, the price of the fixture and the number of retail stores selling the fixture in a particular month influence the light fixture sales. The manufacturer randomly selects 10 months and performs a regression analysis - part of the output appears below. The sales are in thousands of units per month, the advertising is given in hundreds of dollars per month, the price is the unit retail price in a given month.



What is the best interpretation of the coefficient of advertising? Be VERY careful with the units!!

Group of answer choices

For every $1000 in sales each month we expect advertising to increase by $820.20 on average, everything else being constant.

For every $100 spent in advertising in a month we expect sales of the light fixture to increase by 0.8202 fixtures on average, everything else being constant.

For every $100 in sales each month we expect advertising to increase by $820.20 on average, everything else being constant.

For every $100 spent in advertising in a month we expect sales of the light fixture to increase by 820.2 fixtures on average, everything else being constant.

Answer 1

Correct option:

For every $100 spent in advertising in a month we expect sales of the light fixture to increase by 820.2 fixtures on average, everything else being constant.

Explanation:

The slope coefficient of the Independent Variable: the dollars spent on advertising is 0.8202

The Independent Variable is the dollars spent on advertising,

The advertising is given in hundreds of dollars per month

The Dependent Variable is the light fixture sales

The sales are in thousands of units per month,