A linear regression analysis between sales (in $1,000 units) and prices (in dollars) resulted in the following equation. You have tested the statistical significant of the estimated coefficient for prices and rejected the null hypothesis the coefficient for prices is equal to zero at the 1% level of statistical significance. The linear regression equation is: sales (in thousands of dollars) = 50,259 - 8.25 * price (in dollars) The estimated equation implies: Select the correct answer Group of answer choices A increase of price of $1 is associated with a $8.25 decline in predicted sales A increase of price of $1 is associated with a $8.25 increase in predicted sales An increase in sales by $1 decreases price by 8.25 dollars A increase in sales by $1 increases price by 8.25 dollars An increase in price by $1 increases sales by 50,259 dollars

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A linear regression analysis between sales (in $1,000 units) and prices (in dollars) resulted in the following equation. You have tested the statistical significant of the estimated coefficient for prices and rejected the null hypothesis the coefficient for prices is equal to zero at the 1% level of statistical significance.

The linear regression equation is: sales (in thousands of dollars) = 50,259 - 8.25 * price (in dollars)

The estimated equation implies:

Select the correct answer

Group of answer choices
A increase of price of $1 is associated with a $8.25 decline in predicted sales
A increase of price of $1 is associated with a $8.25 increase in predicted sales
An increase in sales by $1 decreases price by 8.25 dollars
A increase in sales by $1 increases price by 8.25 dollars
An increase in price by $1 increases sales by 50,259 dollars
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