A regression analysis between sales (in $1000) and advertising (in $100) yielded the least squares line = 75 +6x. This implies that if $800 is spent on advertising, then the predicted amount of sales (in dollars) is:Group of answer choices$487 500.$12 300.$123 000.$4875.

A regression analysis between y, sales (in $1000) and x, advertising (in $) yielded the least squares line  = 60 + 5x. We can interpret the slope by saying that we estimate for each extra $1 spent on advertising that sales will increase by $5 000, on average.Group of answer choicesTrueFalse

Regression analysis was applied between sales (in $1,000) and advertising (in $100), and the following regression function was obtained.y-hat = 87 + 7.8 xBased on the above estimated regression line, if advertising is $11,924, then the point estimate for sales (in dollars) is

Suppose the linear regression line y = 2.1x + 130 predicts sales based on the money spent on advertising. If x represents the dollars spent in advertising, and y represents the company sales in dollars, about how much can the company expect in sales if it spends $50 in advertising?A.$650B.$105C.$323D.$235

In order to analyze sales as a function of advertising expenses, the sales manager developed a simple regression model. The model included the following equation, which was based on 32 monthly observations of sales and advertising expenses with a related coefficient of determination of .90.Sales = $10,000 + (2.5 × Advertising expenses)If the advertising expenses in 1 month amounted to $1,000, the related point estimate of sales would be

A regression model between sales (y in Php 1,000), unit price (x1 in Pesos ) and online  advertisement (x2 in Pesos ) resulted in the following function: y = 7 – 3x1 + 5x2The coefficient of the unit price indicates that if the unit price is increased by PhP 1, holding advertising constant, then sales are expected to ___.Group of answer choicesdecrease by PhP 3,000increase by PhP 4,000decrease by PhP 3increase by PhP 3 PreviousNext