An electronics retailer used regression to find a simple...

An electronics retailer used regression to find a simple model to predict sales growth in the first quarter of the new year (January through March). The model is good for 90 days, where x is the day. The model can be written as follows: ? = 103.38 + 2.45x where ? is in thousands of dollars. What would you predict the sales to be on day 90? (Enter an exact number as an integer, fraction, or decimal.)

Transcript

00:01 Okay, we have a regression model and this is the equation of the regression line.

00:08 It is used to make predictions.

00:11 Here the variable x represents the number of the day, for example, second day, the 50th day, the 90th day.

00:20 And then y, if you plug in x and you do this calculation, the answer you get is y and y is to sale.

00:30 Basically the dollars the amount of sales in thousands so if why comes out to be 14 that means 14 ,000 dollars in sales if why comes out to be 22 that means 22 ,000 dollars in sales so you plug in the number of the day into this regression equation and the answer is the predicted amount of sales for that particular day.

01:03 Well, we want to use this regression model to predict the amount of sales on day 90.

01:11 So to predict the amount of sales on day 90, first let's take a little look at this graph.

01:16 We didn't have to graph this.

01:18 We just have to use the equation by one to give you an idea what this looks like.

01:21 So here is your x -axis, you know, which represents the number of the days, zero up to 90.

01:27 You can go past 90, although this one only is good up until 90 days according to the problem.

01:34 Then the vertical axis, the y -axis, that is your sales.

01:39 And don't forget, in thousands.

01:41 So this little notch right here, this point on the y -axis says 100, that really represents $100 ,000 in sales.

01:50 Remember, the y number is sales, but in thousands.

01:54 Okay.

01:55 If we want to predict the amount of sales on day 90, to get an idea of what that looks like using this graph, here's day 90.

02:04 You would follow day 90 straight up until you hit the regression line...

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