Hoping to increase pizza sales on Tuesdays, a new pizza restaurant prints and disperses the following coupon through email and fliers in a neighborhood marketing magazine After ten weeks of the coupon offer, the owner of the restaurant needs to decide if the restaurant should continue to run the same special or offer a new one. Use the data collected in the table to determine whether or not the coupon appears to be generating sales for the restaurant.

Week Total

Sales
1 $859
2 $1,176
3 $1,480
4 $1,365
5 $1,576
6 $1,498
7 $1,180
8 $1,964
9 $1,634
10 $1,580
Part A: Create a scatter plot and the line of best fit to represent the data in the table. Be sure to label each axis, as well as to include the proper titles for the scatter plot.
Part B: Use the total sales for weeks 4 and 9 to write an equation for the line of best fit. In your final answer, include all of your calculations.
Part C: in terms of the slope of the equation for the line of best fit, explain whether or not you think the coupon is responsible for the restaurant's increase or decrease in pizza sales on Tuesdays. Use complete sentences in your answer.
Part D: Assuming that the pizza restaurant wishes to continue the coupon for an additional five weeks, use the equation for the line of best fit to predict the restaurant's sales for the Tuesday of the 15th week.

Part A:

Find the attached

Part B:

y = 23.875x + 279.625

Part C:

Total Sales increase from one week to the next by $23.875. Therefore, it is true to say that the paid advertisement is responsible for the restaurant's increase in pizza sales during the first quarter.

Part D:

The restaurant's sales for the Monday of the 20th week is expected to be $757.13

Step-by-step explanation:

Part A:

The scatter-plot can be obtained from stat-crunch by following the given steps below;

The first step is to enter the data given into any two adjacent columns of Stat-crunch application.

Next, click on Graph then Scatter plot

In the pop-up window that appears, select the week column as the X variable and the Total Sales column as the Y variable

Click Compute. Stat-crunch returns the scatter-plot as shown in the attachment below.

Part B:

The total sales for week 5 is given as 399 while that of week 13 is 590. Therefore, we shall determine the equation for the line of best fit using the points;

( 5, 399) and ( 13, 590)

The first step would be to determine the slope of this line;

slope = (590 - 399)/ (13 - 5)

        = 23.875

The equation of the line in slope-intercept form is thus;

Sales = 23.875(Week) + c

We need to determine c, the y-intercept. We can use the point ( 5, 399);

399 = 23.875(5) + c

c = 279.625

The equation of the line of best fit is thus;

y = 23.875x + 279.625

Part C:

The slope of the equation for the lines of best fit was found to be 23.875. This value is positive which implies that the Total Sales increase from one week to the next by $23.875. The slope in regression is defined as the change in y for every unit change in x.

Therefore, it is true to say that the paid advertisement is responsible for the restaurant's increase in pizza sales during the first quarter.

Part D:

The equation  for the line of best fit was found to be;

y = 23.875x + 279.625

where y denotes the total sales and x the corresponding week. We are required to determine y when x is 20;

Y = 23.875(20) + 279.625

y = 757.125

Therefore, the restaurant's sales for the Monday of the 20th week is expected to be $757.13

Step-by-step explanation:

add the table and i will answer correctly

step-by-step explanation:

< xcy ≅ < xby

step-by-step explanation:

in δcax and δbax,

so, δcax ≅ δbax (side-angle-side or sas)

since δcax ≅ δbax, we can conclude < acx ≅ < abx

in δacy and δaby,

so, δacy ≅ δaby (side-angle-side or sas)

since δacy ≅ δaby, we can conclude < acy ≅ < aby

now,

< acy ≅ < aby

=> < acx + < xcy ≅ < abx + < xby

=> < acx + < xcy ≅ < acx + < xby   (since < acx ≅ < abx already proved above)

subtracting < acx from both sides, we get

< acx + < xcy -< acx ≅ < acx + < xby -< acx

cancelling out < acx and -< acx from both sides, we get

< xcy ≅ < xby (proved)