A scatter plot is shown with the title Jordans Hot Chocolate Sales. The x axis is labeled High Temperature and the y axis is labeled Cups of Hot Chocolate Sold. Data points are located at 20 and 20, 30 and 18, 40 and 20, 35 and 15, 50 and 20, 45 and 20, 60 and 14, 65 and 18, 80 and 10, 70 and 8, 40 and 2.

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Answer:

First Part;

As the temperature in the city increases, the number of cups of hot chocolate sold decreases

Second Part;

The approximate value of the slope in number of cups sold per change in temperature is -0.12222

The approximate value of the y-intercept is 20.944 degrees temperature

Step-by-step explanation:

Question;

First Part

What is the relationship between temperature in the city and the number of cups of hot chocolate sold, using your own words

Second Part

How can the line of best fit be made. Give the approximate values of the slope and the y-intercept of the line of best fit

Explanation

The data points are presented in the following table;

[tex]\begin{array}{lcl}x&&y\\20&&20\\30&&18\\40&&20\\35&&15\\50&&20\\45&&20\\60&&14\\65&&18\\80&&10\\70&&8\\40&&2\end{array}[/tex]

A scatter plot is formed from the given data using MS Excel

First part;

From the values of the number of cups sold, y, and the temperature at which they are sold, x, there is correlation between the x and y values, such that as the temperature increases, the number of cups sold decreases, the becomes more rapid as the temperature increases past 50, such that the reason for taking the hot chocolate decreases with increasing temperature

Second Part;

The line of best fit can be found using the least squares regression formula to draw a straight line from the regression equation as follows;

y = a + b·x

[tex]b = \dfrac{\sum \left(x_i - \bar x\right) \times \left(y_i - \bar y\right) }{\sum \left(x_i - \bar x\right )^2 }[/tex]

a = [tex]\bar y[/tex] - b·[tex]\bar x[/tex]

Where;

b = The slope

a = The y-intercept

From MS Excel, we have;

[tex]\bar y[/tex] = 15, [tex]\bar x[/tex] = 48.63636

[tex]\sum \left(x_i - \bar x\right) \times \left(y_i - \bar y\right) = -410[/tex]

[tex]\sum \left(x_i - \bar x\right )^2 = 3354.545[/tex]

Therefore;

[tex]b = \dfrac{-410}{3354.545} = -0.12222[/tex]

The approximate value of the slope, b = -0.12222 (number of cups sold per unit change in temperature)

a = 15 - (-0.12222)×48.63636 ≈ 20.944

The approximate value of the y-intercept, a = 20.944 (in degrees of temperature).

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