Answer:
0
Step-by-step explanation:
102500
521
Answer:
Step-by-step explanation:
The main part of the song is the end. The beginning length is based on the end and so is the middle length. The beginning length is 1/6 as long as the end, so if the end is our main unknown, x, the beginning is 1/6x. The middle is one-half as long as the end, so if the end is our main unknown, x, the middle is 1/2x. The sum of the 3 parts is 370, so:

The GCF of those denominators is 6, so multiply through every term by 6 to get rid of the fractions:

Simplifying gives us
6x + x + 3x = 2220 and
10x = 2220 so
x = 222
That means that the end is 222 seconds, the bginning is 37 seconds, and the middle is 111 seconds.
The length of each side of the square is 3 ft.
<u>Step-by-step explanation:</u>
We have , A composite figure is formed by combining a square and a triangle. Its total area is 52.5 ft. The area of the triangle is 43.5 ft . We know that area of a square =
. According to question:
area of square = 
area of triangle = 43.5 ft
total area = 52.5 ft.
Total area = area(square+triangle)
⇒ 
⇒ 
⇒ 
⇒ 
⇒ 
Therefore, the length of each side of the square is 3 ft.
Answer:
Residual = -2
The negative residual value indicates that the data point lies below the regression line.
Step-by-step explanation:
We are given a linear regression model that relates daily high temperature, in degrees Fahrenheit and number of lemonade cups sold.

Where y is the number of cups sold and x is the daily temperature in Fahrenheit.
Residual value:
A residual value basically shows the position of a data point with respect to the regression line.
A residual value of 0 is desired which means that the regression line best fits the data.
The Residual value is calculated by
Residual = Observed value - Predicted value
The predicted value of number of lemonade cups is obtained as

So the predicted value of number of lemonade cups is 23 and the observed value is 21 so the residual value is
Residual = Observed value - Predicted value
Residual = 21 - 23
Residual = -2
The negative residual value indicates that the data point lies below the regression line.