Point slope form equation: y - y1 = m(x - x1)
in this case
y - 3 = 1.5(x - 4)
Answer
Point slope form equation:
y - 3 = 1.5(x - 4)
Answer:
459
Step-by-step explanation:
First line:
clock showing 9 o'clock + clock showing 9 o'clock + clock showing 3 o'clock = 9 + 9 + 3 = 21
Second line:
The calculators look exactly the same.
3 * calculators = 30
1 calculator = 10
Third line:
1 bulb + 1 bulb - 1 bulb = 15
Since 1 bulb - 1 bulb = 0, we have 1 bulb + 0 = 15, or
1 bulb = 15
Last line:
Clock showing 9 o'clock = 9
Calculator = 10
3 bulbs = 3 * 15 = 45
Total = 9 + 10 * 45 = 9 + 450 = 459
1) -149, -1; -148, -2; -147, -3
2) No, according to the commutative property of addition, it does not matter the order they are put in.
Hope this helps!
The number of people increased by 2.
This means as a ratio it is now 6/4 as many as originally planned.
6/4 can be reduced to 3/2.
The time and the number of people are inversely proportional, so the food would last 2/3 ( inverse is flipping the original ratio over) the original length of time.
This means the length of time the food lasts is reduced by 1/3, which is 33.3%
Answer:
Here's what I get.
Step-by-step explanation:
1. Representation of data
I used Excel to create a scatterplot of the data, draw the line of best fit, and print the regression equation.
2. Line of best fit
(a) Variables
I chose arm span as the dependent variable (y-axis) and height as the independent variable (x-axis).
It seems to me that arm span depends on your height rather than the other way around.
(b) Regression equation
The calculation is easy but tedious, so I asked Excel to do it.
For the equation y = ax + b, the formulas are

This gave the regression equation:
y = 1.0595x - 4.1524
(c) Interpretation
The line shows how arm span depends on height.
The slope of the line says that arm span increases about 6 % faster than height.
The y-intercept is -4. If your height is zero, your arm length is -4 in (both are impossible).
(d) Residuals

The residuals appear to be evenly distributed above and below the predicted values.
A graph of all the residuals confirms this observation.
The equation usually predicts arm span to within 4 in.
(e) Predictions
(i) Height of person with 66 in arm span

(ii) Arm span of 74 in tall person
