Yup, the amount of 0's is the exponent. for example 1000= 10^3, hope this helped :)
Step-by-step explanation:
Mechanical advantage can be defined using distances:
Mechanical advantage = input distance / output distance
Or it can be defined using forces:
Mechanical advantage = output force / input force
Solving for the output force:
Output force = mechanical advantage × input force
Plugging in values:
Output force = 2.2 × 202 N
Output force = 444.4 N
Answer:
Step-by-step explanation:
Hi there!
Linear equations are typically organized in slope-intercept form: 
where m is the slope (also called the gradient) and b is the y-intercept (the value of y when x is 0)
<u>1) Plug the gradient into the equation (b)</u>
We're given that the gradient of the line is 4. Plug this into as m:
<u>2) Determine the y-intercept (b)</u>
Plug in the given point (1,10) as (x,y) and solve for b
Subtract 4 from both sides to isolate b
Therefore, the y-intercept of the line is 6. Plug this back into as b:
I hope this helps!
Answer:
a) y = 0.74x + 18.99; b) 80; c) r = 0.92, r² = 0.85; r² tells us that 85% of the variance in the dependent variable, the final average, is predictable from the independent variable, the first test score.
Step-by-step explanation:
For part a,
We first plot the data using a graphing calculator. We then run a linear regression on the data.
In the form y = ax + b, we get an a value that rounds to 0.74 and a b value that rounds to 18.99. This gives us the equation
y = 0.74x + 18.99.
For part b,
To find the final average of a student who made an 83 on the first test, we substitute 83 in place of x in our regression equation:
y = 0.74(83) + 18.99
y = 61.42 + 18.99 = 80.41
Rounded to the nearest percent, this is 80.
For part c,
The value of r is 0.92. This tells us that the line is a 92% fit for the data.
The value of r² is 0.85. This is the coefficient of determination; it tells us how much of the dependent variable can be predicted from the independent variable.
