Answer:
Ethan rollerbladed each day 
kilometers.
Step-by-step explanation:
Given:
Ethan rollerbladed a total of 623 km over d days.
Now, to find the kilometers Ethan rollerblade each day.
Total number of distance rollerbladed = 623 km.
Total number of days = 
<u><em>As, Ethan rollerbladed each day the same distance.</em></u>
Now, to get the distance Ethan rollerbladed in each day we divide total number of distance rollerbladed by total number of days:
Therefore, Ethan rollerbladed each day kilometers.
Answer:
First one
Step-by-step explanation:
3s=5s-6
The equation most closely models the line of best fit for the scatter plot is y = 2.5x
<h3>Equation of a line</h3>
A line is the distance between two points. The equation of a line in point-slope form is given as y =mx + b
- m is the slope
- b is the intercept
Using the coordinate points on the line (3, 5000) and (9, 20000)
Get the slope
Slope = 20000-5000/9-3
Slope = 15000/6 = 2500
Determine the y-intercept
5000 = 2500(3) + b
b = 5000 - 7500
b = -2500
Determine the equation
y = 2.5x - 2.5
Hence the equation most closely models the line of best fit for the scatter plot is y = 2.5x
Learn more on line plot here: brainly.com/question/8989301
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Answer:
Step-by-step explanation:
Since f(x) and g(x) share the same top at (0,0), their difference is only a scale factor, so g(x) = ax² but we need to find a.
To find it, we use the given point of g, (2,1)
So g(2) = 1
meaning that
a·2² = 1
4a = 1
a = 1/4
Answer:
(A)Simple First Order Model with Two Predictor Variables.
Step-by-step explanation:
A multiple linear regression model with k predictor variables X₁, X₂, ..., Xₖ and a response Y, can be written as:
y = βₒ + β₁x₁ + β₂x₂ + ··· βₖxₖ + ∈.
Where:
x₁ , x₂,...xₖ are the predictor variables.
βₒ ,β₁, β₂ ··· βₖ are the Regression Coefficients.
and ∈ are the residual terms of the model
Multiple regression models describe how a single response variable y depends linearly on a number of predictor variables x.
The model:
y = βₒ + β₁x₁ + β₂x₂ + ∈ has two predictor variables. It is also of first order since there is no quadratic term, therefore it is a Simple First Order Model with Two Predictor Variables.
