he table represents an exponential function. A 2-column table has 4 rows. The first column is labeled x with entries 1, 2, 3, 4.
1 answer:
Exponential functions are defined as y = Abˣ, where b > 0 and b≠1. The multiplicative rate of change of the function is (1/5).
<h3>What is an exponential function?</h3>Exponential functions are defined as y = Abˣ, where b > 0 and b≠1. As with every exponential equation, b is known as the base and x is known as the exponent. Bacterial growth is an example of an exponential function. Some germs multiply every hour.
An exponential equation is represented by y=Abˣ, given the table with the values, substitute the values from the first row we will get,
y=Abˣ
2=A x b¹
2 = Ab
A = 2/b
Now, substitute the values in the equation from the second row,
y=Abˣ
Hence, the multiplicative rate of change of the function is (1/5).
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Answer:
15 two-point questions and 5 four-point questions.
Step-by-step explanation:
Let x represent number of two-points questions and y represent number of four-points questions.
We have been given that Janice wants to create a test containing 20 questions. We can represent this information in an equation as:
Since all questions are worth 50 points. We can represent this information in an equation as:
From equation (1), we will get:
Upon substituting this value in equation (2), we will get:
Therefore, there are 5 questions that are worth 4 points each.
Now, we will substitute in equation (1) to solve for x.
Therefore, there are 15 questions that are worth 2 points each.
Answer:
a. the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.
Step-by-step explanation:
If we have N points and we want to adjust a model
We can define the error associated to this like that:
So as we can see here we are adding the square distances between the real and the adjusted values in order to minimize the error for this reason the correct answer is:
a. the line that makes the sum of the squares of the vertical distances of the data points from the line (the sum of squared residuals) as small as possible.
For this case we need to calculate the slope with the following formula:
Where:
And we can find the intercept using this: