Answer:
y=mx+b y=4/3x-3
Step-by-step explanation:
b= your y-slope which is -3
from -3. Count your rise until it reaches an equal run
3= run (count left or right)
4 = rise (count up or down)
y=
y=4/3x-3
Answer:
im not sure
Step-by-step explanation:
im not sure
Answer: 3.5
Step-by-step explanation:
Log(10)(3.5) = 3.5
log(10)=1
1(3.5) = 3.5
The common logarithm, or log base 10, of 3.5 is about 0.544068. However, when rounded to two decimal places, it is 0.54.
The question is asking for the value of log_(10)(3.5). To find this, you would need to use a calculator that has a logarithm function. This value does not have a simple, exact decimal representation, but it can be approximated. Using a calculator, we find the common logarithm, or log base 10, of 3.5 to be approximately 0.54.
However, the question asks us to round this answer to two decimal places. So, using the rules of rounding numbers, we can round 0.544068 to 0.54.
Remember, when you're rounding numbers, if the digit after the place to which you're rounding is 5 or greater, you round up, otherwise, you round down.
Note: Before the days of calculators, we used log tables. Nowadays, we have a simple and easy way to calculate logs - calculators! If you are using a scientific or graphing calculator, there is typically a 'log' button that enables you to calculate logs at the push of a button.
Learn more about Logarithm here:
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Answer:
(7x²y + 4xy²+ y³ + 1) is the answer.
Step-by-step explanation:
We have to write the simplified form of the given expression.
(6x²y + 4xy² - 2y³ + 6) + (x²y + 3y³ - 5)
We will collect the similar terms first
(6x²y + x²y) + 4xy²- (2y³ - 3y³) + (6 - 5)
= 7x²y + 4xy²+ y³ + 1
Therefore, (7x²y + 4xy²+ y³ + 1) will be the answer.
A) linear
B) power
C) exponential
D) linear and exponential
Answer-
Exponential regression line is the best fit for the data.
Solution-
Taking
x = input variable,
y = output variable.
Taking the data from the table, regression models were generated using Excel.
As shown in the attachments, the co-efficient of determination (R²) is maximum for Exponential Regression model or more closer to 1.
As,
The more closer the value of R² to 1, the better the regression model and the best fit line is.
In general also, when we consider growth or decay, we follow the exponential function approach.
Therefore, the exponential regression models should be followed and so exponential regression line is the best fit for the data.