The ratios are equivalent because you multiplied 18:4 by 2&3. And also you divided it by 2. I had the same homework!
Y=mx+b
so is 2
the last number of the equation
<span>You can get those ordered pairs subtracting 9-0.5, then that result minus 0.5 because from the rate the candle reduces its height 0.5inches per each hour.Part B: Is this relation a function? Yes, because each value of x has a single result or output in “y” (image)Part C: Yes, only difference is the time, the candle reduces its height 0.45 inches per each hour, examples of ordered pairs: (0,9) (1,8.55) (2,8.1)</span>
Answer:
units
Step-by-step explanation:
The Line 1 has equation x + y = 5 ....... (1) , and
Line 2 has equation x + y = 3 .......... (2)
Now, we have to find the perpendicular distance between line 1 and line 2.
We can say that (3,0) is a point on line 2 as it satisfies the equation (2).
Now, the perpendicular distance from point (3,0) to the line 1 will be given by the formula
units (Answer)
We know that the perpendicular distance from any external point (
) to a given straight line ax + by + c = 0 is given by the formula 
.
If data for a time series analysis are collected on an annual basis only, then the option(b) seasonal pattern can be ignored
Time series analysis is a specific way of analyzing a sequence of data points collected over an interval of time
The seasonal pattern is refers to the seasonal characteristics of the time series data. It is the predictable pattern that repeats at a certain frequency within one year, such as weekly, monthly, quarterly, etc.
A horizontal pattern exists when the data fluctuate randomly around a constant mean over time.
A trend pattern exists when there is a long-term increase or decrease in the series.
The cyclical component of a time series refers to fluctuations around the trend, excluding the irregular component, revealing a succession of phases of expansion and contraction
Here, the data for a time series analysis are collected on an annual basis, so the seasonal pattern can be ignored
Learn more about Time series analysis here
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