Answer:
2.5
Step-by-step explanation:
Answer:
15
Step-by-step explanation:
<em>here's </em><em>your</em><em> solution</em>
<em> </em><em> </em><em> </em><em> </em><em> </em><em> </em><em> </em><em>=</em><em>></em><em> </em><em>Both </em><em>the </em><em>traingle</em><em> </em><em>are </em><em>similar</em><em> </em><em>hence </em><em>,</em>
<em> </em><em> </em><em> </em><em> </em><em> </em><em>=</em><em>></em><em> </em><em>it's</em><em> </em><em>corresponding</em><em> </em><em>sides</em><em> </em><em>are </em><em>in </em><em>equal </em><em>ratio</em>
<em> </em><em> </em><em> </em><em> </em><em>=</em><em>></em><em> </em><em>so,</em><em>8</em><em>/</em><em>2</em><em>0</em><em> </em><em>=</em><em> </em><em>6</em><em>/</em><em>y </em>
<em> </em><em> </em><em> </em><em> </em><em> </em><em>=</em><em>></em><em> </em><em>8</em><em>y</em><em> </em><em>=</em><em> </em><em>2</em><em>0</em><em>*</em><em>6</em>
<em> </em><em> </em><em> </em><em> </em><em> </em><em>=</em><em>></em><em> </em><em>y=</em><em> </em><em>2</em><em>0</em><em>*</em><em>6</em><em>/</em><em>8</em>
<em> </em><em> </em><em> </em><em> </em><em> </em><em>=</em><em>></em><em> </em><em>y </em><em>=</em><em> </em><em>1</em><em>5</em>
<em> </em><em> </em><em> </em><em> </em><em>hope</em><em> it</em><em> helps</em>
2 and five fifths = 3
I hope that's help. Good night .
Answer:
y intercept is (0 , 2.5)
x intercept is (3.5 , 0)
Step-by-step explanation:
Khan Academy is fun !!!!!!!!!!
y- intercept is where the line meets the y line l
x- intercept is where the line meets the x line ____
The y-intercept is 2.5
The x-intercept is 3.5
Answer: C. there is still not enough evidence to conclude that the time series is stationary.
Step-by-step explanation: First thing to note for a time series plot is that it is required to select a suitable forecast method for the data set being considered.
A stationary time series means that the process generating the data set has a constant mean and the variations are constant over time. This means all evidence is present leading to the conclusion that the entire time series is stationary. A stationary time series thus exhibits an horizontal pattern which enables an appropriate forecast method to be selected for this type of pattern.
A horizontal pattern of a time series plot indicates that a data set fluctuates around a constant mean for a period of time. This period of time may however not be the entire time of the time series or take the entire data set into consideration and might just be a reflection of a portion of the time series hence why it can not be explicitly considered to be stationary. This means that a horizontal pattern can change into a seasonal or trending pattern if more variables/data are added over time.
For instance, a manufacturer sells a certain amount of products over a 10 week period and the resulting pattern of a time series plot is horizontal, then from the 11th week to the 15th week he gets a sharp and continuous increase in sales. This change in level will therefore change the time series plot from horizontal to trending making it more difficult to select a suitable forecast method.
