Which of the following are true of the "linear regression" forecasting method? 1. the data pattern can allow for stationarity. 2. a trend in the data can also be spotted, but that trend must be non-linear. 3. the amount of historical data required is typically in the range of 10-20 observations, though more is generally preferred. 4. a trend in the data can also be spotted, but that trend must be linear. 5. linear regression is the most popular of all forecasting methods. 6. seasonality in the data cannot be accomodated. 7. the forecast horizon can range from short to medium.

this was already solved by me with a different person. if you look it up in the search bar at the top of the page, you will get the answer.

step-by-step explanation:

fa tha first one you gonna add y to both sides

it look like this

x-y+y=6+y

x=y+6

therefore your answer is

x =y+6

fa tha second one

you gonna flip tha equation

it gonna look like x-4y=y

then add 4 to both sides

it gonna look like tha

x-4 +4 =y +4

so therefore tha answer is x=y+4

step-by-step explanation:

i you goofed up when writing this, because hnot is not a word minusf is not either. i cannot answer   because of that.

step-by-step explanation: