In: Math
A furniture store has maintained monthly sales records for the past 20 months, with the results shown below.
|
Month |
Sales |
|
1 |
2360 |
|
2 |
1820 |
|
3 |
1760 |
|
4 |
1560 |
|
5 |
1950 |
|
6 |
1950 |
|
7 |
3360 |
|
8 |
1740 |
|
9 |
3780 |
|
10 |
2400 |
|
11 |
2160 |
|
12 |
2760 |
|
13 |
3570 |
|
14 |
2820 |
|
15 |
2800 |
|
16 |
1890 |
|
17 |
2500 |
|
18 |
3630 |
|
19 |
2530 |
|
20 |
3270 |
Assume you have determined there is NO SEASONALITY in this time series. Therefore, you want to fit a linear trend model (that is, trend only) to the data.
Calculate the linear trend equation. (Round coefficients to the nearest whole number.)
y= _+ _ * t
nothing*t
What are the test statistic and p-value to test for a significant trend. Round both to two decimal places.
T =
p-value =
Is the trend significant using a 10% significance level?
Yes
No
What is the value of R-squared? (Round to two decimals.)
Forecast the sales for the next month (t = 21). (Round to the nearest whole number.)
Upper F21=?
Based on the R-squared value, how confident are you in this forecast? (That is, how accurate do you think the forecasts will be?)
A.
Not confident at all because the R-squared value is so low
B.
Very confident because the R-squared value is high
C.
Somewhat confident because the R-squared value is moderate (not extremely high but not extemely low)
Click to select your answer(s).