Please see problem with the solution: after reviewing the information below, assess the appropriateness and accuracy of using a linear regression model. Discuss the meaning of the standard error of the estimate and how it affects the predicted values of Y for that analysis.
To predict the air travel industries future, I looked at the 2018 and 2019 travel data from the Bureau of Transportation Statistics (BTA, 2019). With a breakdown in month to month data, I am not sure if this is a good model. If I was to try and predict 2020’s numbers through the rest of the year, my model would be way off with the Corona Virus halting most travel. And a month over month analysis, without Corona say, is probably not good enough. I couldn’t use March of 2018 to predict November of 2019. Below is an analysis of the two years for review.
Dependent Variable: 2018
Independent Variable: 2019
2018 = 0.93316444 + 0.9520346 2019
Sample size: 12
R (correlation coefficient) = 0.98390126
R-sq = 0.9680617
Estimate of error standard deviation: 1.4640167
| Parameter | Estimate | Std. Err. | Alternative | DF | T-Stat | P-value |
|---|---|---|---|---|---|---|
| Intercept | 0.93316444 | 4.8161538 | ≠ 0 | 10 | 0.19375719 | 0.8502 |
| Slope | 0.9520346 | 0.054683605 | ≠ 0 | 10 | 17.409873 | <0.0001 |
| Source | DF | SS | MS | F-stat | P-value |
|---|---|---|---|---|---|
| Model | 1 | 649.65572 | 649.65572 | 303.10367 | <0.0001 |
| Error | 10 | 21.43345 | 2.143345 | ||
| Total | 11 | 671.08917 |
Please see problem with the solution: after reviewing the information below, assess the appropriateness and accuracy of using a linear regression model. Discuss the meaning of the standard error of the estimate and how it affects the predicted values of Y for that analysis.
Many people are also using online shopping to avoid going to stores in person. I decided to use my own data. I am going to add how many Amazon transactions I have made each month from March- August.
X= the month
Y= amount of purchases
Simple linear regression results:
Dependent Variable: sale
Independent Variable: month
sale = 0.53333333 + 1.6 month
Sample size: 6
R (correlation coefficient) = 0.99410024
R-sq = 0.98823529
Estimate of error standard deviation: 0.36514837
Parameter estimates:
|
Parameter |
Estimate |
Std. Err. |
Alternative |
DF |
T-Stat |
P-value |
|
Intercept |
0.53333333 |
0.50269117 |
≠ 0 |
4 |
1.0609562 |
0.3485 |
|
Slope |
1.6 |
0.087287156 |
≠ 0 |
4 |
18.330303 |
<0.0001 |
Analysis of variance table for regression model:
|
Source |
DF |
SS |
MS |
F-stat |
P-value |
|
Model |
1 |
44.8 |
44.8 |
336 |
<0.0001 |
|
Error |
4 |
0.53333333 |
0.13333333 |
||
|
Total |
5 |
45.333333 |
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.
Read moreEach paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.
Read moreThanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.
Read moreYour email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.
Read moreBy sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.
Read more
Recent Comments