# Please see problem with the so

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.

### Simple linear regression results:

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 estimates:

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

### Analysis of variance table for regression model:

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 so

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

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