u9a2
Airport Linear Regression Analysis
Larp Wilder
Capella University
Introduction
The data set presented the percentage of flight delay during arrival and departure from 13 airports The information is from the Federal Aviation Administration. The assumption of the data is normally distributed with the level of significance at 5%. The below analysis determines whether there is a positive linear relationship between late arrivals and late departures.
Visual displays of data of 13 airport arrival and departure flight delay
Null Hypothesis and Alternate Hypothesis Formulations
Ho There is no linear relationship exists between arrival and departures
Ha There is a significant linear relationship exists between arrivals and departures.
Linear Regression Analysis.
The level of significance using to analyze the data of the below correlation analysis and regression is normally distributed with the level of significance at 5% (alpha = 0.05).
Table 1
Linear Regression
Regression Statistics
Multiple R
0.896675
R Square
0.804027
Adjusted R Square
0.786211
Standard Error
1.719714
Observations
13
ANOVA
Degrees of Freedom
Sum of Squares
Mean of Squares
F-Ratio
Significance F
Regression
1
133.468
133.468
45.130
0.00003
Residual
11
32.532
2.957
Total
12
166
Coefficients
Standard Error t-Stat P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
1.840
2.892
0.636
0.538
-4.524
8.205
-4.524
8.205
X Variable1
0.940
0.140
6.718
0.000
0.632
1.248
0.632
1.248
A significant relationship exists between arrivals delay and departures delay (r-squared = 0.804027; p < 0.05). The null is therefore rejected, and the alternative hypothesis retained.
Figure 1
Scatter Plot Showing The Relationship Between Arrivals Delay and Departures Delay
The relationship between percentage of flight arrival delay and flight departure delay from the 13 airports visually present a significant linear relationship exists between arrivals and departures. There is no
Larp Wilder
Capella University
Introduction
The data set presented the percentage of flight delay during arrival and departure from 13 airports The information is from the Federal Aviation Administration. The assumption of the data is normally distributed with the level of significance at 5%. The below analysis determines whether there is a positive linear relationship between late arrivals and late departures.
Visual displays of data of 13 airport arrival and departure flight delay
Null Hypothesis and Alternate Hypothesis Formulations
Ho There is no linear relationship exists between arrival and departures
Ha There is a significant linear relationship exists between arrivals and departures.
Linear Regression Analysis.
The level of significance using to analyze the data of the below correlation analysis and regression is normally distributed with the level of significance at 5% (alpha = 0.05).
Table 1
Linear Regression
Regression Statistics
Multiple R
0.896675
R Square
0.804027
Adjusted R Square
0.786211
Standard Error
1.719714
Observations
13
ANOVA
Degrees of Freedom
Sum of Squares
Mean of Squares
F-Ratio
Significance F
Regression
1
133.468
133.468
45.130
0.00003
Residual
11
32.532
2.957
Total
12
166
Coefficients
Standard Error t-Stat P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
1.840
2.892
0.636
0.538
-4.524
8.205
-4.524
8.205
X Variable1
0.940
0.140
6.718
0.000
0.632
1.248
0.632
1.248
A significant relationship exists between arrivals delay and departures delay (r-squared = 0.804027; p < 0.05). The null is therefore rejected, and the alternative hypothesis retained.
Figure 1
Scatter Plot Showing The Relationship Between Arrivals Delay and Departures Delay
The relationship between percentage of flight arrival delay and flight departure delay from the 13 airports visually present a significant linear relationship exists between arrivals and departures. There is no
References: Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2015). Modern Business Statistics with Microsoft® Excel (5th ed.). Stamford, CT: Cengage Learning.