I am conducting research on the relationship between the number of flights per quarter versus the number of runway incursions. The data collected and organized is broken into quarters within each fiscal year. My proposed hypotheses are:

Ha: There is statistical significance between the number of runway incursions between FY 2018 and FY 2021 and the number of annual flights.

Ho: There is no statistical significance between the number of runway incursions between FY 2018 and FY 2021 and the number of annual flights.

Fiscal Year	Total Number of Flights	Quarters Q1-Q4
18-1	1952648	403
18- 2	2152072	482
18-3	2203077	493
18-4	2092178	440
19-1	2004939	382
19-2	2191370	445
19-3	2243908	486
19-4	2156495	419
20-1	1977447	295
20-2	699518	217
20-3	1226146	330
20-4	1260181	318
21-1	1307735	296
21-2	1697945	485
21-3	1936988	476
21-4	1245026	398

The results I got from running a simple linear regression with a significance level of 0.05 set are as follows:

Simple linear regression results:

Dependent Variable: Quarters Q1-Q4 Independent Variable: Flights FY 18 - 21 Quarters Q1-Q4 = 286.25992 + 0.000068146655 Flights FY 18 - 21 Sample size: 16 R (correlation coefficient) = 0.51227147 R-sq = 0.26242206 Estimate of error standard deviation: 75.070936

Parameter estimates:

Parameter	Estimate	Std. Err.	DF	95% L. Limit	95% U. Limit
Intercept	286.25992	53.390023	14	171.74971	400.77013
Slope	0.000068146655	0.000030534043	14	0.0000026576453	0.00013363566

Analysis of variance table for regression model:

Source	DF	SS	MS	F-stat	P-value
Model	1	28071.402	28071.402	4.9810448	0.0425
Error	14	78899.035	5635.6454
Total	15	106970.44

Would you make any recommendations on organizing the data and does the outcome make sense? The resultant R (correlation coefficient) = 0.5122 with a p-value of 0.0425 is less than 0.05 which means that we reject the null hypothesisthat there is no statistical significance between the number of runway incursions between FY 2018 and FY 2021 and the number of annual flights.