Most individuals are aware of the fact that the average annual repair cost for an automobile depends on the age of the automobile. A researcher is interested in finding out whether the variance of the annual repair costs also increases with the age of the automobile. A sample of 26 automobiles 4 years old showed a sample standard deviation for annual repair costs of $171.18 and a sample of 23 automobiles 2 years old showed a sample standard deviation for annual repair costs of $118.75. Let 4 year old automobiles be represented by population 1. The data is located in the Microsoft Excel Online file below.

Car Age and Repair Cost ($)			F-Test Two-Sample for Variances
4 years	2 years
788	637			4 years	2 years
839	431		Mean	784.6154	450.7826
778	454		Variance	29301.93	14101.54
791	404		Observations	26	23
889	410		df	25	22
791	713		F	2.077924
775	573		P(F<=f) one-tail	0.043639
943	469		F Critical one-tail	2.019644
1013	155
943	419
1060	399		Significance Level (alpha)	0.05
405	524
955	396		Test Statistic (2 decimals)	2.08
874	548
886	396		Critical Value (2 decimals)	2.02
336	598
733	391		p-value (4 decimals)	0.0436
654	336
507	370		Reject the null hypothesis?	Yes
823	455
708	356
808	365
898	569
790
801
612

State the null and alternative versions of the research hypothesis that the variance in annual repair costs is larger for the older automobiles.

H ₀ : ²₁

H _a : ²₁

2. Conduct the hypothesis test at a 0.01 level of significance.

Calculate the value of the test statistic (2 decimals).

2.08

Calculate the critical value (2 decimals).

2.02 <------- This is flagging as incorrect even inputting the formula given.

Calculate the p-value (4 decimals).

0.0436

Reject null hypothesis? No