Hypothesis Testing using R - ChiSquareTest

Consider the following treadmill usage data:

Product	Age	Gender	Education	Marital Status	Usage	Fitness	Income	Miles
TM195	18	Male	14	Single	3	4	29562	112
TM195	19	Male	15	Single	2	3	31836	75
TM195	19	Female	14	Partnered	4	3	30699	66
TM195	19	Male	12	Single	3	3	32973	85
TM195	20	Male	13	Partnered	4	2	35247	47
TM195	20	Female	14	Partnered	3	3	32973	66
TM195	21	Female	14	Partnered	3	3	35247	75
TM195	21	Male	13	Single	3	3	32973	85
TM195	21	Male	15	Single	5	4	35247	141
TM195	21	Female	15	Partnered	2	3	37521	85
TM498	19	Male	14	Single	3	3	31836	64
TM498	20	Male	14	Single	2	3	32973	53
TM498	20	Female	14	Partnered	3	3	34110	106
TM498	20	Male	14	Single	3	3	38658	95
TM498	21	Female	14	Partnered	5	4	34110	212
TM498	21	Male	16	Partnered	2	2	34110	42
TM498	21	Male	12	Partnered	2	2	32973	53
TM498	23	Male	14	Partnered	3	3	36384	95
TM498	23	Male	14	Partnered	3	3	38658	85
TM498	23	Female	16	Single	3	3	45480	95

NULL Hypothesis

H0: Miles and Marital.Status are independent

Ha: Miles and Marital.Status are not independent

SOLUTION

attach(mydata);

tabProdGend = table(Miles, Marital.Status)
tabProdGend

#CHI-SQUARE
chisq.test(tabProdGend)

OUTPUT

CONCLUSION

• Since P-value is greater than Alpha, we do not reject the NULL hypothesis
• There is a strong relation between Miles clocked to Marital.Status 

REFERENCES

https://www.greatlearning.in/great-lakes-pgpba/