Interpret the following three sets of data using two sample t-testing. Each of these columns of data represent a different soda bottling line, with the values representing the amount of soda per bottle in ounces. The business question being asked for each of the three comparisons is "do the two bottling lines bottle the same volume of soda on average, bearing in mind that the sample difference that you may observe could just be coincidence."

Data sets:

1. C7 v C9
2. C7 v C8
3. C7 v C3

(Hint: always check for homogeneity before conducting 2 sample t-tests.)

t-Test: Two-Sample Assuming Equal Variances
	C7	C9
Mean	20.01679	19.98479
Variance	0.010325	0.008904
Observations	50	50
Pooled Variance	0.009615
Hypothesized Mean Difference	0
df	98
t Stat	1.631847
P(T<=t) one-tail	0.052961
t Critical one-tail	1.660551
P(T<=t) two-tail	0.105921
t Critical two-tail	1.984467
t-Test: Two-Sample Assuming Equal Variances
	C7	C8
Mean	20.01679	20.59291
Variance	0.010325	0.010112
Observations	50	50
Pooled Variance	0.010218
Hypothesized Mean Difference	0
df	98
t Stat	-28.4961
P(T<=t) one-tail	1.6E-49
t Critical one-tail	1.660551
P(T<=t) two-tail	3.21E-49
t Critical two-tail	1.984467
t-Test: Two-Sample Assuming Equal Variances
	C7	C3
Mean	20.01679	20.19596
Variance	0.010325	0.065118
Observations	50	80
Pooled Variance	0.044142
Hypothesized Mean Difference	0
df	128
t Stat	-4.73024
P(T<=t) one-tail	2.92E-06
t Critical one-tail	1.656845
P(T<=t) two-tail	5.83E-06
t Critical two-tail	1.978671