Esyim stats

Step 1: Descriptive statistics

	State	TotalEmployment	AverageHourlySalary	AverageYearlySalary
Median	New York	3120	21	43400
	Indiana	1250	17.5	36345
Mean	New York	11529.66176	24.24594993	50427.71723
	Indiana	4180.712121	20.50909091	42630.33333
standard deviation	New York	26134.41901	13.09405089	27244.63052
	Indiana	9555.009949	11.48998016	23921.97487
Range	New York	274470	81	167400
	Indiana	95100	90	185800

The descriptive statistics used for this case were the median, mean, standard deviation and range. From the results, is possible to see that the total employment is higher in New York than in Indiana. However, the standard deviation is higher in New York than in Indiana. The range for New York total employment is 274470 and for Indiana, it is 95100

New York has an average hourly salary of 21 and a standard deviation of 13.09 meaning it is highly spread which is higher than that of New York. Indiana has an average yearly salary of 17.5 and a standard deviation of 11.49. The range for New York average hourly salary is 81 and for Indiana, it is 90.

New York has an average yearly salary of 43400 and a standard deviation of 27244.63052 meaning it is highly spread which is higher than that of New York. Indiana has an average yearly salary of 36345 and a standard deviation of 23921.97487. The range for New York average yearly salary is 167400 and for Indiana, it is 185800

Step 2: Frequency table for Average hourly salary in Indiana

Bin	Frequency
1-10	50
11-20	373
21-30	153
31-40	55
41-50	16
51-60	1
61-70	5
71-80	2
81-90	2
91-100	3

Used the “Count If” Function to generate a frequency table for average hourly salary. From the frequency table, it can be seen that the average hourly salary is strongly skewed left with more than half of the data concentrated in the lowest four bins

Step 3: t-Test: Two-Sample Assuming unequal Variances

t-Test: Two-Sample Assuming Unequal Variances
	AverageYearlySalary	AverageYearlySalary
Mean	42630.33333	50427.71723
Variance	572260881.8	742269892
Observations	660	679
Hypothesized Mean Difference	0
df	1323
t Stat	-5.5692148
P(T<=t) one-tail	1.54784E-08
t Critical one-tail	1.646006192
P(T<=t) two-tail	3.09567E-08
t Critical two-tail	1.961758651

For the multivariate analysis, the t-Test: Two-Sample Assuming Unequal Variances was performed. The objective was to determine if the difference between the two means is significant. It was hypothesized that there is no difference between the two means. However, with a p-value of approximately zero at 55 level of significance, the null hypothesis should be rejected.

Step 4: Regression Analysis

Add additional analysis done was regression analysis. A scatter plot of Average Hourly Salary of the two states was done in step 4. The scatter plot illustrated that there is a negative relationship between Average Hourly Salary for Indiana state and New York state.

General conclusion

From the our steps above, it can be conclude that the average salary is better in New York than in Indiana. The average hourly rate, the average annual rate and the total employment is better is New York than in Indiana.

A scatter plot of AverageHourlySalary of the two states

AverageHourlySalary 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 25 25 25 25 25 25 25 25 25 25 26 26 26 26 26 26 26 26 26 26 26 26 27 27 27 27 27 27 27 27 27 27 27 27 27 27 27 28 28 28 28 28 28 28 28 28 28 28 28 28 29 29 29 29 29 29 29 29 29 30 30 30 30 30 30 30 31 31 31 32 32 32 32 32 32 32 32 33 33 33 33 33 33 33 33 33 34 34 34 34 34 34 34 35 35 35 35 35 35 35 35 35 36 36 36 36 36 37 37 37 37 37 37 38 39 39 39 40 40 40 40 41 41 41 42 43 43 43 44 44 45 45 46 46 47 48 50 56 62 63 69 70 70 73 79 85 87 93 94 97 17.5 20.50909091 11.48998016 90 0 79 61 61 69 70 58 46 64 66 51 54 51 45 51 47 36 59 28 47 44 61 25 34 30 32 48 58 29 42 35 44 48 34 30 28 29 25 29 31 28 31 29 28 25 33 42 27 33 36 25 31 45 48 63 35 38 21 35 28 15 33 53 35 43 46 25 41 39 37 38 47 42 39 32 24 39 34 28 29 40 41 36 48 40 41 36 34 36 35 36 38 24 28 24 22 24 24 25 24 21 26 24 24 19 29 26 41 32 27 31 27 24 41 38 27 56 37 34 35 35 29 29 40 46 34 26 38 55 35 37 30 34 31 15 34 29 17 19 20 19 32 17 20 28 18 19 20 28 19 18 13 18 23 24 21 24 22 27 14 20 23 18 19 66 33 58 27 32 20 20 24 14 28 21 24 28 22 28 14 18 22 31 24 53 19 27 29 24 31 39 13 27 30 17 24 26 52 32 24 28 20 35 28 29 35 30 33 25 22 22 25 18 27 19 21 31 28 38 70 79 84 35 26 54 47 74 80 89 68 64 89 68 40 62 33 33 30 34 36 22 29 32 26 50 47 27 20 30 25 30 32 29 18 16 14 17 25 19 16 19 17 23 34 21 30 22 34 11 14 16 21 15 21 12 24 16 15 16 16 11 12 16 33 39 37 25 26 25 22 24 25 31 15 27 21 14 22 12 13 13 10 25 15 9 13 13 10 12 10 12 8 9 12 14 10 8 10 10 20 23 13 13 16 13 18 19 20 19 19 11 9 14 11 9 21 11 11 25 12 10 13 26 9 9 14 13 18 12 16 10 11 10 20 12 14 14 21 48 9 11 12 16 12 29 32 55 18 30 39 32 14 17 38 43 13 21 26 13 20 15 16 16 17 11 17 17 12 22 16 23 18 16 19 13 12 15 12 18 16 14 18 13 16 16 18 12 18 17 20 21 21 20 21 14 11 13 23 21 15 15 17 13 15 21 17 14 13 13 18 20 15 21 23 13 11 11 14 15 19 15 11 35 26 26 21 24 27 18 16 26 25 29 21 24 28 25 27 30 24 21 31 21 20 23 28 25 31 21 27 33 17 12 15 12 15 12 16 25 33 15 23 17 21 18 21 21 21 13 16 12 14 15 31 21 24 28 24 18 20 31 17 16 18 24 17 14 18 21 15 21 17 14 16 12 15 11 15 30 22 16 22 17 20 26 22 31 29 21 13 27 23 15 18 13 23 15 17 26 18 12 14 16 18 18 11 12 16 13 18 11 10 13 11 11 15 18 17 17 16 14 15 15 17 15 18 20 16 23 18 16 15 15 21 17 19 16 17 14 18 25 14 15 18 20 17 11 10 11 12 12 16 15 11 12 11 17 28 17 15 16 14 14 15 13 12 13 31 32 28 21 24 34 22 22 20 17 16 12 17 15 15 15 15 16 19 17 14 15 13 15 16 13 19 11 14 14 12 16 14 15 21 11 14 28 23 27 22 11 21 16 14 20 15 13 16 23 24 27 16 25 15 30 9 9 21 16 13 25 20 17 16 12 12 13 10 19 22 21 29 21 24.245949926362279 13.094050888703427 81