From the data in the table it shows the 2019 daily return data for the S&P 500 Index (stocks) and the US 30 year Bond (bonds).How do I test the hypothesis that mean daily return on stocks exceeds the mean daily return on bonds? Please show the steps.S&P500 dailt returns data:DatePriceDaily Return2019-12-313230.780.294602%2019-12-303221.29-0.578082%2019-12-273240.020.003398%2019-12-263239.910.512817%2019-12-243223.38-0.019545%2019-12-233224.010.086614%2019-12-203221.220.494478%2019-12-193205.370.445929%2019-12-183191.14-0.043230%2019-12-173192.520.033529%US 30 Year BondDatePriceDaily Return31-Dec-192.3892.32%30-Dec-192.3350.78%29-Dec-192.317-0.03%27-Dec-192.318-0.16%26-Dec-192.321-0.71%25-Dec-192.3380.15%24-Dec-192.334-0.83%23-Dec-192.3540.31%22-Dec-192.3460.12%20-Dec-192.344-0.43%

Let μ1 is defined as the population mean daily return on stocks, and μ2 is defined as the population…

Answered: How do I test the hypothesis that mean…

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Author:Amos Gilat

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From the data in the table it shows the 2019 daily return data for the S&P 500 Index (stocks) and the US 30 year Bond (bonds).

How do I test the hypothesis that mean daily return on stocks exceeds the mean daily return on bonds? Please show the steps.

S&P500 dailt returns data:

Date	Price	Daily Return
2019-12-31	3230.78	0.294602%
2019-12-30	3221.29	-0.578082%
2019-12-27	3240.02	0.003398%
2019-12-26	3239.91	0.512817%
2019-12-24	3223.38	-0.019545%
2019-12-23	3224.01	0.086614%
2019-12-20	3221.22	0.494478%
2019-12-19	3205.37	0.445929%
2019-12-18	3191.14	-0.043230%
2019-12-17	3192.52	0.033529%

US 30 Year Bond

Date	Price	Daily Return
31-Dec-19	2.389	2.32%
30-Dec-19	2.335	0.78%
29-Dec-19	2.317	-0.03%
27-Dec-19	2.318	-0.16%
26-Dec-19	2.321	-0.71%
25-Dec-19	2.338	0.15%
24-Dec-19	2.334	-0.83%
23-Dec-19	2.354	0.31%
22-Dec-19	2.346	0.12%
20-Dec-19	2.344	-0.43%

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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