Question

It is claimed that average solar power generation in City B solar farm is 56 kWh (cityB = 56.0). Test this claim using a hypothesis test at 1% level of significance. In order to perform this function, you need to make the appropriate modifications to the provided script. In other words, you should:

Replace ???DATASET_NAME??? with solarkwh

Replace '???VARIABLE_NAME???' with the variable 'cityB'

Replace ???NULL_VALUE??? with the appropriate value for the null hypothesis

Replace '???TEST_TYPE???' with the appropriate alternative hypothesis

Provided code:

print ('Hypothesis Test - Step 7')
n = ???DATASET_NAME???[['???VARIABLE_NAME???']].count()
df = n - 1
mean = ???DATASET_NAME???[['???VARIABLE_NAME???']].mean()
std_dev = ???DATASET_NAME???[['???VARIABLE_NAME???']].std()
null_value = ???NULL_VALUE???
alternative = '???TEST_TYPE???'
print (means_1samp_ttest(mean, std_dev, n, null_value, alternative))
print ('')
My code:

print ('Hypothesis Test - Step 7')
n = solarkwh[['cityB']].count()
df = n - 1
mean = solarkwh[['cityB']].mean()
std_dev = solarkwh[['cityB']].std()
null_value = 0.01
alternative = 'larger'
print (means_1samp_ttest(mean, std_dev, n, null_value, alternative))
print ('')

I am having trouble understanding exactly what they want me to use for the null_value and the alternative

Answer 1

The claim is that average solar power generation in City B solar farm is 56. So, the hypotheses are

H0: average solar power generation = 56

H1: average solar power generation $\ne$ 56

You need to modify the script as below.

print ('Hypothesis Test - Step 7')
n = solarkwh[['cityB']].count()
df = n - 1
mean = solarkwh[['cityB']].mean()
std_dev = solarkwh[['cityB']].std()
null_value = 56
alternative = 'not-equal'
print (means_1samp_ttest(mean, std_dev, n, null_value, alternative))
print ('')