Question

NEED HELP WRITING THE CODE STEP 4..

Step 3: Hypothesis Test for the Population Mean (I)

A relative skill level of 1420 represents a critically low skill level in the league. The management of your team has hypothesized that the average relative skill level of your team in the years 2013-2015 is greater than 1420. Test this claim using a 5% level of significance. For this test, assume that the population standard deviation for relative skill level is unknown. Make the following edits to the code block below:

1. Replace ??DATAFRAME_YOUR_TEAM?? with the name of your team's dataframe. See Step 2 for the name of your team's dataframe.

1. Replace ??RELATIVE_SKILL?? with the name of the variable for relative skill. See the table included in the Project Two instructions above to pick the variable name. Enclose this variable in single quotes.

For example, if the variable name is var2 then replace ??RELATIVE_SKILL?? with 'var2'.

1. Replace ??NULL_HYPOTHESIS_VALUE?? with the mean value of the relative skill under the null hypothesis.

After you are done with your edits, click the block of code below and hit the Run button above.

In [22]: import scipy.stats as st

# Mean relative skill level of your team mean_elo_your_team = your_team_df['elo_n'].mean()

print("Mean Relative Skill of your team in the years 2013 to 2015 =", round(me an_elo_your_team,2))

# Hypothesis Test

# ---- TODO: make your edits here ----

test_statistic, p_value = st.ttest_1samp(your_team_df['elo_n'], 1420)

print("Hypothesis Test for the Population Mean") print("Test Statistic =", round(test_statistic,2)) print("P-value =", round(p_value,4))

Mean Relative Skill of your team in the years 2013 to 2015 = 1347.75

Hypothesis Test for the Population Mean

Test Statistic = -11.97

P-value = 0.0

Step 4: Hypothesis Test for the Population Mean (II)

A team averaging 110 points is likely to do very well during the regular season. The coach of your team has hypothesized that your team scored at an average of less than 110 points in the years 2013-2015. Test this claim at a 1% level of significance. For this test, assume that the population standard deviation for relative skill level is unknown.

You are to write this code block yourself.

Use Step 3 to help you write this code block. Here is some information that will help you write this code block.

Reach out to your instructor if you need help.

1. The dataframe for your team is called your_team_df.
2. The variable 'pts' represents the points scored by your team.
3. Calculate and print the mean points scored by your team during the years you picked.
4. Identify the mean score under the null hypothesis. You only have to identify this value and do not have to print it. (Hint: this is given in the problem statement)
5. Assuming that the population standard deviation is unknown, use Python methods to carry out the hypothesis test.
6. Calculate and print the test statistic rounded to two decimal places.
7. Calculate and print the P-value rounded to four decimal places.

Write your code in the code block section below. After you are done, click this block of code and hit the Run button above. Reach out to your instructor if you need more help with this step.

Answer 1

P value -
let’s toss the coin and calculate p- value ( probability value).
Toss a coin 1st time and result is tail- P-value = 50% (as head and tail have equal probability)
Toss a coin 2nd time and result is tail, now p-value = 50/2 = 25%
and similarly we Toss 6 consecutive time and got result as P-value = 1.5% but we set our
significance level as 95% means 5% error rate we allow and here we see we are beyond that level such as our null- hypothesis
does not hold good so we need to reject and propose that this coin is a tricky coin which is actually.

import numpy as np
your_team_df = dataframe
expenditure = np.random.normal(your_team_df)
np.mean(expenditure)

from scipy.stats import ttest_1samp
import numpy as np

team_mean = np.mean(your_team_df)
print(team_mean)
tset, pval = ttest_1samp(team_mean, 110)
print(“p-values”,pval)
if pval < 0.05: # alpha value is 0.05 or 5%
print(" we are rejecting null hypothesis")
else:
print("we are accepting null hypothesis")

# function for calculating the t-test for two independent samples
def independent_ttest(data1, data2, alpha):
   # calculate means
   mean1, mean2 = mean(data1), mean(data2)
   # calculate standard errors
   se1, se2 = sem(data1), sem(data2)
   # standard error on the difference between the samples
   sed = sqrt(se1**2.0 + se2**2.0)
   # calculate the t statistic
   t_stat = (mean1 - mean2) / sed
   # degrees of freedom
   df = len(data1) + len(data2) - 2
   # calculate the critical value
alpha = 0.05
cv = t.ppf(1.0 - alpha, df)
   # calculate the p-value
   p = (1.0 - t.cdf(abs(t_stat), df)) * 2.0
   # return everything
   return t_stat, df, cv, p