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Learning Statistics with Python
course content

Course Content

Learning Statistics with Python

Learning Statistics with Python

1. Basic Concepts
Sample vs PopulationTypes of StatisticsTypes of DataMean ValueMedian ValueMedian Value of the Even Number of ValuesMean or MedianMode ValueDescriptive Statistics Quiz
2. Mean, Median and Mode with Python
Examine the DatasetCalculating Mean and Median Values with PythonStatistics with pandasCalculate the Mean and Median Salary
3. Variance and Standard Deviation
Population VarianceSample VarianceCalculate Variance with PythonStandard deviationStandard Deviation with PythonCalculating Variance and Standard Deviation
4. Covariance vs Correlation
CovarianceCorrelationSelect All Possible ValuesCalculate Covariance and Correlation
5. Confidence Interval
Explore the Data SetConfidence IntervalCalculating Confidence Interval with PythonConfidence Interval Width QuizCalculate 95% Confidence IntervalAdvanced Confidence Interval Calculation with PythonMatch the Functions
6. Statistical Testing
What is t-testHypothesest-test MathematicallyOne-Tailed And Two-Tailed Testt-test AssumptionsPerforming a t-test in PythonConduct a t-testPaired t-test

bookConduct a t-test

A company wants to determine if there is a significant difference in the productivity levels of developers who work from home versus those who work in the office. Good thing you already know a t-test can help with it.

The company has two independent developer teams: one works remotely, and the other works from the office. You've been provided with two files, 'work_from_home.csv' and 'work_from_office.csv', which contain the monthly task completion counts for each developer.

Your task is to conduct a t-test. The company wants to know whether developers who work from the office are more productive than home workers. If so, they will also force the second team to work from the office. In case of home workers being more productive, the company will not make any changes. So the desired alternative hypothesis is "The mean productivity of office workers is greater than that of home workers."

Let's check if the variances are the same:


              
1234567

            
import pandas as pd

home_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_home.csv').squeeze()
office_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_office.csv').squeeze()
# Printing sample standard deviations
print('Home workers std:', home_workers.std())
print('Office workers std:', office_workers.std())
copy

The second standard deviation is twice as much as the first, so variances differ. Recall the function ttest_ind to perform a t-test.

Task

  1. Import scipy.stats using the st alias.
  2. Conduct a t-test with the following setup:
    • Samples: home_workers, office_workers;
    • Alternative hypothesis: office > home;
    • No homogeneity of variances.

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Everything was clear?

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Section 6. Chapter 7
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bookConduct a t-test

A company wants to determine if there is a significant difference in the productivity levels of developers who work from home versus those who work in the office. Good thing you already know a t-test can help with it.

The company has two independent developer teams: one works remotely, and the other works from the office. You've been provided with two files, 'work_from_home.csv' and 'work_from_office.csv', which contain the monthly task completion counts for each developer.

Your task is to conduct a t-test. The company wants to know whether developers who work from the office are more productive than home workers. If so, they will also force the second team to work from the office. In case of home workers being more productive, the company will not make any changes. So the desired alternative hypothesis is "The mean productivity of office workers is greater than that of home workers."

Let's check if the variances are the same:


              
1234567

            
import pandas as pd

home_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_home.csv').squeeze()
office_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_office.csv').squeeze()
# Printing sample standard deviations
print('Home workers std:', home_workers.std())
print('Office workers std:', office_workers.std())
copy

The second standard deviation is twice as much as the first, so variances differ. Recall the function ttest_ind to perform a t-test.

Task

  1. Import scipy.stats using the st alias.
  2. Conduct a t-test with the following setup:
    • Samples: home_workers, office_workers;
    • Alternative hypothesis: office > home;
    • No homogeneity of variances.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 6. Chapter 7
toggle bottom row

bookConduct a t-test

A company wants to determine if there is a significant difference in the productivity levels of developers who work from home versus those who work in the office. Good thing you already know a t-test can help with it.

The company has two independent developer teams: one works remotely, and the other works from the office. You've been provided with two files, 'work_from_home.csv' and 'work_from_office.csv', which contain the monthly task completion counts for each developer.

Your task is to conduct a t-test. The company wants to know whether developers who work from the office are more productive than home workers. If so, they will also force the second team to work from the office. In case of home workers being more productive, the company will not make any changes. So the desired alternative hypothesis is "The mean productivity of office workers is greater than that of home workers."

Let's check if the variances are the same:


              
1234567

            
import pandas as pd

home_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_home.csv').squeeze()
office_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_office.csv').squeeze()
# Printing sample standard deviations
print('Home workers std:', home_workers.std())
print('Office workers std:', office_workers.std())
copy

The second standard deviation is twice as much as the first, so variances differ. Recall the function ttest_ind to perform a t-test.

Task

  1. Import scipy.stats using the st alias.
  2. Conduct a t-test with the following setup:
    • Samples: home_workers, office_workers;
    • Alternative hypothesis: office > home;
    • No homogeneity of variances.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

A company wants to determine if there is a significant difference in the productivity levels of developers who work from home versus those who work in the office. Good thing you already know a t-test can help with it.

The company has two independent developer teams: one works remotely, and the other works from the office. You've been provided with two files, 'work_from_home.csv' and 'work_from_office.csv', which contain the monthly task completion counts for each developer.

Your task is to conduct a t-test. The company wants to know whether developers who work from the office are more productive than home workers. If so, they will also force the second team to work from the office. In case of home workers being more productive, the company will not make any changes. So the desired alternative hypothesis is "The mean productivity of office workers is greater than that of home workers."

Let's check if the variances are the same:


              
1234567

            
import pandas as pd

home_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_home.csv').squeeze()
office_workers = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a849660e-ddfa-4033-80a6-94a1b7772e23/Testing2.0/work_from_office.csv').squeeze()
# Printing sample standard deviations
print('Home workers std:', home_workers.std())
print('Office workers std:', office_workers.std())
copy

The second standard deviation is twice as much as the first, so variances differ. Recall the function ttest_ind to perform a t-test.

Task

  1. Import scipy.stats using the st alias.
  2. Conduct a t-test with the following setup:
    • Samples: home_workers, office_workers;
    • Alternative hypothesis: office > home;
    • No homogeneity of variances.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 6. Chapter 7
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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