Course Content

Learning Statistics with Python

## Learning Statistics with Python

# Conduct 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:

`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())`

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

- Import
`scipy.stats`

using the`st`

alias. - Conduct a t-test with the following setup:
- Samples:
`home_workers`

,`office_workers`

; - Alternative hypothesis: office > home;
- No homogeneity of variances.

- Samples:

Task

- Import
`scipy.stats`

using the`st`

alias. - Conduct a t-test with the following setup:
- Samples:
`home_workers`

,`office_workers`

; - Alternative hypothesis: office > home;
- No homogeneity of variances.

- Samples:

Everything was clear?

# Conduct 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:

`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())`

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

- Import
`scipy.stats`

using the`st`

alias. - Conduct a t-test with the following setup:
- Samples:
`home_workers`

,`office_workers`

; - Alternative hypothesis: office > home;
- No homogeneity of variances.

- Samples:

Task

- Import
`scipy.stats`

using the`st`

alias. - Conduct a t-test with the following setup:
- Samples:
`home_workers`

,`office_workers`

; - Alternative hypothesis: office > home;
- No homogeneity of variances.

- Samples:

Everything was clear?

# Conduct 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:

`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())`

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

- Import
`scipy.stats`

using the`st`

alias. - Conduct a t-test with the following setup:
- Samples:
`home_workers`

,`office_workers`

; - Alternative hypothesis: office > home;
- No homogeneity of variances.

- Samples:

Task

- Import
`scipy.stats`

using the`st`

alias. - Conduct a t-test with the following setup:
- Samples:
`home_workers`

,`office_workers`

; - Alternative hypothesis: office > home;
- No homogeneity of variances.

- Samples:

Everything was clear?

`'work_from_home.csv'`

and `'work_from_office.csv'`

, which contain the monthly task completion counts for each developer.

Let's check if the variances are the same:

`ttest_ind`

to perform a t-test.

Task

- Import
`scipy.stats`

using the`st`

alias. - Conduct a t-test with the following setup:
- Samples:
`home_workers`

,`office_workers`

; - Alternative hypothesis: office > home;
- No homogeneity of variances.

- Samples: