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Create Confidence Interval

  |   |   |   -- | -- | -- | --   |   |   |   Situation 1: Create A Confidence Interval   |   |   |   You have completed a test mailing and obtained a response rate. |   |   |   What is the confidence interval? |   |   |     |   |   |     |   |   |   Test Mailing |   | Enter Data |     |   |   |   Proportion Response: |   | 0.5 |   Confidence Level: (80%,85%,90%,95%) |   | 0.9 |   Sample Size: |   | 223000 |     |   |   |   This is a Two Sided Test |   |   |     |   |   |   The Expected Response Rate is Between: | =+E39 | And | =+E38   |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |   Work Area   |   |   |   Percent Response | P= | =+E13 |   Sample Size | N= | =+E15 |     |   |   |   Standard Error = se = sqrt{P*(1-P)/N} | SE | =SQRT(E13*(1-E13)/E15) |     |   |   |   Critical Point | a= | =ABS(NORMSINV((1-E14)/2)) |   Confidence Interval =P +/-a*SE | a*SE | =+E36*E34 |     | P+a*SE | =+E31+E37 |     | P-a*SE | =+E31-E37 |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |     |   |   |  

 


USAA Classification: Internal

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Created Two Sided T-Test

Purpose: Test for Significant Difference

Two tailed test: Indicates a difference on either direction

Enter Data

Test
Control

Test Results:
0.507
0.4728

Confidence Level:
0.8

Sample Size:
25000
25000

p-value=
=IF(2*(1-NORMSDIST(ABS(E39)))<0.0001,"<0.0001",2*(1-NORMSDIST(ABS(E39))))

Answer:
=VLOOKUP(E43,E41:F42,2)

Work Area

Test Proportion
P
=+D9

Control Proportion
T
=+E9

Absolute Difference

=ABS(+E30-E29)

Critical Point
a =
=ABS(NORMSINV((1-D10)/2))

Sample size control
N1
=+D11

Sample Size Test
N2
=+E11

Pooled Sample Proportion
p*
=(+E33D9+E34E9)/(E33+E34)

Pooled Standard Error
SE(ofP*)
=SQRT((E35*(1-E35)(E33+E34))/(E33E34))

Test Statistic
(P-T)/SE(of p*)
=(E29-E30)/E37

TABLE
0
NOT SIGNIFICANT

1
SIGNIFICANT

ANSWER
=IF(ABS(E39)>=E32,1,0)

Design Streamlit UI for Statistical Experiments Module

We need to design the user interface for a new module that will assist users with conducting statistical experiments. The UI will be created in Balsamic for wireframing purposes and implemented in Streamlit for the final interface.

Requirements:

  • The UI should be user-friendly and intuitive for users to navigate through different statistical tools and features.
  • Include options for users to input data, select statistical tests, and visualize results.
  • Ensure the design is clean, modern, and visually appealing to enhance user experience.

Tasks:

  1. Create wireframes in Balsamic for the different screens and functionalities of the module.
  2. Incorporate feedback from stakeholders to refine the UI design.
  3. Implement the final UI design in Streamlit for seamless integration with the statistical experiments module.

Design Command Line Tool for Statistical Experiments Module

We need to design the user interface for a module that assists with statistical experiments. The UI will be created using Balsamic for visualization purposes. The module itself will be a Python command-line tool that utilizes argparse for parsing command-line arguments.

Key Features:

  • Intuitive command-line interface for easy interaction
  • Clear and concise display of statistical experiment options
  • Error handling for invalid inputs
  • Help documentation for users to understand available commands

Please provide mockups and suggestions for the UI design in Balsamic. Your input will be crucial in creating a user-friendly and efficient tool for statistical experiments.

Create Sample Size Calculator

I would like to request the development of a sample size calculator for experiments. This tool should help researchers and scientists determine the appropriate sample size needed for their experiments based on factors such as desired confidence level, margin of error, and population size. Having a reliable sample size calculator will greatly benefit those conducting experiments and ensure that their results are statistically significant. Thank you for considering this request.

Create One Sided T Test

One tailed test: Tests for better treatment performance (use with discretion - only tests one direction) |   |   |   |   |   |   -- | -- | -- | -- | -- | -- | --   | Enter Data |   |   |   |     | Test | Control |   |   |   |   Test Results: | 0.51 | 0.5 |   |   |   |   Confidence Level: | 0.9 |   |   |   |   |   Sample Size: | 100000 | 100000 |   |   |   |     |   |   | p-value= | =IF((1-NORMSDIST(E47))<0.0001,"<0.0001",(1-NORMSDIST(E47))) |   |   Answer: | =VLOOKUP(E51,E49:F50,2) |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |   Should I use a one-tailed test? |   |     |   |   |   |   |   |   Use a one-tailed test if you are interested in an effect in only one direction and not the other, even if you see a large impact in the other direction. Examples:1. Does $25M marketing spend improve product acquisition rate?2. A new tool has been developed that is cheaper than the existing tool. Is it less effective than the existing tool?Sources:https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/j.2041-210X.2010.00014.xhttps://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/*Note that switching to a one-tailed test for the sole purpose of making an otherwise nonsignificant result significant is not appropriate. |   |     |     |     |     |     |     |     |     |     |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |   Work Area   |   |   |   |   |   |   Test Proportion | P | =+D9 |   |   |   |   Control Proportion | T | =+E9 |   |   |   |   Difference |   | =+E37-E38 |   |   |   |   Critical Point | a = | =ABS(NORMSINV(1-D10)) |   |   |   |   Sample size control | N1 | =+D11 |   |   |   |   Sample Size Test | N2 | =+E11 |   |   |   |   Pooled Sample Proportion | p* | =(+E41*D9+E42*E9)/(E41+E42) |   |   |   |     |   |   |   |   |   |   Pooled Standard Error | SE(ofP*) | =SQRT((E43*(1-E43)*(E41+E42))/(E41*E42)) |   |   |   |     |   |   |   |   |   |   Test Statistic | (P-T)/SE(of p*) | =(E37-E38)/E45 |   |   |   |     |   |   |   |   |   |     | TABLE | 0 | NOT SIGNIFICANT |   |     |   | 1 | SIGNIFICANT |   |   |     | ANSWER | =IF(E47>=E40,1,0) |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |     |   |   |   |   |   |  

 


USAA Classification: Internal

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