E-commerce giants design and run frequent campaigns on their touchpoints which also includes websites to attract more and more customers. The purpose of this paper is to investigate the effectiveness of a newly launched web page for consumers and find out if the new page is resulting in different consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’ helps us find out if the different user groups of old and new web page are significantly different from each other based on conversion rate or not!
Predictive analytics targets data to predict if ATL advertising is more effective than BTL advertising and to target customer segments and characteristics.
Frequent pattern mining is an analytical algorithm that is used by businesses and, is accessible in some self-serve business intelligence solutions. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
Building solid marketing strategies in today’s competitive market is impossible without sound market research. The right market information can boost your sales, position your product more effectively, and help you speak more effectively to your audience.
Reinforce and focus your marketing research skills. This highly interactive program, facilitated by an experienced marketing research professional, can provide you with the knowledge and tools you need to develop and manage research projects to meet your specific goals. Furthermore, the workshop debunks the myth that you have to spend a lot of money to gain valuable information for decision making. No prior marketing research experience is required!
The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples. It is helpful when an organization wants to determine whether there is a statistical difference between two categories or groups or items and, furthermore, if there is a statistical difference, whether that difference is significant.
Customer Satisfaction Data - Multiple Linear Regression Model.pdfruwanp2000
In this project, we will discuss the results of our analysis of customer satisfaction
conducted using R Studio. Our team did this by carefully analyzing a number of factors that would have an effect on customer happiness of that certain company, such as Complaint
Resolution, Delivery Speed, Order Billing, Warranty Claims, Technical Support, E- commerce, Product Quality, Sales Force Image, Advertising, Price, and Product Line.
We performed a thorough study using a random sample of 70 data points, using a pre- chosen seed value which we obtained from the largest student number in our group. To
ensure the accuracy of our results, we replace missing values of the dataset with the mean of the dataset.
Bing Ads' Eric Couch dives in to beginning and advanced Excel tips and tricks for PPC marketers- including data analysis tips, Excel formulas, and incredibly handy plugins.
ANOVA is a hypothesis testing technique used to compare the equali.docxjustine1simpson78276
ANOVA is a hypothesis testing technique used to compare the equality of means for two or more groups; for example, it can be used to test that the mean number of computer chips produced by a company on each of the day, evening, and night shifts is the same. Give an example of an application of ANOVA in an industrial, operations, or manufacturing setting that is different from the examples provided in the overview. Discuss and share this information with your classmates.
In responding to your peers, select responses that use an ANOVA application that is different from your own. Are the results of the ANOVA application statistically significant? Why are the results significant or not significant? Explain your reasoning. Consider how ANOVA could be applied to the final project case study.
Support your initial posts and response posts with scholarly sources cited in APA style.
https://statistics4beginners.wordpress.com/2015/02/18/how-to-calculate-anova-in-excel-2013/
PLEASE GIVE A 1-2 PARAGRAPH RESPONSE TO THE FOLLOWING:
1.
In this module, our goal is to learn the statistical process of comparing several population means through a procedure called "analysis of variance", or ANOVA. ANOVA uses the variance from the mean of 2 or more sample populations to see if there is a statistically significant difference between them (Sharpe, DeVeaux, Velleman, 2016). We've learned that this is a valuable tool in all sorts of areas of study, including automotive, chemical, and medical industries.
There are many practical examples of ANOVA throughout business. As previously mentioned, the medical field can benefit from the use of this statistics tool. For example, a drug company may be interested in the results of clinical trials for a few new drugs they plan to release. Medicine A, B, and C are all now in the clinical testing phase, so the instances in which each cures a specific ailment can be summed up using ANOVA. Each of the individual drugs, through the course of multiple trials, will have a number of "cured" patients. The following is an example of what the results may be, in table format:
A B C
Trial 1 4 9 2
2 5 8 7
3 7 1 6
4 6 1 5
5 6 4 9
Using ANOVA to evaluate the variance from the mean for each trial, the ultimate goal would be to compare each trial to one another. By comparing the variance, we can say, with statistical confidence, that one medicine may be more effect.
Predictive analytics targets data to predict if ATL advertising is more effective than BTL advertising and to target customer segments and characteristics.
Frequent pattern mining is an analytical algorithm that is used by businesses and, is accessible in some self-serve business intelligence solutions. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
Building solid marketing strategies in today’s competitive market is impossible without sound market research. The right market information can boost your sales, position your product more effectively, and help you speak more effectively to your audience.
Reinforce and focus your marketing research skills. This highly interactive program, facilitated by an experienced marketing research professional, can provide you with the knowledge and tools you need to develop and manage research projects to meet your specific goals. Furthermore, the workshop debunks the myth that you have to spend a lot of money to gain valuable information for decision making. No prior marketing research experience is required!
The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples. It is helpful when an organization wants to determine whether there is a statistical difference between two categories or groups or items and, furthermore, if there is a statistical difference, whether that difference is significant.
Customer Satisfaction Data - Multiple Linear Regression Model.pdfruwanp2000
In this project, we will discuss the results of our analysis of customer satisfaction
conducted using R Studio. Our team did this by carefully analyzing a number of factors that would have an effect on customer happiness of that certain company, such as Complaint
Resolution, Delivery Speed, Order Billing, Warranty Claims, Technical Support, E- commerce, Product Quality, Sales Force Image, Advertising, Price, and Product Line.
We performed a thorough study using a random sample of 70 data points, using a pre- chosen seed value which we obtained from the largest student number in our group. To
ensure the accuracy of our results, we replace missing values of the dataset with the mean of the dataset.
Bing Ads' Eric Couch dives in to beginning and advanced Excel tips and tricks for PPC marketers- including data analysis tips, Excel formulas, and incredibly handy plugins.
ANOVA is a hypothesis testing technique used to compare the equali.docxjustine1simpson78276
ANOVA is a hypothesis testing technique used to compare the equality of means for two or more groups; for example, it can be used to test that the mean number of computer chips produced by a company on each of the day, evening, and night shifts is the same. Give an example of an application of ANOVA in an industrial, operations, or manufacturing setting that is different from the examples provided in the overview. Discuss and share this information with your classmates.
In responding to your peers, select responses that use an ANOVA application that is different from your own. Are the results of the ANOVA application statistically significant? Why are the results significant or not significant? Explain your reasoning. Consider how ANOVA could be applied to the final project case study.
Support your initial posts and response posts with scholarly sources cited in APA style.
https://statistics4beginners.wordpress.com/2015/02/18/how-to-calculate-anova-in-excel-2013/
PLEASE GIVE A 1-2 PARAGRAPH RESPONSE TO THE FOLLOWING:
1.
In this module, our goal is to learn the statistical process of comparing several population means through a procedure called "analysis of variance", or ANOVA. ANOVA uses the variance from the mean of 2 or more sample populations to see if there is a statistically significant difference between them (Sharpe, DeVeaux, Velleman, 2016). We've learned that this is a valuable tool in all sorts of areas of study, including automotive, chemical, and medical industries.
There are many practical examples of ANOVA throughout business. As previously mentioned, the medical field can benefit from the use of this statistics tool. For example, a drug company may be interested in the results of clinical trials for a few new drugs they plan to release. Medicine A, B, and C are all now in the clinical testing phase, so the instances in which each cures a specific ailment can be summed up using ANOVA. Each of the individual drugs, through the course of multiple trials, will have a number of "cured" patients. The following is an example of what the results may be, in table format:
A B C
Trial 1 4 9 2
2 5 8 7
3 7 1 6
4 6 1 5
5 6 4 9
Using ANOVA to evaluate the variance from the mean for each trial, the ultimate goal would be to compare each trial to one another. By comparing the variance, we can say, with statistical confidence, that one medicine may be more effect.
A primer on how ab testing can be set-up for success in an e-commerce environment. Includes guidelines of how to set-up ab tests including hypotheses definition, sample size determination, statistical testing and avoiding bias that can come in any experiment's set-up
A primer on AB testing and it's application in ecommerce. A necessary tool in every product manager's arsenal. Covers the principles behind setting up a good test and the statistical tools required to analyze results.
OL 325 Milestone Three Guidelines and Rubric SectionMoseStaton39
OL 325 Milestone Three Guidelines and Rubric
Section 2: External Competitiveness
Section 2 shifts your focus outside the company to compare pay rates of positions inside the firm with similar positions in the external market place. The shift to
outside the company will move you away from the previous focus on e-sonic’s internal consistency to external competitiveness. Conducting an analysis of
external market data will support your decisions about appropriate pay-policy mixes for job structures in the company.
In section 2 of Milestone Three, you will be introduced to tools compensation professionals use to allocate total compensation within job structures. Total
compensation includes base pay, benefits, and varied incentives used to attract and retain employees. During the simulation you will use some of these tools to
develop pay policies for each e-sonic job structure.
In order to conduct your external market survey you will use web-based salary sites developed by the US Bureau of Labor Statistics and Glassdoor.com. These
websites develop salary information based off of actual pay data from professionals working in specific jobs and potentially represent the most current pay for
the job titles at e-sonic. Follow the steps outlined below:
Section 2 Outline:
Executive Summary Findings
1. Determine Appropriate Pay-Policy Levels for E-sonic Jobs
2. External Market Review
a) Research market competitiveness using the free salary websites listed above, which provide salary data by title and region.
b) Research trends about cost of living adjustments in e-sonic locations. Apply some discussion around leading, lagging or matching the market to
the salary data you found in your market salary research. Assume that the salary research you are using is similar to benchmark jobs. Also,
discuss whether jobs you researched would match the benchmark jobs or require more or less experience and talent than the benchmark job.
c) Update salary data for inflation using CPI-U.
3. Implement Salary Survey Results
a) Create pay grades and ranges by integrating external market data with internal pay grades.
b) Evaluate and summarize decisions made for each job structure.
The External Competitiveness section is fully described in the MyManagementLab Building Strategic Compensation Systems casebook for faculty and students,
linked in the course menu. Follow the explanations and outline to complete this milestone. Section 2: External Marketplace is due at the end of Module Six.
Rubric
Requirements of submission: Each section of the final project must follow these formatting guidelines: 5–7 pages, double spacing, 12-point Times New Roman
font, one-inch margins, and discipline-appropriate citations.
https://www.bls.gov/bls/blswage.htm
https://www.glassdoor.com/Salaries/index.htm
Critical Elements Exemplary (100%) Proficient (85%) Needs Improvement (55%) Not Evident (0%) Value
Section 2: External
Compet ...
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
A/B Testing - Customer Experience Platform experimentation using Pearson’s Chi-Squared Test
1. A/B Testing - Customer Experience Platformexperimentation using Pearson’s Chi-
Squared Test
Aurangzeb Khan
Senior Data Analyst
rana.aurangzeb@hotmail.com
MBA, University of Wollongong, Australia
Abstract: E-commerce giants design and run frequent campaigns on their touch points which includes
website to attract more and more customers. The purpose of this paper is to investigate the effectiveness
of a newly launched web page for consumers and find out if the new page is resulting into different
consumer behavior and/or more website visits and conversion. The ‘Chi-Square Test of Independence’
helps us find out if the different user groups of old and new web page are significantly different from each
other based on conversion rate or not!
The Business Problem
As described in Kaggle (Kaggle link is here), an e-commerce company has designed a new web
page for a website to attract more customers. The e-commerce company wants to investigate if
it should implement the new page or keep the old web page.
Many of the times the consumer/user groups are exposed to and/ or studied based on different
situation (before and after a change) to find out if there is a significant difference in terms of their
performance/consumer behavior using some set of metrics like web site visits, click-through-rate
and the conversion rate. The ‘control’ consumer group is exposed to the ‘old page’ and the
‘treatment’ consumer group is exposed to the ‘new page’ of the website. Now the e-commerce
company wants to know that if the two consumergroups are significantly different from each other
in terms of conversion rate & hence consumer behavior.
Analytical Problem
To determine if the two user/consumer groups exposed to ‘old page’ vs ‘new page’ (consumer
group being the categorical variable) are different in term of click-through-rate and conversion
rates we recommend using Pearson’s Chi-Squared Test for Independence. The Chi-Square
Test is suitable for quantifying the independence of pairs of categorical variables i.e the click and
non-click behavior of the consumers against the website page design.
Chi Square also tell us if the input variable has significant impact on the output variable and hence
will let us choose or drop certain variables when we decide to continue feature selection for the
Analysis.
2. Formula : Chi Square
Image source: Author
Fo: Observed Frequencies
Fe: Expected Frequencies
Steps to conduct Chi-Square of Independence
1. Data wrangling & data consolidation in the shape of contingency table
2. Hypothesis Formulation & Decision Rule
3. Data Visualization
4. Test Statistics calculation
5. Conclusion
1. Data wrangling & Data consolidation in the shape of contingency table
The pairs of categorical variables i.e user group and the click/non-click variables will be displayed
in a contingency table to help us visualize the frequency distribution of the variables.
Below is an example of how the overall data should look like:
Click No-Click Click + No-
Click
Old Page 17489 127785 145,274
New Page 17264 128047 145,311
Old Page + New
Page
34,753 255,832 290,585
Table 1.0: Sample Format of the Data
3. For Chi Square Test we need the Data in below format
Click No-Click
Old Page 17489 127785
New Page 17264 128047
Table 2.0: Sample format for Contingency Table
First of all, we need to import the Python (Data Analysis programming language) libraries and
the data from Kaggle and visualize it.
Python Code:
# Import necessary Libraries
import numpy as np
import pandas as pd
import seaborn as sns
import scipy
import matplotlib.pyplot asplt
# The data has been taken fromKaggle per below link
# https://www.kaggle.com/zhangluyuan/ab-testing
df = pd.read_csv('ab_data.csv')
df.head()
Table 3.0: Simple Data Visualization
4. Let's perform few steps to validate if the data is clean and is ready for Chi Square Test
Python Code:
# The control group represents the users of Old Page
# The treatment group represents the users of new Page
# Let’s see how the data looks like
df.groupby(['group','landing_page']).count()
Table 4.0: Data Aggregation for Visualization
We have noticed above that some users in ‘control group’ have visited ‘new page’ and the data
is wrongly classifying against our objectives. We have also noticed that some users in
‘treatment group’ have visited ‘old page’ and the data is wrongly classified against our
objectives. Now instead of cleaning the data we can only pick the relevant correct data
(control/new_page and treatment/old_page) with the help of below Python code.
Python Code:
# from 'Control Group' we only need old page
# From 'Treatment Group' we only neednew page
df_cleaned= df.loc[(df['group'] == 'control') & (df['landing_page'] == 'old_page') |(df['group'] == 'treatment')
& (df['landing_page'] == 'new_page')]
df_cleaned.groupby(['group','landing_page']).count()
Table 5.0: Cleansed and consolidated Data for both user groups
5. Finding Duplicates
Python Code:
# Checking for duplicate values
print(df_cleaned['user_id'].duplicated().sum())
# Finding user_idfor duplicate value
df_cleaned[df_cleaned.duplicated(['user_id'],keep=False)]['user_id']
# Now we need to drop the Duplicates
df_cleaned= df.drop_duplicates(subset='user_id',keep="first")
Preparing the Contingency Tabe for Chi-Square Test
### To prepare and arrange the Data for Chi-Square Contigency Table
# 1) Take out the Control group
control = df_cleaned[df_cleaned['group'] == 'control']
# 2) Take out the Treatment group
treatment = df_cleaned[df_cleaned['group'] == 'treatment']
# 2A) A-click -i.e The ones who convertedfrom Control group
control_click = control.converted.sum()
# 2B) No-click,i.e The one who did not click fromControl group
control_noclick = control.converted.size -control.converted.sum()
#3 B-click, B-noclick
# 3A) A-click -i.e The ones who convertedfrom Treatment group
treatment_click = treatment.converted.sum()
# 2B) No-click,i.e The one who did not click fromTreatment group
treatment_noclick = treatment.converted.size - treatment.converted.sum()
# 3) Create np array
Table = np.array([[control_click, control_noclick], [treatment_click, treatment_noclick]])
print(Table)
2. Hypothesis Formulation & Decision Rule
Null Hypothesis
H0:
The ‘control’ user group and ‘treatment’ user group are independent in terms of their conversion rate.
Alterative hypothesis
H1:
The ‘control’ user group and ‘treatment’ user group are dependent and different in terms of their
conversion rate
Level of significance
For this test, we assume that α = 0.05 or Confidence Interval = 95%
Decision Rule
If p-value is less than Level of significance (5%) then we will Reject Null Hypothesis (H0).
6. 3. Data Visualization
Let’s printthe multidimensional array thatwe created in Python :
Click No-Click
Old Page 17471 127761
New Page 17274 128078
Table 6.0: Chi-Square Test Contingency Table
4. Test Statistics Calculations
To perform the Test let’s import the necessary Python libraries and get the following parameters
1. Test Statistics
2. P- Value
3. Degree of Freedom
4. Expected Frequencies
Python Code:
import scipy
from scipy importstats
# The correction will Adjustthe observerdvalue by .5 towards the corressponding ExpectedValues
stat,p,dof,expected = scipy.stats.chi2_contingency(Table,correction=True)
print('nStat : ',stat)
print('nP-Value : ',p)
print('nDegree of Freedom : ',dof)
print('nObservedFrequencies: ',Table)
print('nExpectedFrequencies: ',expected)
Snapshot 1.0: Chi-Square Test Results
7. Python Code:
# interpret p-value
alpha = 1.0 - .95
if p <= alpha:
print('Dependent(reject H0)')
else:
print('Independent(fail to reject H0)')
5. Conclusion:
The p-value is 22.9% at 5% level of significance. As the p-value is greater than alpha so we do
not Reject the Null Hypothesis
The old and new page's users did not behave significantly different and the conversion ratio is
not significantly different. Hence, the new web page is not different from the old one.
The conversion rate is considered independent as the observed and expected frequencies are
similar, the variables do not interact and are not dependent.
THE END