- The document outlines a class on multiple regression analysis and its applications.
- It discusses using regression to analyze the relationship between sales of a product and its own price as well as the price of a substitute or complementary product.
- As an example, it describes a group exercise analyzing sales data of Best Foods and Kraft mayonnaise to determine if they are substitute or complementary goods.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
Simple Linear Regression: Step-By-StepDan Wellisch
This presentation was made to our meetup group found here.: https://www.meetup.com/Chicago-Technology-For-Value-Based-Healthcare-Meetup/ on 9/26/2017. Our group is focused on technology applied to healthcare in order to create better healthcare.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Time Series basic concepts and ARIMA family of models. There is an associated video session along with code in github: https://github.com/bhaskatripathi/timeseries-autoregressive-models
https://drive.google.com/file/d/1yXffXQlL6i4ufQLSpFFrJgymhHNXL1Mf/view?usp=sharing
It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
Forecasting techniques, time series analysisSATISH KUMAR
Forecasting techniques, time series analysis
Introduction
Meaning
Definition
Features of forecasting
Process of forecasting
Importance of forecasting
Advantages of forecasting
Limitations of forecasting
Methods of forecasting
Conclusion
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Simple Linear Regression: Step-By-StepDan Wellisch
This presentation was made to our meetup group found here.: https://www.meetup.com/Chicago-Technology-For-Value-Based-Healthcare-Meetup/ on 9/26/2017. Our group is focused on technology applied to healthcare in order to create better healthcare.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Time Series basic concepts and ARIMA family of models. There is an associated video session along with code in github: https://github.com/bhaskatripathi/timeseries-autoregressive-models
https://drive.google.com/file/d/1yXffXQlL6i4ufQLSpFFrJgymhHNXL1Mf/view?usp=sharing
It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
Forecasting techniques, time series analysisSATISH KUMAR
Forecasting techniques, time series analysis
Introduction
Meaning
Definition
Features of forecasting
Process of forecasting
Importance of forecasting
Advantages of forecasting
Limitations of forecasting
Methods of forecasting
Conclusion
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
Walmart Sales Prediction Using Rapidminer Prepared by Naga.docxcelenarouzie
Walmart Sales Prediction Using Rapidminer
Prepared by : Nagarjun Singharavelu
I. Introduction:
Wal-Mart Stores, Inc is an American Multinational retail corporation that
operates a chain of discount department stores and Warehouse Stores. Headquartered in
Bentonville, Arkansas, United States, the company was founded by Sam Walton in 1962 and
incorporated on October 31, 1969. It has over 11,000 stores in 27 countries, under a total 71
banners. Walmart is the world's largest company by revenue, according to the Fortune Global
500 list in 2014, as well as the biggest private employer in the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Walton family. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. The company was listed on the New York
Stock Exchange in 1972. In the late 1980s and early 1990s, the company rose from a regional to
a national giant. By 1988, Walmart was the most profitable retailer in the U.S. Walmart helps
individuals round the world economize and live better.
The main aim of our project is to identify the impact on sales throughout
numerous strategic selections taken by the corporate. The analysis is performed on historical
sales data across 45 Walmart stores located in different regions. The foremost necessary is
Walmart runs many promotional markdown events throughout the year and we have to check
the impact it creates on sales during that particular period. The markdowns precede prominent
holidays, the four largest of which are the Labor Day, Thanksgiving and Christmas. During these
weeks it is noted that there is a tremendous amount of change in the day-to-day sales. Hence
we tend to apply different algorithms which we learnt in class over this dataset to identify the
effect of markdowns on these holiday weeks.
II. Information about dataset:
We had taken four different datasets of Walmart from Kaggle.com
containing the information about the stores, departments, average temperature in that
particular region, CPI, day of the week, sales and mainly indicating if that week was a
holiday. Let us explain each dataset in detail.
Stores:
The no. of attributes in this dataset is 3.
They are store number, type of store and the size of store.
Output attribute is the size of store.
There are 45 stores whose information is collected.
Stores are categorized into three such as A, B and C, which we assume it to be
superstores containing different types of products.
The store size would be calculated by the no. of products available in the particular
store ranging from 34,000 to 210,000.
Train:
This is the historical training data, which covers to 2010-02-05 to 2012-11-01.
It consists of the store and department number.
Date of the week.
Weekl.
As part of the OESON Data Science internship program OGTIP Oeson, I completed my first project. The goal of the project was to conduct a statistical analysis of the stock values of three well-known companies using Advanced Excel. I used descriptive statistics to analyze the data, created charts to visualize the trends and built regression models for each company.
Price movement prediction in Hong Kong equity marketTc. Ying
DARK HISTORY back in undergraduate year (2011). A naive and embarrassing effort to predict buy signals for stock index with price data analytics.
https://gitlab.com/tc-ying/cuhk/tree/master/CSCI4020-Financial-Data-Mining
Channel capabilities, product characteristics, and impacts of mobile channel ...Minha Hwang
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SMM Cheap - No. 1 SMM panel in the worldsmmpanel567
Boost your social media marketing with our SMM Panel services offering SMM Cheap services! Get cost-effective services for your business and increase followers, likes, and engagement across all social media platforms. Get affordable services perfect for businesses and influencers looking to increase their social proof. See how cheap SMM strategies can help improve your social media presence and be a pro at the social media game.
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Digital Commerce Lecture for Advanced Digital & Social Media Strategy at UCLA...Valters Lauzums
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Amid these operational challenges, customer data has emerged as an important strategy. By focusing on personalization and enhancing customer experience from historical behavior, businesses can deliver improved website and brand experienced, better product recommendations, optimal promotions, and content to meet individual preferences. Better data analytics can also help in effectively creating marketing campaigns, improving customer retention, and driving product development and inventory management.
Innovative formats such as social commerce and live shopping are beginning to impact the digital commerce landscape, offering new ways to engage with customers and drive sales, and may provide opportunity for brands that have been priced out or seen a downturn with post-pandemic shopping behavior. Social commerce integrates shopping experiences directly into social media platforms, tapping into the massive user bases of these networks to increase reach and engagement. Live shopping, on the other hand, combines entertainment and real-time interaction, providing a dynamic platform for showcasing products and encouraging immediate purchases. These innovations not only enhance customer engagement but also provide valuable data for businesses to refine their strategies and deliver superior shopping experiences.
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AI-Powered Personalization: Principles, Use Cases, and Its Impact on CROVWO
In today’s era of AI, personalization is more than just a trend—it’s a fundamental strategy that unlocks numerous opportunities.
When done effectively, personalization builds trust, loyalty, and satisfaction among your users—key factors for business success. However, relying solely on AI capabilities isn’t enough. You need to anchor your approach in solid principles, understand your users’ context, and master the art of persuasion.
Join us as Sarjak Patel and Naitry Saggu from 3rd Eye Consulting unveil a transformative framework. This approach seamlessly integrates your unique context, consumer insights, and conversion goals, paving the way for unparalleled success in personalization.
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Mastering Multi-Touchpoint Content Strategy: Navigate Fragmented User Journeys
Multiple Regression Analysis
1. Class Outline
• Multiple Regression Analysis
• Application of Regression
– Substitute goods VS. Complimentary goods
• Group Exercise: Best Foods VS. Kraft
4. Example – Sales Data
SALES
COMPETITOR
PRICE
ADVERTISIGN
PROMOTION
COUPON
DISPLAY
••••••
PRICE
5. • SALES = f ( Price, Competitor Price, Other factors )
• Assumptions of Regression Model
1. Linear Relationship Between SALES and PRICE
2. Linear Relationship Between SALES and
COMPETITOR PRICE
3. Other factors follow N( )
2
,
),0(~
,CPricePriceSALES
2
21
Ni
iiii
Competitor Price
6. • Using data, we make inferences on , , and .
• Our best guess on using the sample data: a
• Our best guess on using the sample data: b1
• Our best guess on using the sample data: b2
• Determine a, b1, and b2 by minimizing the sum of
squared errors
1
iiii CPricePriceSALES 21
2
1
2
7. Use of Regression Model
1. Prediction / Forecasting
eg.) Price = 3; CPrice = 2
Exp. Sales=284.86–46.60*3+22.40*2+ Expected Value of ε
=284.86–46.60*3+22.40*2
2. Relationship between variables
One Unit Increase in Price 46.60 Units Decrease in
Expected Sales
One Unit Increase in CPrice 22.40 Units Increase in
Expected Sales
Sales=284.86–46.60*Price+22.40*CPrice+ε
8. Exercise
• Use “Regression Exercise 3.xlsx => Multiple
Regression 1”
• Use Excel “Solver” and “Data Analysis”
9. In-Class Exercise
• Use “Regression Exercise 3.xlsx” Multiple Regression
2
• Q1: Estimate a, b1,and b2
• Q2: Compute the average of errors
• Q3: Compute the expected sales when Price=3; CPrice=2
• Q4: Compute the expected sales when Price=2; CPrice=3
• Q5: Compute the R-Square
• Q6: Perform the same regression analysis using “Excel
Data Analysis”
10. Regression Statistics
Multiple R 0.85
R Square 0.73
Adjusted R Square 0.68
Standard Error 7.83
Observations 15.00
ANOVA
df SS MS F Significance F
Regression 2.00 1984.27992.13 16.19 0.00
Residual 12.00 735.33 61.28
Total 14.00 2719.60
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 419.95 37.40 11.23 0.00 338.46 501.45
Price -42.80 8.30 -5.15 0.00 -60.89 -24.70
Cprice 4.39 9.74 0.45 0.66 -16.82 25.60
11. Application of Regression Model
Substitute Good VS. Complimentary Good
• Substitute goods: replace each other in use
Margarine and butter
Tea and coffee
Sales_Tea = a + b1 * Price_Tea + b2 * Price_Coffee + ε
• Complimentary goods: complement each other in use
Hotdog and hotdog bun
Hardware and software
Sales_Hard = a + b1 * Price_Hard + b2 * Price_Soft + ε
+ or - ?
+ or - ?
12. Application of Regression Model
Substitute Good VS. Complimentary Good
• Coke vs. Pepsi
• Coke vs. Sierra Mist (?)
• Why important?
– Identify _________________
13. Samuel Adams – Brewer & Patriot
• Relationship between Beer and Tea: Substitute goods
• Sales_Beer = a + b1 * Price_Beer + b2 * Price_Tea + ε
• b2: ( + ) or ( - ) ?
• Tea supply ↓ Tea price ↑ Sales_Beer ?
• For Sam, Good or Bad ?
15. Group Exercise: Best Foods VS. Kraft
• Use “PHXMayoData.xlsx”
• 173 weeks (2002-2005)
• A grocery store in Phoenix area
• Sales and Prices of Best Foods (BF) Mayo and Kraft (KR)
Mayo
Week Sales_BF Sales_KR Price_BF Price_KR
1 455 135 1.61 1.02
2 530 63 1.34 1.29
3 527 41 1.38 1.63
4 418 71 1.44 1.53
5 380 34 1.62 1.71
: : : : :
16. Group Exercise: Best Foods VS. Kraft
• Q1: Compute average sales and average prices for
both brands. What can we infer about this market
from these numbers?
Use “=average( )”
Best Foods Kraft
Average Sales 350 73
Average Price 1.63 1.48
17. Group Exercise: Best Foods VS. Kraft
• Q2: Perform regression analysis
– Model1: Sales_BF = a + b1* Price_BF + b2* Price_KR + Error
– Model2: Sales_KR = a + b1* Price_BF + b2* Price_KR + Error
Use “Data Analysis – Regression”
Model 1
Model 2
19. • Q3: Interpret the results – Model2 (Kraft)
Sales_KR = a + b1* Price_BF + b2* Price_KR + ε
20. Group Exercise: Best Foods VS. Kraft
• Q4: Compute the expected sales of both brands when
Price_BF = average of Price_BF’s
Price_KR = average of Price_KR’s
Sales_BF = 900 - 393 * Price_BF + 61* Price_KR + ε
Sales_KR = 155 + 55 * Price_BF – 116* Price_KR + ε
21. Group Exercise: Best Foods VS. Kraft
Best Foods Kraft
Average Sales 350 73
Average Price 1.63 1.48
Exp. Sales_BF = 900 - 393 * 1.63 + 61* 1.48 = 350
Exp. Sales_KR = 155 + 55 * 1.63 – 116* 1.48 = 73
22. Group Exercise: Best Foods VS. Kraft
• Q5: Now assume that Best Foods decrease its price
by $0.1. What will happen to the sales of both
brands?
Best Foods Kraft
Average Sales 350 73
Average Price 1.63 1.48
Exp. Sales_BF = 900 - 393 * 1.53 + 61* 1.48 = 389 (+11%)
Exp. Sales_KR = 155 + 55 * 1.53 – 116* 1.48 = 68 (-8%)
1.53
23. Group Exercise: Best Foods VS. Kraft
• Q6: Now assume that Kraft decrease its price by $0.1.
What will happen to the sales of both brands?
Best Foods Kraft
Average Sales 350 73
Average Price 1.63 1.48
Exp. Sales_BF = 900 - 393 * 1.63 + 61* 1.38 = 344 (-2%)
Exp. Sales_KR = 155 + 55 * 1.63 – 116* 1.38 = 85 (+16%)
1.38
24. Group Exercise: Best Foods VS. Kraft
Best Foods Kraft Total
Average Sales 350 73 423
Best Foods Price ↓ $0.1
389 68 457
(+11%) (-8%) (+8%)
Kraft Price ↓ $0.1
344 85 429
(-2%) (+16%) (+1%)
25. Group Exercise: Best Foods VS. Kraft
• Q7: Now assume that the cost of BF is $1. What is the
BF’s expected profit?
Exp.Profit = Exp.Sales * ( Price – Cost )
Coefficients Standard Error t Stat
Intercept 900.80 58.06 15.52
Price_BF -392.88 32.88 -11.95
Price_KR 61.25 23.29 2.63
Best Foods Kraft
Average Price 1.63 1.48
Exp.Sales 350 =
Exp.Profit 221=
1
2
3
4 51 2 3+ +X X
X ( - 1)
4
4
5
26. • Q8: What is the optimal price that maximizes the BF’s
profit? Hint: Use “Solver”
Best Foods Kraft
Average Price 1.76 1.48
Exp.Sales 299
Exp.Profit 228
Optimal
Solution