The document describes multiple regression analysis and its applications in business decision making. It explains that multiple regression allows examination of the linear relationship between one dependent variable and two or more independent variables. The chapter goals are to help readers apply and interpret multiple regression, perform residual analysis, and test significance of variables. An example of using price and advertising spending to predict pie sales is provided to illustrate multiple regression concepts.
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...Simplilearn
This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
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To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...Simplilearn
This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -
To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
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BAFN 305 - Multiple Regression Questions
The attached provides descriptive statistics, correlations and a multiple regression run for four variables.
The four variables are about corporate earnings, debt, sales and ownership. Sample size is 400
companies. You are provided means, standard deviations, correlations and two regression models, a full
and reduced model.
The variables are: Earnings per share (EPS) - the 'Y'variable-\,
Company Debt, measured in millions of dollars,
Annual Sales, measured in millions of US dollars, and
Public or private ownership, a binary of (1,0) with 1 assigned to public.
Answer the following:
L. What percent of the companies are private. How many is that
2. The best predictor of EPS is
3. The weakest predictor of EPS is
4. ldentify the independent variable(s) understudy
5. A multiple regression model for EPS = f(Debt Sales,Ownership] was run and is noted on the
attached. Answer the following:
a. Coefficient of determination is
b.TestthehypothesisthatHop=0'AIphaat.05(AcceptorReject}-
a. State the critical value
b. State the computed value
c. State the p-value
d. State the decision
e. State the conclusion.
c. State the regression equation to forecast EPS.
d. Test the hypothesis to determine the importance of each variable.
a. State the critical value for the test - Alpha at .01 TT
b. State the computed value and the p-value for each
c. State the decisions.
d. State the conclusions.
6. lf you completed the above, you evaluated the 'Full Model'
a. Would you create a reduced model based on the analysis above.
b. lf so, which variables would be kept for the reduced model.
c. lf you ran the reduced model, why did you remove the variable, multicollinearity or lack
of a relationship with 'Y'.
d. Would the model provide good prediction of EPS._ Why
7
Full Model - Earnings = f(Debt,Sales,Ownership)
Summary
R-Square .75
Standard error .75
Cases 400
Reduced Model - Earnings = f(Debt, Sales)
Summary
R-Square .71
Standard error .80
Cases 400
Earnings (per sh) Debt Sales Public/Private(1,0
Means 3.00 20 (million) 30 (million) .65
Standard Deviations 1.50 8 (million) 10 (million) .30
Correlations Earnings Debt Sales Public/Private(t,0)
Earninss 1.00
Debt -.58 1.00
Sales 0.40 0.40 1.00
Public/Private (1,0) 0.30 =.25 o.41 1.00
ANOVA Sum of squares Df Mean Square F p-value
Regression 7,500 3 2.s00 396.20 .000
Residual 2.s00 396 6.31
Total 10,000 399
Variable Coefficient Standard Error t/z statistic p-value
lntercept 1.25
Debt -.10 xxxxxxxx -2.70 .006
Sales 0.15 xxxxxxxx L.45 .090
Public/Private 0.08 xxxxxxxx L.40 .096
ANOVA Sum of squares Df Mean Square F p-value
Reeression 7,too 2 3s50 486.30 .000
Residual 2,9OO 397 7.30
Total 10,000 399
Variable Coefficients Standard error t/z statistic
lntercept 1.85
Debt -.L4 xxxxxxxx -2.90 .003
Sales .20 xxxxxxxx 2.00 ,o23
Comparative Analysis
Xxxxxxxx Yyyyyyyyy
ITM 619
xx/xx/xxxx
Dr. Webb
waelalturki
Highlight
waelalturki ...
Using Monte Carlo Simulation in Project Estimates by Akram Najjar
The PMI Lebanon is glad to announce that Akram Najjar is the speaker for the a lecture titled “Using Monte Carlo Simulation in Project Estimates” delivered on Thursday, 28 July 2016
Lecture Outline
* Why are single point estimates unreliable and what is the alternative?
* What are distributions and how do we extract random samples from them (using Excel)? Two costing examples.
* How to setup a Monte Carlo Simulation model in a spreadsheet?
* Two PM examples (in detail)
* How to statistically analyze the thousands of runs to reach reliable estimates?
Lecture Objectives
* A Project Manager usually knows how certain parameters (such as duration, resource rates or quantities) behave. However, the PM can almost never define reliable single point estimates for these parameters. The result: many projects fail due to unreliable estimates. The alternative? The PM has to use his/her knowledge of how specific parameters behave statistically. For example, the PM knows that a specific task’s duration is distributed according to the bell shaped curve OR that another is uniformly distributed (flat variation), or triangular, or Beta-PERT, etc. The PM can then use Monte Carlo Simulation (MCS) to arrive at statistically significant and robust results. Monte Carlo Simulation (MCS) is a technique that relies on two processes. Process 1 aims at developing a spreadsheet model that calculates the critical path or the total cost, etc. The calculation is setup in a single row (or Run). This row is then duplicated a large number of times (thousands). Process 2 aims at inserting Excel functions in each of the parameters (durations, costs). In each row (or Run), such functions will provide a sample drawn from a statistical distribution that properly describes the behavior of that parameter. For example, a specific duration follows a Normal (Bell) distribution with an Average A and a Standard Deviation S. The model will then generate for each run and for that duration a different value that conforms with the bell shaped curve as defined (A and S). Each of these thousands of runs will provide the PM with a different “simulation” of the duration or the total cost, etc. By statistically analyzing the thousands of results, the PM can arrive at a robust and reliable estimate. Proprietary Add On’s for Monte Carlo Simulation in Microsoft Project are available. However, it is easy, free and more flexible to use native Microsoft functions to carry out the full simulation. The talk covered all the steps needed for such simulations giving several examples
Week 2 Individual Assignment 2: Quantitative Analysis of Credit -
Solution
s
This assignment is based on the data we used during our two live sessions, but it has been updated to include a splitting variable (credit2.xlsx). In the spreadsheet under the tab “Data," you will find data
pertaining to 1,000 personal loan accounts. The tab “Data Dictionary” contains a description of what the various variables mean.
As a part of a new credit application, the company collects information about the applicant. The company then decides an amount of the credit extended (the variable CREDIT_EXTENDED). For these 1,000 accounts, we also have information on how profitable each account turned out to be (the variable NPV). A negative value indicates a net loss, and this typically happens when the debtor defaults on his/her payments.
The goal in this assignment is to investigate how one can use this data to better manage the bank's credit extension program. Specifically, our goal is to develop a classification model to classify a new credit account as “profitable” or “not profitable." Secondly we want to compare its performance in the context of decision support to a linear regression model that predicts NPV directly.
Please answer all the questions. Supply supporting documentation and show calculations as
needed. Please submit a single, well-formatted PDF or Word file. The instructor should not need to go searching for your answers! In addition, please upload an Excel file with your model outputs – the file will not be graded, but will help the instructor give you feedback, if your model differs substantially from the solutions.
For extra assistance, you may want to access the tutorials located on the course resource center page.Data Preparation
The data preparation repeats the steps from the live session:
a) The goal is to predict whether or not a new credit will result in a profitable account. Create a new variable to use as the dependent variable.
b) Create dummy variables for all categorical variables with more than 2 values (or if you prefer, you can sort your variables into numerical and categorical when you run the model).
c) Split the data into 2 parts using the splitting variable that has been added to the data set. This is to ensure a more balanced split between the validation and training samples. Note that Analytic Solver Data Mining only allows 50 columns in the analysis, so leave out your base dummies (if you created them) when partitioning. After the data partition, you should have 666 rows in your training data and 334 in your validation data.
The Assignment
1. Applying Logistic Regression
If one fits a Logistic Regression Model using all the independent variables, one observes a) a gap in the classification performance between the training data and the validation data, and b) very
high p-values for some of the variables. The performance gap between the training and validation may be a sign of overfitting, and the high p-values may b ...
Operations ImprovementBUS255 GoalsBy the end of this.docxhopeaustin33688
Operations Improvement
BUS255
Goals
By the end of this chapter, you should know:
Importance of Operations improvement
Improvement Techniques
Broad approaches to improvement
Elements of Improvement
2
In ‘Alice’s adventures through the looking glass’, by Lewis Carroll, Alice encounters living chess pieces and, in particular, the ‘Red Queen’.
‘Well, in our country’, said Alice, still panting a little, ‘you’d generally get to somewhere else – if you ran very fast for a long time, as we’ve been doing’. ‘A slow sort of country!’ said the Queen. ‘Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!
The Red Queen effect
3
Think about examples!
Automotive sector
Telecommunications sector (cell phones)
Implications
Operations Improvement is necessary to retain competitive position
Greater operations improvements (comparatively) are necessary to improve competitive position
Improvement Techniques
Scatter Diagram
Scatter Diagram: A graph of the value of one variable vs. another variable
Absenteeism
Productivity
6
Scatter Diagram
Help us understand the relationship between variables (tool to generate ideas)
Remember, correlation doesn’t mean causation
X and Y have positive relationship doesn’t necessarily mean X causes Y.
Refer to in-class problem # 1
7
Flowchart
Flowchart (Process Diagram): A chart that describes the steps in a process. It is also called as process map.
8
Flow Chart
MRI Flowchart
Physician schedules MRI
Patient taken to MRI
Patient signs in
Patient is prepped
Technician carries out MRI
Technician inspects film
If unsatisfactory, repeat
Patient taken back to room
MRI read by radiologist
MRI report transferred to physician
Patient and physician discuss
11
10
20%
9
8
80%
1
2
3
4
5
6
7
Flow Chart
Flowcharts are vey useful in visually describing processes (tool to organize data)
Refer to in-class problem # 2
Let’s do it in Visio
Cause and Effect Diagram
Cause-and-Effect Diagram: A tool that identifies process elements (causes) that might effect an outcome. Also called Fishbone diagram or Ishikawa diagram.
Cause
Materials
Methods
Manpower
Machinery
Effect
11
Cause-and-Effect Diagram
Material
(ball)
Method
(shooting process)
Machine
(hoop &
backboard)
Manpower
(shooter)
Missed
free-throws
Rim alignment
Rim size
Backboard stability
Rim height
Follow-through
Hand position
Aiming point
Bend knees
Balance
Size of ball
Lopsidedness
Grain/Feel (grip)
Air pressure
Training
Conditioning
Motivation
Concentration
Consistency
12
Cause-and-Effect Diagram
Very helpful for performing root cause analysis. Can also identify areas where further data is needed (tool to generate ideas)
Most used categories: Machinery, Manpower, Materials, Methods, and Money
Other categories can also be used
Refer to in-class problem # 3.
To simulate is to try to duplicate the features, appearance and characteristics of a real system.
The idea behind simulation is to imitate a real-world situation mathematically, to study its properties and operating characteristics, to draw conclusions and make action decisions based on the results of the simulation.
The real-life system is not touched until the advantages and disadvantages of what may be a major policy decision are first measured on the system's model.
Similar to Introduction to Multiple Regression (20)
Konsep Balanced Score Card. Penilaian kinerja dilihat dari 4 perspektif yaitu perspektif keuangan, konsumen, learn and growth dan proses bisnis internal.
Makalah Analisis PT Kereta API Indonesia . membahas analisis strategik dalam perusahaan kereta api, dimana dampak peraturan harga pesawat tidak ada penetapan batas bawah maka kereta api berdampak.
Makalah Analisis PT Kereta API Indonesia . membahas analisis strategik dalam perusahaan kereta api, dimana dampak peraturan harga pesawat tidak ada penetapan batas bawah maka kereta api berdampak.
Dmfi booklet indonesian. isi petisi nya yah jangan lupa klik www.dogmeatfreeindonesia.org
tidak sampai 1 menit isi petisi ini agar indonesia bebas dari daging anjing, anjing layak diperlakukan layak dan lebih baik.
tolong ya teman - teman
Dmfi booklet indonesian. isi petisi nya yah jangan lupa klik www.dogmeatfreeindonesia.org
tidak sampai 1 menit isi petisi ini agar indonesia bebas dari daging anjing, anjing layak diperlakukan layak dan lebih baik.
tolong ya teman - teman
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation