Logistic Regression, Linear and Quadratic Discriminant Analysis and K-Nearest...Tarek Dib
Example of logistic regression, linear and quadratic discriminant analysis and KNN. The data set was pulled off the 'ISLR' package. The main goal of the model is to predict the direction of market based of the previous 2-day returns. Accuracy was compared among the 4 models.
Fault prediction using logistic regression (Python)Binayak Dutta
The document discusses using logistic regression to predict faults in wind turbines. It describes acquiring operational status and weather data for turbines, preparing the data for modeling, developing a logistic regression model using Python libraries, and evaluating the model on training and test data. The evaluation shows some predictors are more influential than others. The document also discusses implementing a continuous learning process to re-evaluate and fine-tune the model over time as new data becomes available.
Consumer Credit Scoring Using Logistic Regression and Random ForestHirak Sen Roy
The document discusses using logistic regression and random forest models for consumer credit scoring. It begins by introducing credit scoring and explaining that the goal is to classify applicants as "good" or "bad" credit risks. It then outlines the typical steps taken in developing a credit scoring model, including understanding the problem, defining variables, exploratory data analysis, and splitting data into training and test sets. The document focuses on logistic regression, explaining the logistic regression model and how it is fitted. It also briefly introduces random forest methods and LASSO regularization.
Introduction to Analytics
Introduction to SAS
Introduction to Satistics
Introduction to Predictive Modeling
Introduction to Forecasting
Introduction to Bigdata
This document discusses various use cases and analyses for a telecom company, including subscription activation and termination, CRM and billing, revenue segmentation by service and customer, customer churn analysis and reasons for churn, customer profiling, calculating average revenue per user (ARPU) and the shift to average revenue per account (ARPA), segmentation of postpaid and prepaid customers, analyzing tariff plan changes, and how customer segmentation can benefit operators by maximizing revenue and retention.
Logistic regression is a statistical method used to predict a binary or categorical dependent variable from continuous or categorical independent variables. It generates coefficients to predict the log odds of an outcome being present or absent. The method assumes a linear relationship between the log odds and independent variables. Multinomial logistic regression extends this to dependent variables with more than two categories. An example analyzes high school student program choices using writing scores and socioeconomic status as predictors. The model fits significantly better than an intercept-only model. Increases in writing score decrease the log odds of general versus academic programs.
Logistic Regression, Linear and Quadratic Discriminant Analysis and K-Nearest...Tarek Dib
Example of logistic regression, linear and quadratic discriminant analysis and KNN. The data set was pulled off the 'ISLR' package. The main goal of the model is to predict the direction of market based of the previous 2-day returns. Accuracy was compared among the 4 models.
Fault prediction using logistic regression (Python)Binayak Dutta
The document discusses using logistic regression to predict faults in wind turbines. It describes acquiring operational status and weather data for turbines, preparing the data for modeling, developing a logistic regression model using Python libraries, and evaluating the model on training and test data. The evaluation shows some predictors are more influential than others. The document also discusses implementing a continuous learning process to re-evaluate and fine-tune the model over time as new data becomes available.
Consumer Credit Scoring Using Logistic Regression and Random ForestHirak Sen Roy
The document discusses using logistic regression and random forest models for consumer credit scoring. It begins by introducing credit scoring and explaining that the goal is to classify applicants as "good" or "bad" credit risks. It then outlines the typical steps taken in developing a credit scoring model, including understanding the problem, defining variables, exploratory data analysis, and splitting data into training and test sets. The document focuses on logistic regression, explaining the logistic regression model and how it is fitted. It also briefly introduces random forest methods and LASSO regularization.
Introduction to Analytics
Introduction to SAS
Introduction to Satistics
Introduction to Predictive Modeling
Introduction to Forecasting
Introduction to Bigdata
This document discusses various use cases and analyses for a telecom company, including subscription activation and termination, CRM and billing, revenue segmentation by service and customer, customer churn analysis and reasons for churn, customer profiling, calculating average revenue per user (ARPU) and the shift to average revenue per account (ARPA), segmentation of postpaid and prepaid customers, analyzing tariff plan changes, and how customer segmentation can benefit operators by maximizing revenue and retention.
Logistic regression is a statistical method used to predict a binary or categorical dependent variable from continuous or categorical independent variables. It generates coefficients to predict the log odds of an outcome being present or absent. The method assumes a linear relationship between the log odds and independent variables. Multinomial logistic regression extends this to dependent variables with more than two categories. An example analyzes high school student program choices using writing scores and socioeconomic status as predictors. The model fits significantly better than an intercept-only model. Increases in writing score decrease the log odds of general versus academic programs.
Medical equipment manufacturing business plan exampleupmetrics.co
If you are planning to start a new manufacturing business, the first thing you will need is a business plan. Use our Lanzor - medical equipment manufacturing business plan example created using upmetrics business plan software to start writing your business plan in no time.
Before you start writing your business plan for your new medical equipment manufacturing business, spend as much time as you can reading through some examples of manufacturing business plans. Reading some sample business plans will give you a good idea of what you’re aiming for and also it will show you the different sections that different entrepreneurs include and the language they use to write about themselves and their business plans.
We have created this medical equipment manufacturing business plan example for you to get a good idea about how a perfect manufacturing business plan should look like and what details you will need to include in your stunning business plan.
The document discusses various analytics services including predictive analytics for finance, clinical trials, customer segmentation, and grid analytics. It describes an enterprise data science platform that integrates data from various source systems and covers business vision, processes, data, technologies, and user adoption. The platform includes a big data lake that integrates data across formats and structures with built-in data governance. Specific analytics services are discussed for financial services, retail, power and utility, and oil and gas industries, powered by technologies like SAP HANA, Hadoop, R, and open source.
The document discusses various analytics services including predictive analytics for finance, clinical trials, customer segmentation, and grid analytics. It describes an enterprise data science platform that integrates data from various source systems and covers business vision, processes, data, technologies, and user adoption. The platform includes a big data lake that integrates data across formats and structures with built-in data governance. Specific analytics services are discussed for financial services, retail, power and utility, and oil and gas industries, powered by technologies like SAP HANA, Hadoop, R, and open source.
The document discusses the challenges facing process manufacturers and what they should look for in an ERP system. It summarizes key challenges as ageing infrastructure, high costs, increased customer demands for faster development times, and new environmental/safety standards. It recommends looking for an ERP system with strong process manufacturing functionality like formula/recipe management, quality control, lot tracking, production scheduling, and regulatory compliance. Case studies from various companies demonstrate how an ERP system from Sage helped them address challenges and support growth.
Planimeter was founded in 1997 to provide statistical services for clinical trials. It initially had 4 employees focusing on Phase III-IV trials. In 2005, it started e-business solutions like patient registries and eCRFs. It now has 16 employees and contractors providing full clinical trial support including CDISC modeling, statistical programming, and medical writing. Planimeter aims to be an internationally recognized provider through high quality services and applying rigorous quantitative methods. It focuses on therapeutic areas like neurology and oncology while following standards like CDISC and ISO.
Square Pharmaceuticals Ltd. is the leading pharmaceutical company in Bangladesh that was founded in 1958. It has a market share of about 15% and annual sales of over $90 million. Previously, Square faced difficulties meeting market demand and sharing information between departments due to a lack of integration. To address these issues, Square developed its own ERP system called ConSIL. ConSIL integrated all of Square's departments including production, sales, procurement, and quality control. This allows Square to better forecast demand, track materials, ensure compliance, and respond quickly to customers. The ERP system provides benefits like reduced costs, faster product development, and end-to-end traceability.
Sheet1 Intermediate Business StatisticsExcel Exercise - Enter your.docxlesleyryder69361
Sheet1 Intermediate Business StatisticsExcel Exercise - Enter your name:DIRECTIONS - a. Complete the questions below using the Normal distribution functions. Use Excel shapes to complete Box Plotb. On Sheet 2, complete the shaded areas using Excel functionsc. On sheet 3, perform the analysis using Excel's data analysis add-in.FORMAT YOUR RESULTS TO 2 DECIMAL PLACES ON THE THREE SHEETS.1GMATs are normally distributed with a mean of 500 and standard deviation of 100.Using the normdist function for each question below, donot use your tables:a. Find the probability of selecting an individual whose score is less than or equal to 700b. Find the probability of selecting an individual whose score is greater than or equal to 600c. Find the probability of selecting an individual whose score is between 400 and 600.2Using the data from 1, a. Find the 80th percentileb. Find the z value for the 60th percentile3Tire pressure in the John Deere tractor A8 was found to be normally distributed with a mean of 100100 lbs and standard deviation of 20 lbs.a. Find quartiles 1, 2 and 3. b. Estimate the high and low using three standard deviations from the mean.c. Develop a Box Plot for tire pressures - use Excel shapesBox Plot
Sheet21You are given the data below - three variables. Using Excel functions, not data analysis, determine the statistics requested.Week FoodFamilyHouseholdExpenseSizeIncomeLabels to use===>>WFEFam SizeHHI400540,000500330,000900470,000200225,000300232,000700365,000250322,000MeanStd VarRangeMedianSkewHighLow
Sheet31Using the data below, use the data analysis add-in and run the three requested analysis. STACK YOUR RESULTS ON THIS TAB, FORMAT TO 2 PLACES'a. For the three variables above, run the Descriptive Statistics procedure and place the results below the data beginning at the 'Yellow' filled cell. .b. For the three variables above, run the Correlation procedure and place the results below your descriptive statistics.c. For the three variables above, run the Regression procedure with Weekly Food Expenses as 'Y' and Family Size and Household Income as 'X'. Don't choose any optionsWeek FoodFamilyHouseholdExpenseSizeIncomeLabels to use===>>WFEFam SizeHHI400540,000500330,000900470,000200225,000300232,000700365,000250322,000
Comparative Analysis
Xxxxxxxx Yyyyyyyyy
ITM 619
xx/xx/xxxx
Dr. Webb
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This presentation by Mohan Gopalkrishnan, CEO of ERBA Diagnostics Inc., discusses the company's business and growth strategy. ERBA is a global in vitro diagnostics company that provides diagnostic instruments and kits to small and mid-sized hospitals and labs. The presentation outlines ERBA's product portfolio, $75 billion addressable market opportunity, plans to drive operational efficiencies through manufacturing consolidation, and growth strategy of expanding its product lines, customer base, and markets through both organic initiatives and acquisitions. However, the presentation also notes that ERBA's projections are forward-looking statements that are subject to risks and uncertainties that could cause actual results to differ materially.
This document provides information about ERP (Enterprise Resource Planning) software and SAP modules. It discusses what ERP and SAP are, key components of SAP including modules, architecture, and positions. It also covers topics like system landscape, OLTP, dashboards, reports and more. The document appears to be training materials for SAP software provided by ERPTECHLLC.
This document provides information about ERP (Enterprise Resource Planning) software and SAP modules. It discusses what ERP and SAP are, key SAP concepts like the system landscape and architecture, and SAP modules like FI, CO, SD. It also covers topics like data in SAP, roles in SAP implementations, and different types of data analysis tools in SAP like dashboards, reports, and OLAP. The document is intended to provide an overview of fundamental SAP concepts for trainees.
Hierarchical planning - process that translates annual business & marketing plans & demand forecasts into a production plan for a product family (products that share similar characteristics) in a plant or facility leading to the Aggregate Production Plan (APP)
Planning horizon of APP is at least one year & is usually rolled forward by three months every quarter
Includes costs relevant to the aggregate planning decision include inventory, setup, machine operation, hiring, firing, training, & overtime costs
The document provides information on several clinical data management systems and software, including Oracle Clinical, SAS Clinical Software, TCS Clin-E2E Software, Cognos 8 Business Intelligence Software, Symetric Software, Akaza's OpenClinica Software, SigmaSoft's DMSys Software, and Progeny Clinical Software. It discusses their key features for managing clinical trials data such as electronic data capture, reporting, security, compliance with industry standards, and integration with other systems.
AstraZeneca spends over $3 billion annually on research and development across its six international sites. Reducing time-to-market for new drugs is critical as it allows the company to establish brand leadership and generate revenues before generics enter the market. Business Objects business intelligence solutions have provided AstraZeneca visibility into global R&D project data, enabling improved resource utilization, cost control, and the acceleration of drug development timelines. Over 5,000 R&D users can now access information on project schedules, costs and resource usage from a centralized data warehouse. This has provided a competitive advantage by helping AstraZeneca launch products earlier.
Clinical SAS which is broadly used in pharmaceutical industries. After Bpharm and Mpharm, you can learn the Clinical SAS Certification Training in Pune which is offered by Aspire Techsoft SAS Authorized Training Partner in Pune India. If you needed Training on Clinical Trials like SDTM and ADAM CDIC. These are the most used tools in the CRO industry.
The document provides an overview of SAP Advanced Track and Trace for Pharmaceuticals, a solution for tracking pharmaceutical products through the supply chain and complying with regulations requiring serialization. It discusses market requirements around drug serialization, the solution's coverage of key business processes, integration capabilities, and regulatory reporting functionality for countries like Argentina. The technical components include an ECC add-on, integration with SAP ERP and NetWeaver, and support for databases including SAP HANA.
The international community is at a cross-roads, keep allowing counterfeit medicinal drugs, or create legislation that prevents that from happening in the future. The legislation has been created, so how will you ensure compliance?
ERP stands for Enterprise Resource Planning. An ERP system integrates all data and processes of an organization into a unified system using a single database. Major ERP systems include SAP, Oracle, and Microsoft. SAP is currently the largest ERP vendor with 43% of the market. ERP systems are made up of different modules that manage key business functions like finance, supply chain, manufacturing, and human resources. Common career paths in SAP include functional consulting, technical consulting, and administration.
This article discusses how businesses can improve their Sales & Operations Planning (S&OP) process by incorporating scenario planning using probabilistic planning, predictive analytics, and simulation to better address uncertainty, complexity, and risk. Currently, S&OP processes do not adequately account for these factors and can hide critical details. The article recommends using predictive analytics on demand data and scenario planning with quantitative methods to generate alternative plans and assess risks. This will help businesses better balance supply and demand when facing uncertainties.
ERP for the chemical industry is a specialized software solution that helps chemical manufacturers and distributors streamline and automate their operations. It is designed to provide visibility into production and inventory, improve supply chain efficiency, and reduce costs. ERP for the chemical industry can help manufacturers, distributors, and other businesses to better manage their operations, increase efficiency, and reduce costs. ERP software solutions can provide real-time visibility into production, inventory, and financials. Additionally, ERP software can help businesses to manage customer relationships, improve customer service, and maintain compliance with industry regulations.
The making of Arla tactical decision process (S&OP)Jakob Lignell
After the EU milk quota system was scrapped in 2015, Arla Foods faced a significant increase in milk production which destabilized milk prices and pressured farmers. To gain global transparency and coordination, Arla implemented a Sales & Operations Planning (S&OP) process and standardized SAP system. The new process provided integrated demand forecasting, production planning, and inventory projections across Arla's global operations. Early results showed improved forecasting accuracy and coordination. The program increased transparency, improved decision making, and helped Arla maximize value across its global business.
Medical equipment manufacturing business plan exampleupmetrics.co
If you are planning to start a new manufacturing business, the first thing you will need is a business plan. Use our Lanzor - medical equipment manufacturing business plan example created using upmetrics business plan software to start writing your business plan in no time.
Before you start writing your business plan for your new medical equipment manufacturing business, spend as much time as you can reading through some examples of manufacturing business plans. Reading some sample business plans will give you a good idea of what you’re aiming for and also it will show you the different sections that different entrepreneurs include and the language they use to write about themselves and their business plans.
We have created this medical equipment manufacturing business plan example for you to get a good idea about how a perfect manufacturing business plan should look like and what details you will need to include in your stunning business plan.
The document discusses various analytics services including predictive analytics for finance, clinical trials, customer segmentation, and grid analytics. It describes an enterprise data science platform that integrates data from various source systems and covers business vision, processes, data, technologies, and user adoption. The platform includes a big data lake that integrates data across formats and structures with built-in data governance. Specific analytics services are discussed for financial services, retail, power and utility, and oil and gas industries, powered by technologies like SAP HANA, Hadoop, R, and open source.
The document discusses various analytics services including predictive analytics for finance, clinical trials, customer segmentation, and grid analytics. It describes an enterprise data science platform that integrates data from various source systems and covers business vision, processes, data, technologies, and user adoption. The platform includes a big data lake that integrates data across formats and structures with built-in data governance. Specific analytics services are discussed for financial services, retail, power and utility, and oil and gas industries, powered by technologies like SAP HANA, Hadoop, R, and open source.
The document discusses the challenges facing process manufacturers and what they should look for in an ERP system. It summarizes key challenges as ageing infrastructure, high costs, increased customer demands for faster development times, and new environmental/safety standards. It recommends looking for an ERP system with strong process manufacturing functionality like formula/recipe management, quality control, lot tracking, production scheduling, and regulatory compliance. Case studies from various companies demonstrate how an ERP system from Sage helped them address challenges and support growth.
Planimeter was founded in 1997 to provide statistical services for clinical trials. It initially had 4 employees focusing on Phase III-IV trials. In 2005, it started e-business solutions like patient registries and eCRFs. It now has 16 employees and contractors providing full clinical trial support including CDISC modeling, statistical programming, and medical writing. Planimeter aims to be an internationally recognized provider through high quality services and applying rigorous quantitative methods. It focuses on therapeutic areas like neurology and oncology while following standards like CDISC and ISO.
Square Pharmaceuticals Ltd. is the leading pharmaceutical company in Bangladesh that was founded in 1958. It has a market share of about 15% and annual sales of over $90 million. Previously, Square faced difficulties meeting market demand and sharing information between departments due to a lack of integration. To address these issues, Square developed its own ERP system called ConSIL. ConSIL integrated all of Square's departments including production, sales, procurement, and quality control. This allows Square to better forecast demand, track materials, ensure compliance, and respond quickly to customers. The ERP system provides benefits like reduced costs, faster product development, and end-to-end traceability.
Sheet1 Intermediate Business StatisticsExcel Exercise - Enter your.docxlesleyryder69361
Sheet1 Intermediate Business StatisticsExcel Exercise - Enter your name:DIRECTIONS - a. Complete the questions below using the Normal distribution functions. Use Excel shapes to complete Box Plotb. On Sheet 2, complete the shaded areas using Excel functionsc. On sheet 3, perform the analysis using Excel's data analysis add-in.FORMAT YOUR RESULTS TO 2 DECIMAL PLACES ON THE THREE SHEETS.1GMATs are normally distributed with a mean of 500 and standard deviation of 100.Using the normdist function for each question below, donot use your tables:a. Find the probability of selecting an individual whose score is less than or equal to 700b. Find the probability of selecting an individual whose score is greater than or equal to 600c. Find the probability of selecting an individual whose score is between 400 and 600.2Using the data from 1, a. Find the 80th percentileb. Find the z value for the 60th percentile3Tire pressure in the John Deere tractor A8 was found to be normally distributed with a mean of 100100 lbs and standard deviation of 20 lbs.a. Find quartiles 1, 2 and 3. b. Estimate the high and low using three standard deviations from the mean.c. Develop a Box Plot for tire pressures - use Excel shapesBox Plot
Sheet21You are given the data below - three variables. Using Excel functions, not data analysis, determine the statistics requested.Week FoodFamilyHouseholdExpenseSizeIncomeLabels to use===>>WFEFam SizeHHI400540,000500330,000900470,000200225,000300232,000700365,000250322,000MeanStd VarRangeMedianSkewHighLow
Sheet31Using the data below, use the data analysis add-in and run the three requested analysis. STACK YOUR RESULTS ON THIS TAB, FORMAT TO 2 PLACES'a. For the three variables above, run the Descriptive Statistics procedure and place the results below the data beginning at the 'Yellow' filled cell. .b. For the three variables above, run the Correlation procedure and place the results below your descriptive statistics.c. For the three variables above, run the Regression procedure with Weekly Food Expenses as 'Y' and Family Size and Household Income as 'X'. Don't choose any optionsWeek FoodFamilyHouseholdExpenseSizeIncomeLabels to use===>>WFEFam SizeHHI400540,000500330,000900470,000200225,000300232,000700365,000250322,000
Comparative Analysis
Xxxxxxxx Yyyyyyyyy
ITM 619
xx/xx/xxxx
Dr. Webb
waelalturki
Highlight
waelalturki
Highlight
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This presentation by Mohan Gopalkrishnan, CEO of ERBA Diagnostics Inc., discusses the company's business and growth strategy. ERBA is a global in vitro diagnostics company that provides diagnostic instruments and kits to small and mid-sized hospitals and labs. The presentation outlines ERBA's product portfolio, $75 billion addressable market opportunity, plans to drive operational efficiencies through manufacturing consolidation, and growth strategy of expanding its product lines, customer base, and markets through both organic initiatives and acquisitions. However, the presentation also notes that ERBA's projections are forward-looking statements that are subject to risks and uncertainties that could cause actual results to differ materially.
This document provides information about ERP (Enterprise Resource Planning) software and SAP modules. It discusses what ERP and SAP are, key components of SAP including modules, architecture, and positions. It also covers topics like system landscape, OLTP, dashboards, reports and more. The document appears to be training materials for SAP software provided by ERPTECHLLC.
This document provides information about ERP (Enterprise Resource Planning) software and SAP modules. It discusses what ERP and SAP are, key SAP concepts like the system landscape and architecture, and SAP modules like FI, CO, SD. It also covers topics like data in SAP, roles in SAP implementations, and different types of data analysis tools in SAP like dashboards, reports, and OLAP. The document is intended to provide an overview of fundamental SAP concepts for trainees.
Hierarchical planning - process that translates annual business & marketing plans & demand forecasts into a production plan for a product family (products that share similar characteristics) in a plant or facility leading to the Aggregate Production Plan (APP)
Planning horizon of APP is at least one year & is usually rolled forward by three months every quarter
Includes costs relevant to the aggregate planning decision include inventory, setup, machine operation, hiring, firing, training, & overtime costs
The document provides information on several clinical data management systems and software, including Oracle Clinical, SAS Clinical Software, TCS Clin-E2E Software, Cognos 8 Business Intelligence Software, Symetric Software, Akaza's OpenClinica Software, SigmaSoft's DMSys Software, and Progeny Clinical Software. It discusses their key features for managing clinical trials data such as electronic data capture, reporting, security, compliance with industry standards, and integration with other systems.
AstraZeneca spends over $3 billion annually on research and development across its six international sites. Reducing time-to-market for new drugs is critical as it allows the company to establish brand leadership and generate revenues before generics enter the market. Business Objects business intelligence solutions have provided AstraZeneca visibility into global R&D project data, enabling improved resource utilization, cost control, and the acceleration of drug development timelines. Over 5,000 R&D users can now access information on project schedules, costs and resource usage from a centralized data warehouse. This has provided a competitive advantage by helping AstraZeneca launch products earlier.
Clinical SAS which is broadly used in pharmaceutical industries. After Bpharm and Mpharm, you can learn the Clinical SAS Certification Training in Pune which is offered by Aspire Techsoft SAS Authorized Training Partner in Pune India. If you needed Training on Clinical Trials like SDTM and ADAM CDIC. These are the most used tools in the CRO industry.
The document provides an overview of SAP Advanced Track and Trace for Pharmaceuticals, a solution for tracking pharmaceutical products through the supply chain and complying with regulations requiring serialization. It discusses market requirements around drug serialization, the solution's coverage of key business processes, integration capabilities, and regulatory reporting functionality for countries like Argentina. The technical components include an ECC add-on, integration with SAP ERP and NetWeaver, and support for databases including SAP HANA.
The international community is at a cross-roads, keep allowing counterfeit medicinal drugs, or create legislation that prevents that from happening in the future. The legislation has been created, so how will you ensure compliance?
ERP stands for Enterprise Resource Planning. An ERP system integrates all data and processes of an organization into a unified system using a single database. Major ERP systems include SAP, Oracle, and Microsoft. SAP is currently the largest ERP vendor with 43% of the market. ERP systems are made up of different modules that manage key business functions like finance, supply chain, manufacturing, and human resources. Common career paths in SAP include functional consulting, technical consulting, and administration.
This article discusses how businesses can improve their Sales & Operations Planning (S&OP) process by incorporating scenario planning using probabilistic planning, predictive analytics, and simulation to better address uncertainty, complexity, and risk. Currently, S&OP processes do not adequately account for these factors and can hide critical details. The article recommends using predictive analytics on demand data and scenario planning with quantitative methods to generate alternative plans and assess risks. This will help businesses better balance supply and demand when facing uncertainties.
ERP for the chemical industry is a specialized software solution that helps chemical manufacturers and distributors streamline and automate their operations. It is designed to provide visibility into production and inventory, improve supply chain efficiency, and reduce costs. ERP for the chemical industry can help manufacturers, distributors, and other businesses to better manage their operations, increase efficiency, and reduce costs. ERP software solutions can provide real-time visibility into production, inventory, and financials. Additionally, ERP software can help businesses to manage customer relationships, improve customer service, and maintain compliance with industry regulations.
The making of Arla tactical decision process (S&OP)Jakob Lignell
After the EU milk quota system was scrapped in 2015, Arla Foods faced a significant increase in milk production which destabilized milk prices and pressured farmers. To gain global transparency and coordination, Arla implemented a Sales & Operations Planning (S&OP) process and standardized SAP system. The new process provided integrated demand forecasting, production planning, and inventory projections across Arla's global operations. Early results showed improved forecasting accuracy and coordination. The program increased transparency, improved decision making, and helped Arla maximize value across its global business.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Do People Really Know Their Fertility Intentions? Correspondence between Sel...Xiao Xu
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
2. Introduction
CLOVER HEALTH CARE PHARMA
Clover Health Care Pharma offers quality medicines at prices affordable to
the common man.
The company aims to make global products available in the local market at a
price affordable to the common man.
The companys range of products seek to address the ever increasing demands
in the field of Cardiology and Diabetology.
4. Methodology
Create lag for the sales data and make the regression model using SAS
Codes are given below:-
proc import datafile="E:Vishsales.xls" out=work.sales;
run;
Data Sales;set sales;
TruAlag1=lag(TruA);
TruAlag2=lag2(TruA);
TruAlag3=lag3(TruA);
run;
ODS pdf file='E:VishOuput2.pdf';
Proc reg data=work.Sales;
model Chol20=Chol20lag3;
Run;
quit;
quit;
ODS pdf close;
run;
5. Methodology
The data comprises of sales data of all their products for a particular year in
the state of Kerala.
There were 32 products for which lag were created and made regression
model using the lags.