Case Study Description of Practical Regression: Maximum Likelihood Estimation
This is the eighth in a series of lecture notes which, if tied together into a textbook, might be entitled "Practical Regression." The purpose of the notes is to supplement the theoretical content of most statistics texts with practical advice based on nearly three decades of experience of the author, combined with over one hundred years of experience of colleagues who have offered guidance. As the title "Practical Regression" suggests, these notes are a guide to performing regression in practice.This technical note discusses maximum likelihood estimation (MLE). The note explains the concept of goodness of fit and why MLE is a powerful alternative to R-squared. The note follows a simple example that develops the intuition of MLE as well as the computation of the likelihood score and the algorithm used to estimate coefficients in MLE models.
Swot Analysis of "Practical Regression: Maximum Likelihood Estimation" written by David Dranove includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Mle Regression facing as an external strategic factors. Some of the topics covered in Practical Regression: Maximum Likelihood Estimation case study are - Strategic Management Strategies, Financial management, Market research and Finance & Accounting.
Some of the macro environment factors that can be used to understand the Practical Regression: Maximum Likelihood Estimation casestudy better are - – talent flight as more people leaving formal jobs, increasing commodity prices, challanges to central banks by blockchain based private currencies, banking and financial system is disrupted by Bitcoin and other crypto currencies, increasing transportation and logistics costs, supply chains are disrupted by pandemic , increasing inequality as vast percentage of new income is going to the top 1%,
competitive advantages are harder to sustain because of technology dispersion, geopolitical disruptions, etc
Introduction to SWOT Analysis of Practical Regression: Maximum Likelihood Estimation
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Practical Regression: Maximum Likelihood Estimation case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Mle Regression, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Mle Regression operates in.
According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.
SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix
SWOT analysis of Practical Regression: Maximum Likelihood Estimation can be done for the following purposes –
1. Strategic planning using facts provided in Practical Regression: Maximum Likelihood Estimation case study
2. Improving business portfolio management of Mle Regression
3. Assessing feasibility of the new initiative in Finance & Accounting field.
4. Making a Finance & Accounting topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Mle Regression
Strengths Practical Regression: Maximum Likelihood Estimation | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Mle Regression in Practical Regression: Maximum Likelihood Estimation Harvard Business Review case study are -
Sustainable margins compare to other players in Finance & Accounting industry
– Practical Regression: Maximum Likelihood Estimation firm has clearly differentiated products in the market place. This has enabled Mle Regression to fetch slight price premium compare to the competitors in the Finance & Accounting industry. The sustainable margins have also helped Mle Regression to invest into research and development (R&D) and innovation.
Innovation driven organization
– Mle Regression is one of the most innovative firm in sector. Manager in Practical Regression: Maximum Likelihood Estimation Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Low bargaining power of suppliers
– Suppliers of Mle Regression in the sector have low bargaining power. Practical Regression: Maximum Likelihood Estimation has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Mle Regression to manage not only supply disruptions but also source products at highly competitive prices.
Operational resilience
– The operational resilience strategy in the Practical Regression: Maximum Likelihood Estimation Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.
Learning organization
- Mle Regression is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Mle Regression is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Practical Regression: Maximum Likelihood Estimation Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Organizational Resilience of Mle Regression
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Mle Regression does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Digital Transformation in Finance & Accounting segment
- digital transformation varies from industry to industry. For Mle Regression digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Mle Regression has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.
Ability to recruit top talent
– Mle Regression is one of the leading recruiters in the industry. Managers in the Practical Regression: Maximum Likelihood Estimation are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Highly skilled collaborators
– Mle Regression has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Practical Regression: Maximum Likelihood Estimation HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Effective Research and Development (R&D)
– Mle Regression has innovation driven culture where significant part of the revenues are spent on the research and development activities. This has resulted in, as mentioned in case study Practical Regression: Maximum Likelihood Estimation - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Diverse revenue streams
– Mle Regression is present in almost all the verticals within the industry. This has provided firm in Practical Regression: Maximum Likelihood Estimation case study a diverse revenue stream that has helped it to survive disruptions such as global pandemic in Covid-19, financial disruption of 2008, and supply chain disruption of 2021.
Training and development
– Mle Regression has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Practical Regression: Maximum Likelihood Estimation Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
Weaknesses Practical Regression: Maximum Likelihood Estimation | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Practical Regression: Maximum Likelihood Estimation are -
High bargaining power of channel partners
– Because of the regulatory requirements, David Dranove suggests that, Mle Regression is facing high bargaining power of the channel partners. So far it has not able to streamline the operations to reduce the bargaining power of the value chain partners in the industry.
Interest costs
– Compare to the competition, Mle Regression has borrowed money from the capital market at higher rates. It needs to restructure the interest payment and costs so that it can compete better and improve profitability.
High cash cycle compare to competitors
Mle Regression has a high cash cycle compare to other players in the industry. It needs to shorten the cash cycle by 12% to be more competitive in the marketplace, reduce inventory costs, and be more profitable.
High dependence on existing supply chain
– The disruption in the global supply chains because of the Covid-19 pandemic and blockage of the Suez Canal illustrated the fragile nature of Mle Regression supply chain. Even after few cautionary changes mentioned in the HBR case study - Practical Regression: Maximum Likelihood Estimation, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Mle Regression vulnerable to further global disruptions in South East Asia.
Capital Spending Reduction
– Even during the low interest decade, Mle Regression has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
No frontier risks strategy
– After analyzing the HBR case study Practical Regression: Maximum Likelihood Estimation, it seems that company is thinking about the frontier risks that can impact Finance & Accounting strategy. But it has very little resources allocation to manage the risks emerging from events such as natural disasters, climate change, melting of permafrost, tacking the rise of artificial intelligence, opportunities and threats emerging from commercialization of space etc.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Practical Regression: Maximum Likelihood Estimation, is just above the industry average. Mle Regression needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.
Products dominated business model
– Even though Mle Regression has some of the most successful products in the industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. firm in the HBR case study - Practical Regression: Maximum Likelihood Estimation should strive to include more intangible value offerings along with its core products and services.
Increasing silos among functional specialists
– The organizational structure of Mle Regression is dominated by functional specialists. It is not different from other players in the Finance & Accounting segment. Mle Regression needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Mle Regression to focus more on services rather than just following the product oriented approach.
Aligning sales with marketing
– It come across in the case study Practical Regression: Maximum Likelihood Estimation that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Practical Regression: Maximum Likelihood Estimation can leverage the sales team experience to cultivate customer relationships as Mle Regression is planning to shift buying processes online.
Need for greater diversity
– Mle Regression has taken concrete steps on diversity, equity, and inclusion. But the efforts so far has resulted in limited success. It needs to expand the recruitment and selection process to hire more people from the minorities and underprivileged background.
Opportunities Practical Regression: Maximum Likelihood Estimation | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities highlighted in the Harvard Business Review case study Practical Regression: Maximum Likelihood Estimation are -
Lowering marketing communication costs
– 5G expansion will open new opportunities for Mle Regression in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Finance & Accounting segment, and it will provide faster access to the consumers.
Buying journey improvements
– Mle Regression can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Practical Regression: Maximum Likelihood Estimation suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
Developing new processes and practices
– Mle Regression can develop new processes and procedures in Finance & Accounting industry using technology such as automation using artificial intelligence, real time transportation and products tracking, 3D modeling for concept development and new products pilot testing etc.
Identify volunteer opportunities
– Covid-19 has impacted working population in two ways – it has led to people soul searching about their professional choices, resulting in mass resignation. Secondly it has encouraged people to do things that they are passionate about. This has opened opportunities for businesses to build volunteer oriented socially driven projects. Mle Regression can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Leveraging digital technologies
– Mle Regression can leverage digital technologies such as artificial intelligence and machine learning to automate the production process, customer analytics to get better insights into consumer behavior, realtime digital dashboards to get better sales tracking, logistics and transportation, product tracking, etc.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Mle Regression can use these opportunities to build new business models that can help the communities that Mle Regression operates in. Secondly it can use opportunities from government spending in Finance & Accounting sector.
Manufacturing automation
– Mle Regression can use the latest technology developments to improve its manufacturing and designing process in Finance & Accounting segment. It can use CAD and 3D printing to build a quick prototype and pilot testing products. It can leverage automation using machine learning and artificial intelligence to do faster production at lowers costs, and it can leverage the growth in satellite and tracking technologies to improve inventory management, transportation, and shipping.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Mle Regression can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Mle Regression to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Mle Regression to hire the very best people irrespective of their geographical location.
Better consumer reach
– The expansion of the 5G network will help Mle Regression to increase its market reach. Mle Regression will be able to reach out to new customers. Secondly 5G will also provide technology framework to build new tools and products that can help more immersive consumer experience and faster consumer journey.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Mle Regression is facing challenges because of the dominance of functional experts in the organization. Practical Regression: Maximum Likelihood Estimation case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Finance & Accounting industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Mle Regression can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Mle Regression can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.
Low interest rates
– Even though inflation is raising its head in most developed economies, Mle Regression can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.
Threats Practical Regression: Maximum Likelihood Estimation External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Practical Regression: Maximum Likelihood Estimation are -
Environmental challenges
– Mle Regression needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Mle Regression can take advantage of this fund but it will also bring new competitors in the Finance & Accounting industry.
Stagnating economy with rate increase
– Mle Regression can face lack of demand in the market place because of Fed actions to reduce inflation. This can lead to sluggish growth in the economy, lower demands, lower investments, higher borrowing costs, and consolidation in the field.
New competition
– After the dotcom bust of 2001, financial crisis of 2008-09, the business formation in US economy had declined. But in 2020 alone, there are more than 1.5 million new business applications in United States. This can lead to greater competition for Mle Regression in the Finance & Accounting sector and impact the bottomline of the organization.
Consumer confidence and its impact on Mle Regression demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Practical Regression: Maximum Likelihood Estimation, Mle Regression may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Finance & Accounting .
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Mle Regression with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
Easy access to finance
– Easy access to finance in Finance & Accounting field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Mle Regression can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Regulatory challenges
– Mle Regression needs to prepare for regulatory challenges as consumer protection groups and other pressure groups are vigorously advocating for more regulations on big business - to reduce inequality, to create a level playing field, to product data privacy and consumer privacy, to reduce the influence of big money on democratic institutions, etc. This can lead to significant changes in the Finance & Accounting industry regulations.
Aging population
– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.
Technology disruption because of hacks, piracy etc
– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.
High dependence on third party suppliers
– Mle Regression high dependence on third party suppliers can disrupt its processes and delivery mechanism. For example -the current troubles of car makers because of chip shortage is because the chip companies started producing chips for electronic companies rather than car manufacturers.
Shortening product life cycle
– it is one of the major threat that Mle Regression is facing in Finance & Accounting sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Capital market disruption
– During the Covid-19, Dow Jones has touched record high. The valuations of a number of companies are way beyond their existing business model potential. This can lead to capital market correction which can put a number of suppliers, collaborators, value chain partners in great financial difficulty. It will directly impact the business of Mle Regression.
Weighted SWOT Analysis of Practical Regression: Maximum Likelihood Estimation Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Practical Regression: Maximum Likelihood Estimation needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants.
We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –
First stage for doing weighted SWOT analysis of the case study Practical Regression: Maximum Likelihood Estimation is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.
Second stage for conducting weighted SWOT analysis of the Harvard case study Practical Regression: Maximum Likelihood Estimation is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.
Third stage of constructing weighted SWOT analysis of Practical Regression: Maximum Likelihood Estimation is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Mle Regression needs to make to build a sustainable competitive advantage.