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 - – increasing government debt because of Covid-19 spendings, digital marketing is dominated by two big players Facebook and Google, technology disruption, there is backlash against globalization, geopolitical disruptions, there is increasing trade war between United States & China, customer relationship management is fast transforming because of increasing concerns over data privacy,
cloud computing is disrupting traditional business models, increasing energy prices, 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 -
Superior customer experience
– The customer experience strategy of Mle Regression in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
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.
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.
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.
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.
Analytics focus
– Mle Regression is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by David Dranove can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
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 lead change in Finance & Accounting field
– Mle Regression is one of the leading players in its industry. Over the years it has not only transformed the business landscape in its segment but also across the whole industry. The ability to lead change has enabled Mle Regression in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
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.
High brand equity
– Mle Regression has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Mle Regression to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
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.
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.
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 -
Slow to strategic competitive environment developments
– As Practical Regression: Maximum Likelihood Estimation HBR case study mentions - Mle Regression takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.
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.
High operating costs
– Compare to the competitors, firm in the HBR case study Practical Regression: Maximum Likelihood Estimation has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Mle Regression 's lucrative customers.
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.
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.
Slow decision making process
– As mentioned earlier in the report, Mle Regression has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Mle Regression even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.
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.
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.
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.
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.
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.
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 -
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.
Building a culture of innovation
– managers at Mle Regression can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Finance & Accounting segment.
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.
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.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Mle Regression can build a diversified supply chain model across various countries in - South East Asia, India, and other parts of the world. This reconfiguration of global supply chain can help, as suggested in case study, Practical Regression: Maximum Likelihood Estimation, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
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.
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.
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.
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.
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.
Use of Bitcoin and other crypto currencies for transactions
– The popularity of Bitcoin and other crypto currencies as asset class and medium of transaction has opened new opportunities for Mle Regression in the consumer business. Now Mle Regression can target international markets with far fewer capital restrictions requirements than the existing system.
Learning at scale
– Online learning technologies has now opened space for Mle Regression to conduct training and development for its employees across the world. This will result in not only reducing the cost of training but also help employees in different part of the world to integrate with the headquarter work culture, ethos, and standards.
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 -
Increasing wage structure of Mle Regression
– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Mle Regression.
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.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Mle Regression in the Finance & Accounting industry. The Finance & Accounting industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.
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.
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.
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.
High level of anxiety and lack of motivation
– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Mle Regression needs to understand the core reasons impacting the Finance & Accounting industry. This will help it in building a better workplace.
Backlash against dominant players
– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Mle Regression business can come under increasing regulations regarding data privacy, data security, etc.
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.
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.
Instability in the European markets
– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Mle Regression will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.
Technology acceleration in Forth Industrial Revolution
– Mle Regression has witnessed rapid integration of technology during Covid-19 in the Finance & Accounting industry. As one of the leading players in the industry, Mle Regression needs to keep up with the evolution of technology in the Finance & Accounting sector. According to Mckinsey study top managers believe that the adoption of technology in operations, communications is 20-25 times faster than what they planned in the beginning of 2019.
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.
