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 - – supply chains are disrupted by pandemic , there is backlash against globalization, customer relationship management is fast transforming because of increasing concerns over data privacy, increasing government debt because of Covid-19 spendings, cloud computing is disrupting traditional business models, increasing household debt because of falling income levels, central banks are concerned over increasing inflation,
banking and financial system is disrupted by Bitcoin and other crypto currencies, increasing commodity 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 -
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.
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.
Strong track record of project management
– Mle Regression is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
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.
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.
High switching costs
– The high switching costs that Mle Regression has built up over years in its products and services combo offer has resulted in high retention of customers, lower marketing costs, and greater ability of the firm to focus on its customers.
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.
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.
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.
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.
Successful track record of launching new products
– Mle Regression has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Mle Regression has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.
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.
Skills based hiring
– The stress on hiring functional specialists at Mle Regression has created an environment where the organization is dominated by functional specialists rather than management generalist. This has resulted into product oriented approach rather than marketing oriented approach or consumers 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.
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.
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.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Mle Regression is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Practical Regression: Maximum Likelihood Estimation can help to provide better information regarding products and services. It can also build an online community to further reach out to potential 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.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Practical Regression: Maximum Likelihood Estimation, in the dynamic environment Mle Regression has struggled to respond to the nimble upstart competition. Mle Regression has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Workers concerns about automation
– As automation is fast increasing in the segment, Mle Regression needs to come up with a strategy to reduce the workers concern regarding automation. Without a clear strategy, it could lead to disruption and uncertainty within the organization.
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.
Lack of clear differentiation of Mle Regression products
– To increase the profitability and margins on the products, Mle Regression needs to provide more differentiated products than what it is currently offering in the marketplace.
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 -
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.
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.
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.
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.
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.
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.
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.
Loyalty marketing
– Mle Regression has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.
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.
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.
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.
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.
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 -
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.
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.
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.
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.
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 .
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Mle Regression can face downward pressure on margins from increasing competition from international players. The international players have stable revenue in their home market and can use those resources to penetrate prominent markets illustrated in HBR case study Practical Regression: Maximum Likelihood Estimation .
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.
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.
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.
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.
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.
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.
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.