Course Reflective Writeup


Question Description

In your reflective write-up, take 3-5 pages (double spaced) to discuss the methods introduced in the class (regression, forecasting, foresight, and agile strategy).

Specifically, what new insights did you gain? Which one(s) will be most useful in your career? What are potential applications for the methods in your current position? Have you begun using any of the methods outside the class?

Just write with in your own words and the related meterials is attached.

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I. Regression Regression analysis is a family of statistical tools that can help sociologists better understand and predict the way that people act and interact. Regression analysis is used to build mathematical models to predict the value of one variable from knowledge of another. Although statistical methods of correlation offer researchers techniques to help them better understand the degree to which two variables are consistently related, such knowledge alone is typically insufficient to predict behavior. Simple linear regression allows the value of one dependent variable to be predicted from the knowledge of one independent variable. Multiple linear regression can be used to develop models to predict the value of a dependent variable from the knowledge of the value of more than one independent variable. For example, a sociologist interested in the behavior of small groups might want to determine whether or not the efficacy of the decisions made in small groups could be predicted from the number of people in the group. Although larger group size could mean that there are more ideas, more contribution to the thinking process, and a larger potential for synergistic thinking, a larger group could also mean that more time would be required to reach a decision, the competition of ideas could lead to confusion, and coalitions could form within the group and make it harder to resolve disagreements. A predictive model for group size versus efficacy of decision making could be developed by setting up an experiment that compared the efficacy of decision making on the same problem for groups of various sizes. The slope of the line of best fit passing through the data points on the scatter plot could be mathematically calculated, using these data points to determine the equation of the simple regression line. This equation could then be used by the sociologist to recommend optimal group size for similar types of decisions or projects based on the single variable of number of group members. The problem with drawing a line of best fit through a scatter plot, of course, is that unless all the pairs of data fall on one straight line, it is possible to draw multiple lines through a data set. The question faced by the researcher is how to determine which of these possible lines will yield the best predictions of the dependent variable from the independent variable. This can be accomplished mathematically through residual analysis. In regression analysis, a residual is defined as the difference between the actual y values and the predicted y values, or y - y^. To find the line of best fit, it is important to reduce the distance between the points on the scatter plot and the line. This is done by minimizing the sum of the squares of the residuals in order to find the line of best fit. By looking at the residuals, a researcher can better understand how well the regression line fits past data in order to estimate how well it will predict future data. II. Forecasting Forecasting involves using past data to generate a number, set of numbers, or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Forecasting provides information about the potential future events and their consequences for the organization. It may not reduce the complications and uncertainty of the future. However, it increases the confidence of the management to make important decisions. Forecasting is the basis of premising. Quantitative forecasting techniques are generally more objective than their qualitative counterparts. Quantitative forecasts can be timeseries forecasts or forecasts based on associative models. Time-series data may have underlying behaviors that need to be identified by the forecaster. In addition, the forecast may need to identify the causes of the behavior. Some of these behaviors may be patterns or simply random variations. Among the patterns are: • Trends, are long-term movements (up or down) in the data. • Seasonal, it produces short-term variations that are usually related to the time of year, month, or even a particular day, as witnessed by retail sales at Christmas or the spikes in banking activity on the first of the month and on Fridays. • Cyclical, are wavelike variations lasting more than a year that are usually tied to economic or political conditions. • Random (Irregular) variations that do not reflect typical behavior, such as a period of extreme weather or a union strike. Among the time-series models, the simplest is the naïve forecast. A naive forecast simply uses the actual demand for the past period as the forecasted demand for the next period. This, of course, makes the assumption that the past will repeat. It also assumes that any trends, seasonality, or cycles are either reflected in the previous period's demand or do not exist. III. Foresight Foresight is the capacity to think systematically about the future to inform decision making today. It is a cognitive capacity that we need to develop as individuals, as organizations and as a society. In individuals, it is usually an unconscious capacity and needs to be surfaced to be used in any meaningful way to inform decision making, either as individuals or in organizations. It’s a capacity we use every day. Foresight is first and foremost a state of mind that determines how you think about the future. It underpins how you design foresight approaches and how you implement them in your organizations. It needs ways of thinking and doing that are unlike those required for conventional strategic planning processes. Foresight is therefore a strategic thinking capacity. Done well, it expands perceptions of future options available to the organization and enhances the operational context in which strategy is developed. Its use allows new strategic options to emerge and proactive responses to change to be developed. Done less well, it usually generates an interesting experience but there is little change to how strategy is developed or the understanding of the scope of change shaping the organization’s future. IV. Agile Strategy Agile strategy is an approach to project management. Agile strategy break tasks into small increments with no direct long term planning. The idea behind the Agile strategy is that it can be adapted and adjusted as a project develops to reflect the changes that are happening all-around the organization. Since things like outside market forces and consumer interests can’t necessarily be forecast at the start of the project, using the Agile strategy will give you the opportunity to change course as needed to make sure the final result of the project is something that will be of value to the organization. Traditional facet of project management that most leaders are familiar with is the idea of setting project requirements right from the start. Usually, the scope of the project is established clearly before any work begins, and all work going forward is guided by the goals that have been laid out. The Agile methodology ignores this approach and instead establishes the requirements for the project on an ongoing basis during the project. That means that what ends up being accomplished within the project could be very different than what was expected at the start. The advantage to this kind of management is that the project is flexible enough to develop over time until it precisely suits the needs of the market. You don’t run the risk of creating a product that has no market at the end of the project because the project teams have the ability to adapt the final deliverables all throughout the process. Being responsive and flexible is typically a good thing for businesses because they can avoid falling out of touch with what the market is asking for. ...
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