There are several tools you can use in order to maximize efficiency and overall success for your business
1st:
Within the business world, there are several tools you can use in order to maximize efficiency and overall success for your business. One such tool is called regression analysis. Regression analysis is defined as the estimation of relationships between a dependent variable and one or more independent variables (CFI, n.d.). This type of analysis is important due to the simple fact that you can analyze what factors within your business could be causing positive or negative correlations that affect operations and ultimately overall success. Researchers can analyze specific data within their own organizations using regression analysis that can help them predict what future operations will look like and how the company may be able to overcome certain obstacles.
The first example that regression analysis is utilized would be within the automobile industry. As we have seen, the demand is high for vehicles in today’s market and the supply has been low and trying to catch up from all the issues during the height of COVID-19. What these dealers can analyze is to look at the different variables: How much are customers willing to pay for new vs. used vehicles? How much are we willing to pay for used vehicles to build up our supply? What kinds of vehicles do the customers prefer at this time: cars, trucks, or SUVs? Using the regression analysis, the dealers can take all these factors into account and determine how to succeed under the current conditions of the market.
The second example that regression analysis is utilized would be with employee turnover within a particular business. A business can see why employees move on by analyzing what the reasons are for the employees leaving. Are the employees needing more flexibility, leaving for a higher salary, or for poor management? Using regression analysis and building a scatter plot, you can use each one of these factors and put them up against employee turnover and see what particular correlation there is.
The third and final example that regression analysis is utilized would be video games and the types of games that particular gamers prefer to play. Do you prefer first person shooters, strategy, role-playing games, or sports games? Using regression analysis, we can look at which type of game that most gamers would pick more often than not.
CFI (n.d.). Corporate Finance Institute. Retrieved May 16th, 2022 from https://corporatefinanceinstitute.com/resources/kn…
2nd:
Regression analysis involves looking at your data points in a scatterplot and determining if they appear to form a relatively straight line. If they do, and if one variable is dependent and the other is independent, you can perform the calculations for linear regression. Depending on how large the variation from the line is, different people would likely draw their line in different places. To overcome this and obtain the most accurate result, the least-squares regression line is used. This is created by measuring the errors, or vertical distances, between where a data point would be on your predicted line and where it actually falls. The sum of squared errors (SSE) is calculated by determining the errors for all data points along your line and adding them up (Illowsky & Dean, 2013). The SSE being minimized means your line has the least amount of variation between predicted and actual values, which is the least-squares regression line.
Researchers would be interested in using regression analysis because it allows for predictions of future behavior. Once you have determined your least-squares regression line, you can extend that line out however far you want to, which allows you to examine the ongoing relationship between your two variables. This can also show if past behavior fits the predicted line or if there were outliers that might have skewed the data. When combined with correlation, researchers can determine if their independent variable will continue to increase or decrease based on their dependent variable. Regression analysis will also show if their chosen dependent variable has no effect on their independent variable, in which case they would know that variable has no causal effect on their business or research.
One example of using regression analysis in business would be predicting future demand levels for a particular product so the business can adjust supply and/or prices accordingly. Sales of tacos and other similar food items usually spike around Cinco de Mayo, so a regression analysis would allow businesses that carry these products to stock up accordingly. Another example is advertising, where a regression analysis can be used to predict how many people will drive past a particular billboard, or view a particular ad on social media, so the company can determine how much to spend on which ads and where, in order to maximize their return on investment. A third example is efficiency, where a regression analysis could be used to examine the relationship between wait times in a drive-thru or checkout, or on hold with customer service, and the number of complaints and negative reviews received. Common sense would indicate that less waiting while still providing good products or services would lead to less complaints, but this would allow for a quantifiable number to determine where the optimal wait time is to minimize complaints.
References:
Illowsky, B. & Dean, S. (2013). Introductory Statistics. Openstax. https://openstax.org/details/introductory-statisti…
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