Describe 2 -3 combination of independent and dependent variables that you could test using a regression analysis
Statistics offer different techniques for analyzing numerical data to make inferences based on the specified sample. Regression analysis makes inferences using dependent and independent variables to describe correlations among the collected data (Draper, 2014). Depending on the circumstances, researchers can combine both variables in regression analysis.
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For example, to understand the connection between a student's performances, i need to assess how many hours of study and how the number of hours affects his grades. In this case, the independent variable is the students' performance, which are the outcomes of the exams and the dependent being the amount of time spent studying.
Regression analysis is applicable when assessing the amount of time an employee is expected to rest at night to be productive at work or to remain alert. The sleeping hours are the independent variable, while performance at work is the dependent variable (Draper, 2014). The connection between these two elements can be asses to measure how many hours of rest employees need thebe productive.
Similarly, both independent and dependent variables can be used to assess how violent video games influence children's behavior either positively or negatively. The main elements to be evaluated are the levels of violence, the hours spent, and the impact on children's behavior (Draper, 2014). The independent variable, in this case, is the levels of exposure. In contrast, this variable's relationship is the behavior displayed due to playing games several times, which in this case, is the dependent variable (Draper, 2014). Understanding the two relationships can be used to assess how violent video games influence a child behavior.
2. What type of results could regression analysis yield? How could you use the knowledge gained from the test?
Making inferences using the regression technique is essential because it reveals the connection of different variables. Understanding the regression is critical for assessing the correlation between the two variables. Regression can describe the type of either association that can be directly or inverse relationship (Anseel, 2010). Understanding the interaction of variables is critical in explaining how the variables change when exposed to different circumstances (Anseel, 2010). Regression analysis reveals the relationship between a set of independent and dependent variables by producing a regression equation (Anseel, 2010). In this case, the coefficients represent the relationship levels between these two variables, which are essential in making the prediction.
Researchers can use multiple regressions to see the combined effects of the two variables. Regression analysis is essential for researchers because they can determine how one variable can influence the other variables (Ansel, 2010). The most crucial consideration is assessing the variable to determine how they are related to each other. Making such an assessment depends on how data is separated and the type of model used to fit the description.
3. Describe a specific organization application of correlation and regression that you will use in your future career?
Both regression analysis and correlation are techniques applicable in the manufacturing sector. Manufacturers can use these two techniques to control their supply chain. For example, a company can use these models to determine supplies needed based on the proportion (Allison, 2009). Supplies mainly depend on several factors, such as the price, the demand, and the quantity produced. However, determining product prices' effects is the primary consideration that might affect the supply and the demand for a product.
4. Describe a situation in your organization's current or former workplace for which it would be appropriate to use correlation and regression to predict a future outcome that the company may be interested in.
In marketing, both regression and correlation measurement techniques are crucial. Making inferences about the demand for a product can be done by assessing consumer behavior patterns based on the changes in the product price and packaging. As the head of sales, I can use the correlation and regression techniques to review the best prices and packaging quantities for the product based on consumer buying trends (Allison, 2009). Using these techniques, I can change the marketing strategy to offer the best products that would be widely accepted by consumers. The analysis can also be applied to measure the levels of productivity among employees, which can be useful in determining promotions, salary increments, and other non –monetary packages.
Regression analysis is more of an inferential statistic since it helped in detraining whether the broader population can predict the relationship observed in the sample. Regression analysis is an essential model in making a prediction based on a specific relationship that is assessed to determine their correlations (Allison, 2009). These relationships are critical for evaluating particular patterns and trends that can be inferred by finding whether variables are dependent or independent. The variables like the levels of performance versus the number of hours studying and the relationship between sleeping hours and employee performance and children's behavior versus violent video games show the importance of regression analysis.
In the manufacturing sector, the regression and correlation analysis are equally important in several aspects. The two techniques can be used to manage the supply chain. They can be used to change marketing strategies after determining consumer-buying patterns. The significant detriment comes from regression analysis since it is the most effective means of assessing the relationship of different variables and their changes under various circumstances.
References
A llison, P. D. (2009). Fixed effects regression models (Vol. 160). London: SAGE publications.
Anseel, F. L. (2010). Response rates in organizational science, 1995–2008: A meta-analytic review and guidelines for survey researchers. Journal of Business and Psychology , 335-349.
Draper, N. R. (2014). Applied regression analysis. London: John Wiley & Sons.
