1. What is the purpose of using regression analysis? How may it be used to formulate strategies? Provide examples related to strategy formulation and implementation.
The mathematical process that utilizes observations to come up with the paramount fit through sets of data for the purposes of coming up with the best predictions and estimates about variable behaviors is known as regression analysis. The resulting line of the paramount fit might present itself in two different ways, as a curve or a line. As it follows, the main purpose of utilizing regression analysis is to build a statistical or a mathematical representation of the relationship that exists between enhanced causes understanding and variables for predicting results. As a result, when this analysis is utilized in a business sense the ultimate objective of a manger is usually to come up with outcomes that result to better decisions in business. In business, regression analysis can also be helpful to establishing demand functions, cost functions and elasticity. A manager can, therefore, utilize these estimates to come up with critical strategies in pricing, advertising, and income. For example, a manager can use the regression analysis to find out whether his business’s demand is elastic or not. If it is elastic, he can decide to use the reduction in prices strategy to increase the quantity demanded by a percentage that is larger.
2. How is regression analysis used in forecasting? Provide examples
Regression analysis can predict the results of a certain indicator of business, or a variable that is dependent, based on the relations that occur between other business drivers that are related, or variables that are explanatory. For example, one can forecast the volume of sales based on the amount of money a business has spent on campaigns, and advertisements, and the number of sales people the business has.
3. What is the purpose of using correlation analysis? How may correlation analysis be used in business decisions or in relation to strategy formulation and implementation?
Correlation analysis can be used to find how a line fits with an observation one has made, whether well or badly. It can also be helpful in establishing if the correlation is unity, when the made observations lie with the best- fit line. Correlation analysis can be highly essential in a business. For example, correlation analysis can have a lot of applications in management like a measurement for setting chores, projects and job timelines through measurements and variables of work. It can also be used to assess fluctuations in outcomes through the analysis of certain trends, and it can also be used to validate the collected data through the use of sampling.
4. How may correlation analysis be misused to explain a cause-and-effect relationship?
Correlation analysis can be used to explain a relationship that has a cause and effect dimension because it is useful in establishing the extent to which two variables that are quantitative vary, including the direction and strength of this correlation. In this way, correlation analysis can be used to show the extent to which one quantitative variable forecasts or affects the other, through the analysis of the strength of this relationship. Correlation analysis can also be used to measure the direction of this relationship and in the process show whether the two variables affect each other indirectly or directly.
5. Discuss the relationship between independent and dependent variables with regard to correlation and regression analysis. What is the purpose of these terms?
Variables used in correlation and regression are used to measure the extent of the relationship that exists between two or more variables in ways that are different but related. When it comes to regression analysis a variable that is dependent is considered to be the variable that acts as a function for one or more variables that are independent. In regression analysis, it is assumed that, for any figure given for a variable that is independent, figures of a variable that is dependent are distributed around some mean. On the other hand, correlation analysis measures the extent to which two or more variables associate. This method of analysis, therefore, assumes that, for any two or more sets of value assumed under a set of predetermined conditions, variation between the existing variables is random and is distributed in a normal pattern.