disc post and 2 replies 3 parts

• 1.
• Based on the text on regression assumptions and your additional research, discuss the potential impact of assumption violation on interpretation of regression results.
• Is there any influence of the assumption violation on the business decision making? If so, how? If not, why?

Week 1: Provide your initial discussion post to the question. Be sure to include references to any resources you used. You should use at least one resource to help you with your initial discussion.

2. Regression assumption is used as a simple linear regression or multiple regression. The difference between simple linear regression and multiple regression is the simple regression uses one variable and one dependent variable were as the multiple regression uses two or more independent variables. With these two different regression assumptions there is a potential for impact of assumption violation on the interpretation of regression results. “Often, the impact of an assumption violation on the linear regression result depends on the extent of the violation (such as the how inconstant the variance of Y is, or how skewed the Y population distribution is. ” (Does your data violate linear regression assumptions, n.d.). So, there could be small errors that would not affect the results of the regression but some errors within the regression could make the results unusable.

• Is there any influence of the assumption violation on the business decision making? If so, how? If not, why?

Yes, there is influence of the assumption violation on the business decision because if there are errors within the regression and the results are wrong and the business makes the decision off an useless regression result the business could loss profit or other things that those decisions are made off of. “If this assumption is not met, then the predictions may systematically overestimate the actual values for one range of values on a predictor variable and underestimate them for another” (Trident University, 2018). So in this case, if the predictions are overestimated then the business could make a decision to buy less of whatever product and could be short in the long run because the prediction or assumptions was not correct.

3.Utilizing the regression assumptions to make crucial business decisions can be an effective tool provided the error rate closely aligns with the dependent variable. Regression assumptions where the SSE does not closely align with the variable can lead to inconclusive assumptions. This could lead to business decisions that can have a detrimental effect on your business (Foltz, 2015; Trident University International, 2018).

When it comes to assumption violations, they are best avoided altogether. Violations in assumptions then the forecasts, confidence intervals, and scientific insights yielded by a regression model may be inefficient or seriously biased or misleading (Regression Diagnostics, n.d.).

Just as stated, any violation of the regression assumptions can cause the data that is being shown and forecasted to be inaccurate. This can cause complications when trying to plan and prepare for future purchases and sales. If the data is completely inaccurate and misleading, it can cause individuals to make decisions based on this information and may cause lasting effects. Business decisions are made every day with available information and forecasting models and methods to plan for future events and positions the company or business may want to take. Each company will predict their sales and financial information to allow for the ability to attract new investors. Businesses need to turn a profit, and one way to do that is to attract new investors who will take a stake or interest in the company financially. To be able to show a forecasted profit margin exceeding what was expected shows, potential investors, that the company has the ability to make money. Good decision making also plays a large part in how profitable a company is, but the decision-making process is based on available information which includes forecasted information.