Regression is a statistical technique applied in investing, finance, and other disciplines that tries to determine the strength and character of the association between one dependent variable and a list of other independent variables. Regression analysis is designed to use observations to quantify the association between an objective variable and a set of independent variables. In simple regression, there is just one independent variable and one dependent variable, which can be adequately approximated by a linear function.
Concerning the importance of regression in a business, it helps in making informed strategic plans. For instance, managers can weigh the components of a business that are most important to prioritize them. They also identify how various business factors are related to one another. The technique is of value since comparing variables of concern in decision making is a statistical way of testing expected outcomes. Regression analysis also helps an organization understand its data and effectively use the data points in strategic decision-making through business analytical methods. The regression technique of forecasting means examining the relationships between data points, which can help managers to foretell sales in the near and long course, know inventory levels, and learn supply and demand.
I chose the dataset of Test Scores. The dependent variable will be Exam Grades, and the independent variable is Minutes of Study Time. It will be necessary to know if the amount of time a student spends studying has an impact on their exam results. As such, I need to predict if Exam Results depend on the time spent studying. I will assume that the more time spent studying, the higher the exam grades.
Table1: Regression Statistics
|Adjusted R Square||0.842993358|
|R Squared, which is 0.8495, means that 84.95% of Y’s variation is being explained by X.|
|Twenty-five observations mean that we had 25 variables.|
Table 2: Anova
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|Coefficients||Standard Error||t Stat||P-value||Lower 95%||Upper 95%|
|Minutes Study Time, X||0.212508131||0.018648256||11.39560335||6.15511E-11||0.173931274||0.251084989|
Under the coefficients, we have the y-intercept and slope of X variable, which gives us our regression line as y=0.2125m+48.0763
Minutes Study Time’s coefficient is 0.2125, meaning that there is a positive relationship between Exam Grades and Minutes Study Time.
P-value is 6.155511E-11 and is less than 0.05, which means that our coefficients are significant; hence we had significant regression.
A unit increase in Minutes Study Time will lead to a 0.2125 increase in Exam Grades from our regression line.