# Linear Regression Analysis

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## Linear Regression Analysis

Linear regressions analysis is the process of linking a dependent variable with an independent variable to find trends. Simple linear regression is the term for when there is only one independent variable; in contrast, multiple linear regression is the opposite and is adopted when there are multiple independent variables. Linear regression analysis can be used effectively to predict future proceedings as it can identify trends between variables. Furthermore, linear regression analysis may be utilised to identify why fluctuations in the dependent variable happen based on change from the independent variable.

Companies will use linear regression analysis to understand what causes certain trends to occur, for example, you could use linear regression to research obesity rates. The dependent variable could be body mass index (BMI) and the independent variables you wish to study could be: amount of exercise per day, fruit and veg eaten per day and fast-food meals eaten per day; making this a multiple linear regression (because there is more than one independent variable). In the study you might find that people with a higher body mass index exercise less but eat the same number of fast-food meals as people with a lower BMI. From this you could conclude that doing more exercising would be more beneficial to lowering obesity than lowering the number of unhealthy meals, allowing the company to tailor their product for exercise, rather than healthier eating.

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