Multiple and Linear Regressions, Essay Example

Regression is a statistics-based formula used to determine strength of relationship between one dependent variable and a set of other independent variables. It contains within it several branches that include; simple linear regression, linear regression, logistic regression, multiple regression, polynomial regression, and so on.

Results.

The following are the data set that have been determined already:

Body Mass Index readings (BMI0)

Systolic Blood Pressure

Total Cholesterol

Table 1.

Descriptive statistics for the analyzed variables,

Table 2.

Assumptions for the analyzed variables

Assumptions

First, it was assumed that over the period of the analysis, nobody lost their jobs, hence household income as a variable was never drastically affected.

Also, it was assumed that no natural disaster such as hurricanes or pandemic such as covid-19 occurred over the period of analysis, so again, no serious impact household income.

Another assumption made was whether or not the test subjects were under medication. There are some drugs that may have an impact on blood pressure, hence influence the readings and eventual figures.

Another significant assumption made during this test, was the different lifestyles different people live. Some work out, other don’t. Among those who work out, some prefer jogging, others prefer doing weights. So had their different lifestyles been factored, it would have a huge impact on the end result.

Also, the other assumption made was that the equipment used to measure some of the value for this study, such as body mass index, were all giving correct readings and none contained any degree of error.

Linear Regression

Linear is a variation of regression, which uses a number of slightly erroneous points graphically represented, to help plot the line of best fit. Formula wise, it is represented by:

Table 3

Title of Table Simple Linear Regression Example

Note: R²=0.002

Multiple Linear Regression

Conclusions

Based on the findings, Body Mass Index is the dependent variable that is affected by the other independent variables such as household income. Moreover, from the statistics of the studies conducted, it is evident that the cash amount spent on carry out and BMI was significantly positively related. As BMI increased, the amount of reported money spent on carryout increased as well. This relationship is conceivable because takeout meals are often calorically-dense meals.

References

Field, A. P. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). London, United Kingdom: SAGE Publications.