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Assumption #1: Your dependent variable should be measured at the continuous level.

However, you should decide whether your study meets these assumptions before moving on. Since assumptions #1 and #2 relate to your choice of variables, they cannot be tested for using Stata.

If any of these seven assumptions are not met, you cannot analyse your data using linear because you will not get a valid result. There are seven 'assumptions' that underpin linear regression. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for linear regression to give you a valid result.

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In this guide, we show you how to carry out linear regression using Stata, as well as interpret and report the results from this test. We will refer to these as dependent and independent variables throughout this guide. Ultimately, whichever term you use, it is best to be consistent. Note: The dependent variable is also referred to as the outcome, target or criterion variable, whilst the independent variable is also referred to as the predictor, explanatory or regressor variable. Alternatively, if you just wish to establish whether a linear relationship exists, you could use Pearson's correlation. If you have two or more independent variables, rather than just one, you need to use multiple regression. Alternately, you could use linear regression to understand whether cigarette consumption can be predicted based on smoking duration (i.e., your dependent variable would be 'cigarette consumption', measured in terms of the number of cigarettes consumed daily, and your independent variable would be 'smoking duration', measured in days). For example, you could use linear regression to understand whether exam performance can be predicted based on revision time (i.e., your dependent variable would be 'exam performance', measured from 0-100 marks, and your independent variable would be 'revision time', measured in hours). Linear regression, also known as simple linear regression or bivariate linear regression, is used when we want to predict the value of a dependent variable based on the value of an independent variable. Counts after are not included.Linear regression analysis using Stata Introduction ** As of September 21, 2021, B.1.1.7, B.1.351, and P.1 are no longer variants of concern. B.1.427 or B.1.429 counts after are not included. This is a random sample of specimens submitted each week through the Colorado SARS-CoV-2 Variant Sentinel Surveillance Program and provides estimates of total variant of concern prevalence. Not all samples in the state of Colorado are able to be whole genome sequenced. Counts of fewer than 10 are suppressed by the CDC in these published data*Suppressed values **Deaths among cases based on county of residence.***Deaths due to COVID-19 based on county where death occurred. The variant cases identified above are based on a small sampling of positive COVID-19 tests and do not represent the total number of variant cases that may be circulating in Colorado.

Counts by county available in the CDPHE Open Data Portal. Variants being monitored - includes variants with substitutions of concern, previously designated Variants of Interest (VOIs) or Variants of Concern (VOCs), that are no longer detected or are circulating at very low levels in the United States, and as such, do not pose a significant or imminent risk to public health in the United States. As of September 21,2021, B.1.1.7, B.1.351, and P.1 are no longer variants of concern and will not be included in variants of concern after. As of June 28, 2021, B.1.427 and B.1.429 are no longer variants of concern and will not be included in Variants of concern after. *Variants of concern - a variant that may spread easier, cause more severe disease, reduce the effectiveness of treatments or vaccine, or is harder to detect using current tests.