Researchers are trying to assess the effectiveness of a new blood pressure medication. Using their data, they calculate a simple linear regression model that predicts systolic blood pressure (SBP) in terms of BP Meds (where 0 means the new medication is given and 1 means a placebo is give). The results are shown in the last row of the middle 2 columns of the table below. The researchers believe that Age, Gender, and BMI might be confounders. They calculate simple linear models for each of these variables as shown in the table where SBP is the response variable in each model. Then they calculate a multiple regression model that predicts SBP in terms of all 4 variables. The results are given in the last 2 columns on the table. Based on these results, is the association between BP meds and SBP confounded by Age, Gender or BMI? Provide a brief (1-2 sentences) explanation. Simple Models Multiple Regression b p b p Age 1.03 <.0001 0.86 <.0001 Male -2.26 0.0009 -2.22 0.0002 BMI 1.8 <.0001 1.48 <.0001 BP Meds 33.38 <.0001 24.12 <.0001
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Researchers are trying to assess the effectiveness of a new blood pressure medication. Using their data, they calculate a simple linear regression model that predicts systolic blood pressure (SBP) in terms of BP Meds (where 0 means the new medication is given and 1 means a placebo is give). The results are shown in the last row of the middle 2 columns of the table below. The researchers believe that Age, Gender, and BMI might be confounders. They calculate simple linear models for each of these variables as shown in the table where SBP is the response variable in each model. Then they calculate a multiple regression model that predicts SBP in terms of all 4 variables. The results are given in the last 2 columns on the table. Based on these results, is the association between BP meds and SBP confounded by Age, Gender or BMI? Provide a brief (1-2 sentences) explanation.
| Simple Models | Multiple Regression | ||||||||
| b | p | b | p | ||||||
| Age | 1.03 | <.0001 | 0.86 | <.0001 | |||||
| Male | -2.26 | 0.0009 | -2.22 | 0.0002 | |||||
| BMI | 1.8 | <.0001 | 1.48 | <.0001 | |||||
| BP Meds | 33.38 | <.0001 | 24.12 | <.0001 |
Trending now
This is a popular solution!
Step by step
Solved in 2 steps
