I am having a difficult time synthesizing the data. The data in the table was completed on excel with simple linear regression and multivariable regression analyses. The values in the table are comparing these characteristics to the outcome variable of Glucose. Based on the values on the table, what is it saying about the association between serum Glucose and the characteristics? Are the P values demonstrating that there is significant differences in those characteristics with p-values < 0.05 ? I understand that crude models look for associations between a risk factor and an outcome while multivariable regression models look at the interrelatedness of several risk factors to an outcome. Does this mean there are characteristics associated with risks but not as much significance when looking at the interrelatedness of all the variables? The origional question is as follows: What characteristics are associated with serum Glucose? Use simple and multivariable linear regression analysis to complete the following table relating the characteristics listed to Glucose as a continuous variable. Describe how each characteristic is related to Glucose level. Are crude and multivariable effects similar? What might explain or account for any differences? Outcome Variable: Glucose mg/dL Characteristic Regression Coefficient Crude Models p-value Regression Coefficient Multivariable Model p-value Age, years 0.350 < 0.001 0.093 0.018 Male sex 0.289 0.709 0.211 0.735 Systolic blood pressure, mmHg 0.146 < 0.001 0.052 < 0.001 Total serum cholesterol, mg/dL 0.028 0.001 0.004 0.615 Current smoker -2.870 < 0.001 -0.687 0.278 Diabetes 89.799 0 88.273 0
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.
I am having a difficult time synthesizing the data. The data in the table was completed on excel with simple linear regression and multivariable regression analyses. The values in the table are comparing these characteristics to the outcome variable of Glucose. Based on the values on the table, what is it saying about the association between serum Glucose and the characteristics? Are the P values demonstrating that there is significant differences in those characteristics with p-values < 0.05 ? I understand that crude models look for associations between a risk factor and an outcome while multivariable regression models look at the interrelatedness of several risk factors to an outcome. Does this mean there are characteristics associated with risks but not as much significance when looking at the interrelatedness of all the variables? The origional question is as follows:
What characteristics are associated with serum Glucose?
Use simple and multivariable linear
Outcome Variable: Glucose mg/dL
|
Characteristic |
Regression Coefficient Crude Models |
p-value |
Regression Coefficient Multivariable Model |
p-value |
|
Age, years |
0.350 |
< 0.001 |
0.093 |
0.018 |
|
Male sex |
0.289 |
0.709 |
0.211 |
0.735 |
|
Systolic blood pressure, mmHg |
0.146 |
< 0.001 |
0.052 |
< 0.001 |
|
Total serum cholesterol, mg/dL |
0.028 |
0.001 |
0.004 |
0.615 |
|
Current smoker |
-2.870 |
< 0.001 |
-0.687 |
0.278 |
|
Diabetes |
89.799 |
0 |
88.273 |
0 |
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