You fit a simple logistic regression model of the log odds of having had a heart attack (variable name "attack") as pred

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You fit a simple logistic regression model of the log odds of having had a heart attack (variable name "attack") as pred

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You Fit A Simple Logistic Regression Model Of The Log Odds Of Having Had A Heart Attack Variable Name Attack As Pred 1
You Fit A Simple Logistic Regression Model Of The Log Odds Of Having Had A Heart Attack Variable Name Attack As Pred 1 (41.44 KiB) Viewed 23 times
You Fit A Simple Logistic Regression Model Of The Log Odds Of Having Had A Heart Attack Variable Name Attack As Pred 2
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You fit a simple logistic regression model of the log odds of having had a heart attack (variable name "attack") as predicted by cholesterol levels (variable name "chol"). Having had a heart attack was caded as 1 and never having a heart attack was coded as 0. The results from this model are shown below. How does an increase in cholesterol levels impact the odds of having had a heart attack? (1- 3 sentences is sufficient.) Call: glm(formula attack chol, family= "binomial", data = dataset) Deviance Residuals: Min 1Q Median 3Q Max -0.9726 -0.7525 -0.7121 -0.6550 1.8476 Coefficients: Estimate Std. Error z value Pr(>[z]) (Intercept) -1.960288 0.223854 -8.757 chol 0.11040 0.002966 3.722 Null deviance: 3042.2 on 2757 degrees of freedom Residual deviance: 3028.5 on 2756 degrees of freedom AIC: 3032.5 <2e-16*** 0.000198 ***
You fit a simple logistic regression model of the log odds of having had a heart attack (variable name "attack") as predicted by cholesterol levels (variable name "chol"). Having had a heart attack was coded as 1 and never having a heart attack was coded as 0. The results from this model are shown below. How does an increase in cholesterol levels impact the odds of having had a heart attack? (1-3 sentences is sufficient.) Call: glm(formula = attack chol, family = "binomial", data = dataset) Deviance Residuals: Min 1Q Median 3Q Max -0.9726 -0.7525 -0.7121 -0.6550 1.8476 Coefficients: Estimate Std. Error z value Pr(>lzl) (Intercept) -1.960288 0.223854 -8.757 <2e-16 *** chol 0.11040 0.002966 3.722 0.000198 Null deviance: 3042.2 on 2757 degrees of freedom Residual deviance: 3028.5 on 2756 degrees of freedom AIC: 3032.5 ***
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