1.Write the logistic regression equation to model the odds of distress as a function of temperature. Call: glm(formula = as.factor(distress) ~ temp, family = "binomial", data = ONE) Deviance Residuals: Min 1Q Median 3Q Max -1.0611 -0.7613 -0.3783 0.4524 2.2175 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 15.0429 7.3786 2.039 0.0415 * temp -0.2322 0.1082 -2.145 0.0320 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 28.267 on 22 degrees of freedom Residual deviance: 20.315 on 21 degrees of freedom AIC: 24.315 Number of Fisher Scoring iterations: 5 Logit = log (odds) = log{p/(1-p)}= α+β1X1+ β2X2 +…+βiXi
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.
1.Write the logistic regression equation to model the odds of distress as a
Call:
glm(formula = as.factor(distress) ~ temp, family = "binomial",
data = ONE)
Deviance Residuals:
Min 1Q
-1.0611 -0.7613 -0.3783 0.4524 2.2175
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 15.0429 7.3786 2.039 0.0415 *
temp -0.2322 0.1082 -2.145 0.0320 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 28.267 on 22 degrees of freedom
Residual deviance: 20.315 on 21 degrees of freedom
AIC: 24.315
Number of Fisher Scoring iterations: 5
Logit = log (odds) = log{p/(1-p)}= α+β1X1+ β2X2 +…+βiXi
Trending now
This is a popular solution!
Step by step
Solved in 3 steps
