**Introduction**

This is for you if you are looking for interpretation of p-value,coefficient estimates,odds ratio,logit score and how to find the final probability from logit score in logistic regression in R.

Let’s begin !!

*For Best Course on Data Science Developed by Data Scientist ,please follow the below link to avail discount*

*For Best Course on Data Science Developed by Data Scientist ,please follow the below link to avail discount*

**https://www.udemy.com/machine-learning-using-r/?couponCode=DATA_MASTER**

**https://www.udemy.com/machine-learning-using-r/?couponCode=DATA_MASTER**

**Importing libraries,Reading Data & Looking at Data**

Importing the required libraries.MASS is used for importing birthwt dataset

```
library(MASS)
#### Storing the data set named "birthwt" into DataFrame
DataFrame <- birthwt
#### To read about the dataset use following command by uncommenting
#### help("birthwt")
#### Check first 3 rows
head(DataFrame,3)
```

```
## low age lwt race smoke ptl ht ui ftv bwt
## 85 0 19 182 2 0 0 0 1 0 2523
## 86 0 33 155 3 0 0 0 0 3 2551
## 87 0 20 105 1 1 0 0 0 1 2557
```

Machine Learning and Data Science best online courses

**Model fitting & Model Summary**

Now we will fit the logistic regression model using only two continuous variables as independent variables i.e age and lwt.

```
#### Fitting the model
LogisticModel<- glm(low ~ age+lwt, data = DataFrame,family=binomial (link="logit"))
#### Let's check the summary of the model
summary(LogisticModel)
```

```
##
## Call:
## glm(formula = low ~ age + lwt, family = binomial(link = "logit"),
## data = DataFrame)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1352 -0.9088 -0.7480 1.3392 2.0595
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.748773 0.997097 1.754 0.0795 .
## age -0.039788 0.032287 -1.232 0.2178
## lwt -0.012775 0.006211 -2.057 0.0397 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 234.67 on 188 degrees of freedom
## Residual deviance: 227.12 on 186 degrees of freedom
## AIC: 233.12
##
## Number of Fisher Scoring iterations: 4
```

**Basic Maths of Logistic Regression**

We must know odds-ratio and logit score in order to understand logistic regression.

**What is Odds Ratio**

It represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure.

**Formula for Odds ratio**

The mathematical formula for odds ratio is given by:

Odds=probability of success(p)/ probability of failure

=probability of (target variable=1)/probability of (target variable=0)

=p/(1-p)

**Formula for logit **

The logit score can defined as follows:

logit(p) = log(p/(1-p)) = b0 + b1*x1 + … + bk*xk

**Probability Calculation**

Let’s follow the steps as below to find the probability of getting “low=1” (i.e probability of getting success).

**NOTE: Do not confuse p-value with probability.They are different things**

**NOTE: Do not confuse p-value with probability.They are different things**

**Intercept Coefficients interpretation (b0, b1 and b2)**

**1.** Intercept Coefficient(b0)=1.748773

**2.** lwt coefficient(b1) =-0.012775

**Interpretation:** The increase in logit score per unit increase in weight(lwt)

is -0.012775

age coefficient(b2) =-0.039788

**Interpretation:** The increase in logit score per unit increase in age

is -0.039788

**p-value interpretation**

**3.** p-value for lwt variable=0.0397

**Interpretation:** According to z-test,p-value is 0.0397 which is comparatively low

which implies its unlikely that there is “no relation” between lwt and target variable i.e low.Star next to p-value in the summary shows that lwt is significant variable in predicting low variable.

**4.** p-value for age=0.2178

**Interpretation:** According to z-test,p-value is 0.2178 which is comparatively high which implies its unlikely that there is “any relation” between age and target variable i.e low.

**Logit score Calculation**

**5.** Let’s consider a random person with age =25 and lwt=55.Now let’s find the logit score for this person

b0 + b1*x1 + b2*x2= 1.748773-0.039788*25-0.012775*55=0.05144(approx).

**6.** So logit score for this observation=0.05144

**Odds ratio calculation**

**7.** Now let’s find the probability that birthwt <2.5 kg(i.e low=1).See the help page on birthwt data set (type ?birthwt in the console)

**8.** Odds value=exp(0.05144) =1.052786

**Probability Calculation**

**9. **probability(p) = odds value / odds value + 1

** p=1.052786/2.052786=0.513(approx.)**

**p=0.513**

**Interpretation**

0.513 or 51.3% is the probability of birth weight less than 2.5 kg when the mother age =25 and mother’s weight(in pounds)=55

**https://www.udemy.com/machine-learning-using-r/?couponCode=DATA_MASTER**

**https://www.udemy.com/machine-learning-using-r/?couponCode=DATA_MASTER**