---
title: "Results"
output: word_document
---

```{r echo=FALSE}
week4<-read.csv("week4_data.csv", sep=";", header = TRUE)
attach(week4)
```


In the database week4_data, the mean of patients age and the median of the number of pregnancies are respectively:

```{r echo=FALSE}
mean(age)
median(preg)
```

The summary of all variables in the database week4_data is presented below:

```{r echo=FALSE}
summary(week4)

```


The distribution of frequencies for the variables age and survival is presented in Figure 1.

```{r echo=FALSE}
hist(age,
     xlab = "age (years)",
     main = "Fig.1A - Age distribution")

hist(survival,
     xlab = "months",
     main = "Fig.1B - Survival distribution")



```

Task 1. Write down your interpretation of Figure 1.


In Figure 2 is presented the relationship between the number of pregnancies and the number of deliveries.

```{r echo=FALSE}
#Task 2. Please, write here the codes to draw Figure 2.



```

Task 3. Insert here the code to test the significancy of this correlation.

```{r echo=FALSE}



```


Task 4. Write down your interpretation of Figure 1 and from the correlation test.


The distribution of frequencies of the variables ca125b and ca125a is shown in Figure 3.

Task 5. Insert here the codes to draw Figure 3.



CA 125 values are significantly lower after the treatment.

Task 6. Insert here the codes to test this hypotesis.
```{r echo=FALSE}


```


In Table 1 is represented the distribution of surgeries according to tumor type.

Task 7. Insert here the codes to display Table 1.


No gross residual disease (ngr) was significantly more frequent in tumors type I.

Task 8. Insert here the codes to test this hypotesis.


In Figure 4 we can oberve the distribution of patient's age according to tumor type.

Task 9. Insert here the codes to draw Figure 4.

There is no difference of age between tumor types.

Task 10. Insert here the codes to test this hypotesis.