03-Week1_home.Rmd

# Week 1 - Home

Open the data file LifeSat.sav. 

```{r, message=FALSE, echo=FALSE}
library(foreign)
data <- read.spss("TCSM_student/LifeSat.sav", to.data.frame = TRUE)
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```
```{r, message=FALSE,eval=FALSE}
library(foreign)
data <- read.spss("LifeSat.sav", to.data.frame = TRUE)
```

### Question 1.a

Make a table with descriptive statistics for the variables: LifSat, educ, ChildSup, SpouSup, and age. 

What is the average age in the sample? And the range (youngest and oldest child)? 

*Hint: Use* `library(tidySEM); descriptives(); []`

<details>
  <summary>Click for explanation</summary>
The package `tidySEM` contains a function to describe data. Install and load the package, then use the `descriptives()` function. Alternatively, you can also use the `describe()` function in the `psych` package.

```{r, echo = TRUE}
library(tidySEM)
descriptives(data[, c("LifSat", "educ", "ChildSup", "SpouSup", "age")])
```
<\details>


### Question 1.b

Perform a simple regression with LifSat as the dependent variable and educ as the independent variable.

*Hint: The function* `lm()` *(short for linear model) conducts linear regression. The functions* `summary()` *provides relevant summary statistics for the model. It can be helpful to store the results of your analysis in an object, too.*

<details>
  <summary>Click for explanation</summary>

```{r, echo = TRUE}
results <- lm(LifSat ~ educ, data)
summary(results)
```
<\details>

### Question 1.c.

Do the same with age as the independent variable. 

<details>
  <summary>Click for explanation</summary>

```{r, echo = TRUE}
results <- lm(LifSat ~ age, data)
summary(results)
```
<\details>


### Question 1.d. 

Again with ChildSup as the independent variable. 

<details>
  <summary>Click for explanation</summary>

```{r, echo = TRUE}
results <- lm(LifSat ~ ChildSup, data)
summary(results)
```
<\details>


### Question 1.e.

Perform a multiple regression with LifSat as the dependent variable and educ, age and ChildSup as the independent variables. 

*Hint: You can use the + sign to add multiple variables to a model.*

<details>
  <summary>Click for explanation</summary>

```{r, echo = TRUE}
results <- lm(LifSat ~ educ + age + ChildSup, data)
summary(results)
```
<\details>


### Question 1.f. 

Compare the results under 1.e with those obtained under 1.b-1.d. What do you notice when you compare the regression parameter for each of the three predictors in the multiple regression with the corresponding regression parameters obtained in the simple regressions?