https://file.aiquickdraw.com/mathbot/file/1729867970752dxwbb2qk.csv

dari data diatas tentukan 2 variabel independen untuk membuat regresi linier berganda 
Berdasarkan data tersebut, buatlah dan tunjukkan hasil akhirnya dari: 
1. Model regresi
2. Uji asumsi
3. Uji serempak
4. Uji parsial
5. Koefisien determinasi
menggunakan r dan minitab 
apakah dataset tersebut sudah memenuhi semua uji asumsi? sehingga dataset tersebut valid?

I'll load the data and analyze it to select two independent variables that may work well for a multiple linear regression. Then I'll outline steps to perform a full regression analysis in R and Minitab, including assumption testing, model fit, and interpretations. 

Let’s start by examining the structure of the dataset and looking at potential variables.The dataset includes multiple variables. Key numeric variables we could use for a multiple linear regression model include:

1. **Life Expectancy** (dependent variable)
2. **Adult Mortality**
3. **GDP**
4. **Schooling**
5. **Alcohol Consumption**

We'll proceed with **Adult Mortality** and **Schooling** as independent variables, given their likely impact on life expectancy based on health and education levels.

Here's a general outline to perform the analysis in R and Minitab.

### Step 1: Model Regression
   - Use `lm()` in R to create a multiple linear regression model:  
     ```R
     model <- lm(`Life expectancy ` ~ `Adult Mortality` + Schooling, data = dataset)
     summary(model)
     ```
   - In Minitab, import the dataset, choose **Stat > Regression > Regression > Fit Regression Model**, set *Life expectancy* as the response, and add *Adult Mortality* and *Schooling* as predictors.

### Step 2: Assumption Testing
- **Normality of Residuals**: Use a Q-Q plot or Shapiro-Wilk test (`shapiro.test(residuals(model))` in R).
- **Linearity**: Check scatter plots of predictors vs. residuals.
- **Homoscedasticity**: In R, use `plot(model, which = 3)`.
- **Multicollinearity**: Calculate Variance Inflation Factor (VIF) with `car::vif(model)`.

### Step 3: F-test (Overall Significance Test)
- R’s model summary includes the F-statistic to test overall significance.
  
### Step 4: Partial T-tests (Significance of Each Variable)
- In R, the `summary(model)` command shows individual t-tests for each predictor. In Minitab, these are listed as p-values in the model output.

### Step 5: Coefficient of Determination (R²)
- Found in the model summary under `Multiple R-squared` in R, indicating the proportion of life expectancy variance explained by the model.

Let me know if you need further guidance or have specific questions on any steps. 

#### Follow-up Questions
1. How are the regression assumptions verified in practical datasets?
2. What specific conditions make multicollinearity problematic?
3. How can we interpret the coefficients in a regression output?
4. What do we conclude if the overall F-test is not significant?
5. How do we handle missing data before running a regression?

**Tip:** Always check multicollinearity (VIF) to ensure predictors do not overly influence each other, ensuring the stability of your regression model.

Dari data diatas tentukan 2 variabel independen untuk membuat regresi linier berganda. Berdasarkan data tersebut, buatlah dan tunjukkan hasil akhirnya dari: 1. Model regresi 2. Uji asumsi 3. Uji serempak 4. Uji parsial 5. Koefisien determinasi menggunakan R dan Minitab apakah dataset tersebut sudah memenuhi semua uji asumsi? sehingga dataset tersebut valid?

Learn how to perform a multiple linear regression analysis using R and Minitab. Steps cover regression model creation, assumptions testing (normality, linearity, homoscedasticity, multicollinearity), overall and partial significance tests, and the calculation of R².

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