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To help solve the questions based on the table and tasks provided, I'll first outline the steps required to compute the correlation coefficients and partial effects.

Let's work through each problem systematically:

### Table Recap:
- **\(X_1\)**: Biaya Produksi (Production Costs)
- **\(X_2\)**: Biaya Non-Produksi (Non-production Costs)
- **\(Y\)**: Volume Penjualan (Sales Volume)

The table shows monthly data for 2007. The tasks include correlation and regression analysis between these variables.

### Task Breakdown:

1. **Correlation Coefficient**: 
   - Correlation between production costs and sales volume \( (X_1 \text{ and } Y) \).
   - Correlation between production costs and non-production costs \( (X_1 \text{ and } X_2) \).
   - Correlation between non-production costs and sales volume \( (X_2 \text{ and } Y) \).

2. **Partial Regression Analysis**:
   - The partial effect of production costs \( X_1 \) on sales volume \( Y \).
   - The partial effect of non-production costs \( X_2 \) on sales volume \( Y \).

3. **Impact of \( X_1 \) on \( X_2 \)**: 
   Analyze how production costs \( X_1 \) impact non-production costs \( X_2 \).

4. **Simultaneous Impact**: 
   The effect of both \( X_1 \) and \( X_2 \) together on \( Y \) (sales volume).

5. **Hypothesis Testing**:
   Perform hypothesis testing on these effects (likely using t-tests or F-tests).

---

### Approach:

To proceed, I will first calculate the correlation coefficients and then the regression analysis based on the provided data.

I'll start by loading the data and performing the calculations.It appears I missed including December's data point for non-production costs in the table. Let me correct this and rerun the analysis.It looks like there's still an issue with the data length. Upon reviewing the table, I realized that **October's sales volume** data is missing. I will now add this data point, complete the dataset, and proceed with the calculations.It seems like I can’t do more advanced data analysis right now. Please try again later.

Meanwhile, I can guide you on how to solve this manually if you'd like:

1. **Correlation Coefficients**:
   You can calculate these using tools like Excel or Google Sheets by applying the `CORREL` function on the data columns (e.g., Production Costs vs. Sales Volume).

2. **Partial Regression Analysis**:
   You can use linear regression to find the relationship between one independent variable (e.g., production costs) and the dependent variable (sales volume). This can be done in Excel or with statistical software like SPSS or Python.

If you have any questions about how to do these calculations, feel free to ask!

Here are some related questions you can explore:
1. How can you compute the regression coefficient in Excel?
2. What is the significance of a correlation coefficient being close to 1 or -1?
3. How can you perform hypothesis testing with correlation values?
4. What does it mean if two variables have a high correlation but low regression coefficient?
5. How do non-production costs influence production costs based on a regression model?

**Tip**: When working with correlations, ensure the data is clean and there are no outliers, as they can affect the results significantly.

Tentukan: a. Besarnya koefisien korelasi antara biaya produksi dengan volume penjualan b. Besarnya koefisien korelasi antara biaya produksi dengan biaya non produksi c. Besarnya koefisien korelasi antara biaya non produksi dengan volume penjualan d. Besarnya pengaruh biaya produksi secara parsial terhadap volume penjualan e. Besarnya pengaruh biaya non produksi secara parsial terhadap volume penjualan f. Besarnya pengaruh biaya produksi terhadap biaya non produksi g. Besarnya pengaruh biaya produksi dan biaya non produksi secara simultan terhadap volume penjualan h. Ujilah hipotesis tersebut

Learn how to compute the correlation coefficients between production costs, non-production costs, and sales volume based on real data from PT X in 2007. Perform partial and simultaneous regression analysis to understand the impact of production costs on sales volume, and test hypotheses related to these relationships.

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