To calculate the degrees of freedom associated with the sum of squares for treatments (SST), we first need to understand the structure of the problem.

General Setup:

1. Number of Strategies (k): This represents the number of groups or treatments. In this case, there are 4 strategies.

2. Number of Observations per Strategy (n_i): This is the number of observations in each strategy. Here, we have sales data for each of the strategies. Counting the sales data for each strategy:

   • Strategy 1: 10 values
   • Strategy 2: 10 values
   • Strategy 3: 10 values
   • Strategy 4: 10 values

So, $n_i = 10$ for each strategy.

3. Total Number of Observations (N): This is the total number of data points across all strategies. Since we have 4 strategies, each with 10 data points, the total number of observations is:

   $N = 4 \times 10 = 40$

Degrees of Freedom (df) for SST:

The degrees of freedom for the sum of squares for treatments (SST) is given by:

$df_{\text{treatments}} = k - 1$

Where $k$ is the number of strategies (treatments). For this case, $k = 4$, so:

$df_{\text{treatments}} = 4 - 1 = 3$

Conclusion:

The degrees of freedom associated with the sum of squares for treatments is 3.

Would you like more details or have any questions? Here are 5 related questions you may consider:

1. How is the sum of squares for treatments (SST) calculated in ANOVA?
2. What are the degrees of freedom associated with the sum of squares for error (SSE)?
3. How do we interpret the F-statistic in the context of ANOVA?
4. Can you explain the difference between within-group and between-group variance in ANOVA?
5. How does the total sum of squares (SSTotal) relate to SSE and SST?

Tip: Degrees of freedom for treatments (SST) depend only on the number of groups, while degrees of freedom for error (SSE) depend on the number of total observations minus the number of groups.