Final answer:
The correlation coefficient (r) measures the linear relationship between two variables and is compared to critical values to determine significance. To conclude the correlation between advertisement cost and sales, the r-value must be calculated, then checked against the critical values table for significance at the chosen level. However, a typo or missing context in the question makes it difficult to provide the exact answer from the options given.
Step-by-step explanation:
To compute the correlation coefficient (r) between advertisement cost and sales, we use the Pearson correlation formula, which considers both the Advertisement Cost in 1000 and the Sales in CHF variables from the provided dataset. The correlation coefficient, r, is a measure of the strength and direction of the linear relationship between two variables. The calculation of r requires the means and standard deviations of both variables as well as the number of pairs of data.
Once computed, we compare r with the critical value from the 95 Percent Critical Values of the Sample Correlation Coefficient table corresponding to the degrees of freedom, which is n-2 for our dataset size, n. The critical value at the significance level determines if the correlation is significant. For example, if r exceeds the critical value, the correlation is significant, suggesting a relationship that can be used for prediction.
For this question, we do not have the actual data computations, but we can look at the options provided. Options (a) and (b) provide an r-value of 0.7804. However, neither option (c) nor option (d) match exactly with the r-value provided in the question. The r-value we are given is out of context, as we don't have n=14 in the example, so we must focus on the data points we do have, which is n=10. There may be a typo or missing context in the question as none of the provided options match the provided r-value.