Final answer:
To assess if the advertising expenses follow a normal distribution, a chi-square goodness-of-fit test is employed using the .01 significance level decision rule. The computed chi-square value is compared to the critical chi-square value to decide on normality. Without expected frequencies, the exact chi-square value cannot be calculated here.
Step-by-step explanation:
To determine if the sample data of advertising expenses are from a population that follows a normal probability distribution, we can use the chi-square (χ²) goodness-of-fit test. This test will help us assess if the observed frequency distribution of expenses fits a normal distribution.
Decision Rule
Under the .01 significance level, the decision rule for the chi-square goodness-of-fit test is to reject the null hypothesis (H0: the data follow a specified distribution) if the calculated chi-square statistic is greater than the critical value from the chi-square distribution table.
Computing Chi-square Value
To compute the chi-square statistic, we would normally calculate the expected frequencies for each interval, then use the formula:
χ² = Σ[(Observed - Expected)2 / Expected]
where 'Observed' is the observed frequency and 'Expected' is the expected frequency under the normal distribution assumption. However, because we haven't been provided with the expected frequencies, we can't compute the exact chi-square value in this response.
Decision Regarding Normality
The decision on whether to reject or not reject the null hypothesis is based on comparing the calculated chi-square value with the critical value. If the calculated value is greater than the critical value at the .01 significance level, we would reject the null hypothesis and conclude that the sample data do not come from a normal distribution.