Answer:
Explanation:
Data given and notation
represent the sample mean
represent the standard deviation for the sample
sample size
represent the value that we want to test
represent the significance level for the hypothesis test.
t would represent the statistic (variable of interest)
represent the p value for the test (variable of interest)
State the null and alternative hypotheses.
We need to conduct a hypothesis in order to determine if the mean is lower than 5600, the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
We don't know the population deviation, so for this case is better apply a t test to compare the actual mean to the reference value, and the statistic is given by:
(1)
t-test: "Is used to compare group means. Is one of the most common tests and is used to determine if the mean is (higher, less or not equal) to an specified value".
Calculate the statistic
We can replace in formula (1) the info given like this:
The answer is 40%, in which the following are given: the Variable expense is equal to 20 dollars per unit and Sales is equal to 50 dollars per unit. Use the formula Variable Expense Ratio = Variable Expenses / Sales to get the answer.
Variable Expense Ratio = Variable Expenses / Sales
Variable Expense Ratio = 20 dollars per unit / 50 dollars per unit
Variable Expense Ratio = 40 %
The variable expense ratio is an expression of variable production costs of the company as a percentage of sales, calculated as variable expense divided by total sales. It compares a cost that alters with levels of production to the number of revenues generated by production.
Answer:
The ethical dilemma that Marco Manager is facing having to choose between trying to keep an existing friendship (at least he believes that they are friends) or doing the right thing as a manager, which would involve investigating why the money is missing and most certainly firing the employee.
This is known as in-sample forecast. It estimated the model using all available data and then comparing it to the model's fixed values to the actual realizations. But, this method is known to attract an overly positive picture of the model's forecasting ability since common fitting algorithms tend to take pains to avoid big prediction errors and are also inclined to overfitting (mistaking noise for signal in the data).
