Data Analytics True OR False with Full Justification Q1. You are given a task to analyze the salaries of employees in your company and find a representative value for them. Taking into account the information that the CEO and partners have substantially higher salaries than other employees, you should take the average salary as the representative middle point.

Q2. The Dubai General Hospital generally expects 60 patients per day who require urgent care, on average. However, in the past 80 days, the data shows that there are on average 57 urgent cases per day, with a standard deviation of 5. The number of urgent cases has significantly deviated from the expectation of the hospital, at the 5% significance level.

Q3. Your retail firm is considering buying data from a company to improve its current demand forecasts, which are particularly inaccurate for large demand values. One new feature is highly correlated with independent variables in your current model for large demand values, yet not correlated for small demand values. Ideally, you should not use this new independent variable since it is highly correlated with the independent variable you already have (and thus, brings little new information).

Q4. You are considering a potential investment opportunity in a hedge fund. You are given access to the historical monthly returns since the inception of the fund. To be on the safe end, you should remove all the outliers.

Q5. You fit a simple linear regression model to explain the consumption of milk (target variable) using the consumption of coffee (explanatory variable). The R-squared value of your linear regression is less than 20%. This means that there is no statistically significant relationship between milk consumption and coffee consumption at the 5% significance level.

Q6. You have access to sales data of 30000 stores of a Fortune Global 500 company. You are interested in finding if there exists a significant change in overall sales in the year 2019 in comparison to the year 2018. Because you want to keep the analysis relatively simple, you pick 1000 stores at random and conduct a two-sample hypothesis test comparing overall sales in 2019 with overall sales in 2018 for these stores. To make sure the result is reliable, you should repeat this process an arbitrarily large number of times, and take the smallest p-value as your final p-value.