Above is hypothetical ad data of a company that ran Facebook ads for a week (a f

Above is hypothetical ad data of a company that ran Facebook ads for a week (a f

Above is hypothetical ad data of a company that ran Facebook ads for a week (a fixed amount every day). They obtained the attached data from Facebook after the ad campaign has ended. The data is aggregated across the three variables of Gender, Age Group, and day of the week. For each of the combinations, the company knows how much was the reach (number of people who saw the ad) and the number of clicks on the ad (click), therefore there are five variables in the data and they are described below:
Gender: Male, Female, Unknown
Age: Five age groups, 25-34, 35-44, 45-54, 55-64, and above 65
Weekday: Monday, Tuesday, … , Saturday, Sunday
Reach: Number of people who saw the ad
Click: Number of clicks on the ad
Step 2:We are going to run regression analysis to gain insight on the effectiveness of this campaign across different demographics. In order to run regression analysis, first, we need to identify the outcome variable and the explanatory variables. The outcome variable is the dependent variable, and the explanatory variables are the independent variables. In this scenario, outcome variable is the number of clicks, and the explanatory variables are Gender, Age, Weekday, and Reach. By running regression analysis on the selected dependent and independent variables and based on the provided data, answer the following questions in a Google Doc or Word Document:
Advise the company on which day of the week is more suitable to run ads.
Which age group or gender type was more likely to interact with the ad?
What is the interpretation of the relationship between log_reach and log_click?
Note that you have number of clicks and reach as well as their log transformation in the data. Since click and reach are positive values and may not be have a bell-shape distribution, you may want to use log_clicks as the dependent variable and log_reach along with other demographic variables as explanatory variables.
How do you evaluate the reliability of this regression analysis? You may consider R-square and also check for regression assumptions.