STA 326 2.0 Programming and Data Analysis with R

1. Data

Download the data file from the course website here (under week 12).

Use readRDS function to read the file.

facebookdata_marketing <- readRDS("_GIVE_FILE PATH_/facebookdata_marketing.rds")

Your turn (optional)

Explore here function in the here package.

Read here

2. Variable description

A manager of a retail company wants to develop a regression model to identify the effect of the following variables (see below) on the total number of likes, comments, and shares on facebook posts.

• month: Month the post was published (1, 2, 3, …, 12)

• category: Type of the post (1 - Link, 2 - Video, 3 - Picture)

• hour: Hour the post was published (0, 1, …24)

• paid: If the company paid to Facebook for advertising (0 - No, 1 - Yes)

• totalReach: Number of people who saw the page post (unique users).

• engagedUsers: Number of people who clicked anywhere in the post (unique users).

• postConsumers: Number of people who sent a direct message to the owner of the post.

• postConsumption: Number of clicks anywhere in the post.

• sawbyLiked: Number of people who saw the page post because they have liked that page.

• clickbyLiked: Number of people who have liked the Page and clicked anywhere in the post.

Dependent variable:

• totalInteractions: The sum of “likes,” “comments,” and “shares” of the post.

3. Training test and Test set

smp_size <- 400

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(facebookdata_marketing)), size = smp_size)
train <- facebookdata_marketing[train_ind, ]
test <- facebookdata_marketing[-train_ind, ]

4. Questions

1. Perform a thorough Exploratory Data Analysis on facebookdata_marketing.rds.

2. Develop a suitable regression model to predict total interactions (The sum of “likes,” “comments,” and “shares” of the post).

3. Test for significance of regression. What conclusions can you draw?

4. Using t tests, determine the contribution of the regressors in your final model. Discuss your findings.

5. Plot 95% confidence interval for the regression coefficients of the model in part 2.

6. Is multicollinearity a potential concern in the model identified in part 2.

7. Use the model in part 2 to predict each observation in the test test and calculate the out-of-sample accuracy.

8. Prepare a brief report presenting your EDA and regression analysis.