Lab Assignment Using R Programming Basics ( Harrisburg Uni Business Analytics Class)

--- title: "Introduction to Data Analytics 2" author: "Enter Your Name" date: "`r Sys.Date()`" output: word_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` # Part 1: Experiment Design *Title*: Consumer Pseudo-Showrooming and Omni-Channel Product Placement Strategies *Abstract*: Recent advances in information technologies (IT) have powered the merger of online and offline retail channels into one single platform. Modern consumers frequently switch between online and offline channels when they navigate through various stages of the decision journey, motivating multichannel sellers to develop omni-channel strategies that optimize their overall profit. This study examines consumers' cross-channel search behavior of "pseudo-showrooming," or the consumer behavior of inspecting one product at a seller's physical store before buying a related but different product at the same seller’s online store, and investigates how such consumer behavior allows a multichannel seller to achieve better coordination between its online and offline arms through optimal product placement strategies. Participants in the study were grouped into the following categories: - Where_bought: Where they ended up purchasing an item: bought at the store, bought online. - Who_bought: If they bought from the same or a different retailer. Each participant was then measured on: - Money: how much money they spent in dollars on the product. - Time: how much time (in minutes) they spent looking at the product online. 1) What would be one possible null hypothesis based on this study? 2) What would be one possible alternative hypothesis based on this study? 3) Who are they sampling in this study? 4) Who is the intended population in this study? 5) Give an example of type 1 error based on this study (do not just define, explain in context how it might have happened here). 6) Give an example of type 2 error based on this study (do not just define, explain in context how it might have happened here). # Part 2: Use the 04_data.csv to complete this portion. ```{r import-data} ``` 1) For each IV list the levels (next to a, b): a. Where bought: b. Who bought: 2) What are the conditions in this experiment? 3) For each condition list the means, standard deviations, and standard error for the conditions for time and money spent. Please note that means you should have several sets of M, SD, and SE. Be sure you name the sets of means, sd, and se different things so you can use them later. ```{r descriptives} ``` 4) Which condition appears to have the best model fit using the mean as the model (i.e. smallest error) for time? 5) What are the df for each condition? ```{r df} ``` 6) What is the confidence interval (95%) for the means? ```{r conf-interval} ##money ##time ``` 7) Use the MOTE library to calculate the effect size for the difference between money spent for the following comparisons (that means you'll have to do this twice): ```{r MOTE} ##Store versus online when bought at the same retailer ##Store versus online when bought at a different retailer ``` 8) What can you determine about the effect size in the experiment - is it small, medium or large? 9) How many people did we need in the study for each comparison? ```{r pwr} ##Store versus online when bought at the same retailer ##Store versus online when bought at a different retailer ```