--- title: "Statistical Modeling and Graphing" author: "Enter Your Name" date: "`r Sys.Date()`" output: word_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` *Citation*: Trends in Internet-based business-to-business marketing *Abstract*: The Internet is changing the transactional paradigms under which businesses-to-business marketers operate. Business-to-business marketers that take advantage of the operational efficiencies and effectiveness that emerge from utilizing the Internet in transactions are out performing firms that utilize traditional transactional processes. As an example, Dell computers, by utilizing business-to-business processes that take advantage of the Internet, has gained the largest market share in the PC business when compared to traditional manufacturers such as Compaq. This paper first examines the genesis of the Internet movement in business-to-business markets. The long-term impact of the increase of business-to-business utilization of the Internet on the marketing theory and marketing process is then discussed. Finally, managerial implications and directions for future research are highlighted. Dataset includes: 1) Business marketing focus - traditional or forward thinking. 2) Internet use - low, medium, or high levels of business marketing use on the internet. 3) Time _ 1 - sales scores at the first measurement time. 4) Time _ 2 - sales scores at the second measurement time On all of these questions, be sure to include a coherent label for the X and Y axes. You should change them to be "professional looking" (i.e. Proper Case, explain the variable listed, and could be printed in a journal). The following will be assessed: 1) Is it readable? 2) Is X-axis labeled appropriately? 3) Is Y-axis labeled appropriately? 4) Is it the right graph? 5) Do the labels in the legend look appropriate? 6) Are there error bars when appropriate? We won't grade for color of bars or background color, but you should consider that these things are usually printed in black/white - so be sure you know how to change those values as well as get rid of that grey background. Please note that each subpoint (i.e. a, b) indicates a different chart. ```{r starting} ``` 1) Make a simple histogram using ggplot: a. Sales at time 1 ```{r hist1} ``` b. Sales at time 2 ```{r hist2} ``` 2) Make a bar chart with two independent variables: a. Business focus, internet, DV: sales at time 2 ```{r bar1} ``` 3) Make a bar chart with two independent variables: a. Time (time 1, time 2), Business focus, DV: is sales from time 1 and 2 ```{r bar2} ``` 4) Make a simple line graph: a. Time (time 1, time 2), DV: is sales from time 1 and 2 ```{r line} ``` 5) Make a simple scatterplot: a. Sales at Time 1, Time 2 ```{r scatter1} ``` 6) Make a grouped scatterplot: a. Sales at time 1 and 2, Business focus ```{r scatter2} ```

Use the information provided below to complete the questions. Total points = 100.

Suppose that data below corresponds to the small nation of Cruzville with a population of 189 people. Of the 189 residents 57 are under 16 years of age, and 11 are institutionalized adults. People in the last two categories are not considered to be in the labor force. Of the remaining 121 residents, 16 are without jobs and have given up searching for a job, 6 do not have a job, but are actively seeking work, 20 work part ­time, and the rest work full time. Over the years, the natural rate of unemployment has been found to be 7%. In 2014, the consumer price index (CPI) was 112, and in 2015 the CPI was 130.

Big picture

CALCULATING GDP

1. According to the information provided in the GDP background information handout, a) what are the 4 categories of the expenditures approach? B) how are these categories used to calculate GDP? (8 points)

2. According to table 2 in the GDP background information handout, what are the subcategories of Personal Consumption Expenditures? (8 points)

3. According to table 2 in the GDP background information handout handout, what are the subcategories of Gross Private Domestic Investment? (8 points)

4. Use the data provided in Table 1 (above), to calculate (show your work) the following macro aggregates: a) Personal Consumption Expenditures, b) Gross Private Domestic Investment, c) Net Exports, d) Government Purchases of Goods and Services, e) GDP using the expenditure approach & f) GDP per Capita. (18 points)

Personal Consumption Expenditures = 1200000+850000+750000+550000=3350000

b) Gross Private Domestic Investment = 600000+400000+200000+700000=1900000

c) Net Exports = 45000-50500-28000=33500

d) Government Purchases of Goods and Services=1600000-2280000 = -680000

e) GDP using the expenditure approach = 3350000+1200000-33500 + -680000 =3836500

--- 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 ```