Problem Statement You are a data analyst for a retail company. The sales department of your company is concerned about new competitors in the market. The primary focus is on evaluating the performance of sales representatives and devising strategies to enhance their effectiveness. Additionally, they are interested in understanding and forecasting the future trends of revenue. A strategic planning meeting is scheduled soon. Your job is to explore the data and provide meaningful insights and recommendations based on your company's current data. The database contains information on sales representatives during the second quarter, daily customer interactions spanning the first half of the year 2023, and revenue data spanning the past four years. To address the sales team's concerns comprehensively, the following analyses are required. 1. The sales team is trying to figure out the influential factor affecting sales representative performance. Please clearly articulate the following analyses. a. Use statistical summary to interpret the sales representative dataset. Identify at least two (2) interesting and distinctly different relationships amongst three or more variables of the data set and discuss each briefly. You might offer hypotheses for observed correlations.. (Perform necessary data preprocessing, such as data transformation or aggregation, and consider employing conditional formatting for the insights.) b. Create an appropriate visualization to illustrate the relationships you discovered. Use at least three (3) variables for each relationship. Do not use the same three variables for both of your visualizations. Filter, sort and/or rank data if needed and explain what you filter if you do. You might even show the settings in your screenshot. Be sure to identify the aggregation of your measures for each chart if you have applied, perhaps in the title. (Hint: You may need to transfer some categorical data to numeric data to conduct data visualization) c. Explain why each of these visualizations is important to your team. 2. The sales team wants to explore effective strategies to increase the sales and motivate sales representatives. Build a regression model and explain your regression output. Your analysis report should answer the following key questions. a. Whether sales representatives should utilize the MS Teams? b. Whether sales representatives should work remotely? c. How to control seasonality when evaluating sales? d. Are the bonuses reasonable? Why? e. Predict the sales data of the given set of assumptions based on your regression model. (Hint: set values for independent variables to simulate different scenarios and forecast potential outcomes) 3. The sales team is also interested in revenue and customer trends or patterns in the future. Create a time-series forecast of revenue and customer amount for the certain future period. Follow these steps to conduct time series forecasting. a. Plot the current revenue and customer time series data. Demonstrate the level, trend, seasonality, and noise in the charts, and explain the possible reason. b. Explain what forecasting method and value of parameter you chose and why you chose what you did. Plot the forecasting as well. c. Is the forecast reliable? Why or why not? (Hint: use the accuracy measure of forecasting) 4. Formulate a final and comprehensive conclusion based on the findings, insights and recommendations derived from the analyses conducted above. \begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline & A & B & C & D & E & F & G & H \\ \hline 1 & Rep & Month & Daily Sales & Remote & Calls Per Day & Quit? & MS Teams User? & Bonus Amt \\ \hline 2 & Joe & April & $16,334 & N & 82 & 0 & Y & $1,039.68 \\ \hline 3 & Joe & May & $14,639 & N & 73 & 0 & Y & $2,971.82 \\ \hline 4 & Joe & June & $23,642 & Y & 59 & 0 & Y & $1,240.08 \\ \hline 5 & Sam & April & $16,333 & N & 82 & 0 & Y & $644.29 \\ \hline 6 & Sam & May & $24,763 & N & 124 & 0 & Y & $852.04 \\ \hline 7 & Sam & June & $32,831 & N & 164 & 1 & Y & $283.99 \\ \hline 8 & Beth & April & $27,588 & N & 138 & 0 & Y & $536.21 \\ \hline 9 & Beth & May & $16,506 & Y & 41 & 0 & Y & $358.81 \\ \hline 10 & Beth & June & $25,022 & Y & 63 & 0 & Y & $511.18 \\ \hline 11 & Lee & April & $25,002 & Y & 63 & 0 & Y & $1,514.83 \\ \hline 12 & Lee & May & $16,694 & N & 83 & 0 & Y & $2,209.36 \\ \hline 13 & Lee & June & $11,701 & N & 59 & 0 & Y & $22.31 \\ \hline 14 & Ida & April & $3,651 & Y & 9 & 0 & N & $340.16 \\ \hline 15 & Ida & May & $9,528 & Y & 24 & 0 & N & $194.60 \\ \hline 16 & Ida & June & $20,037 & Y & 50 & 1 & N & $172.85 \\ \hline 17 & Juan & April & $13,606 & N & 68 & 0 & N & $2,053.03 \\ \hline 18 & Juan & May & $18,874 & Y & 47 & 0 & N & $98.29 \\ \hline 19 & Juan & June & $13,732 & N & 69 & 0 & N & $179.25 \\ \hline 20 & Ashley & April & $19,634 & Y & 49 & 0 & N & $1,006.08 \\ \hline 21 & Ashley & May & $13,034 & N & 65 & 0 & N & $279.03 \\ \hline 22 & Ashley & June & $663 & Y & 2 & 0 & N & $0.86 \\ \hline 23 & James & April & $18,397 & N & 92 & 0 & N & $276.85 \\ \hline 24 & James & May & $22,053 & N & 110 & 0 & N & $105.64 \\ \hline 25 & James & June & $23,382 & N & 117 & 0 & Y & $1,138.26 \\ \hline \end{tabular} \begin{tabular}{|c|c|} \hline Date & Customers \\ \hline 1/1/23 & 29 \\ \hline 1/2/23 & 31 \\ \hline 1/3/23 & 23 \\ \hline 1/4/23 & 19 \\ \hline 1/5/23 & 24 \\ \hline 1/6/23 & 28 \\ \hline 1/7/23 & 31 \\ \hline 1/8/23 & 30 \\ \hline 1/9/23 & 26 \\ \hline 1/10/23 & 23 \\ \hline 1/11/23 & 21 \\ \hline 1/12/23 & 23 \\ \hline 1/13/23 & 28 \\ \hline 1/14/23 & 31 \\ \hline 1/15/23 & 32 \\ \hline 1/16/23 & 26 \\ \hline 1/17/23 & 22 \\ \hline 1/18/23 & 20 \\ \hline 1/19/23 & 21 \\ \hline 1/20/23 & 29 \\ \hline 1/21/23 & 32 \\ \hline 1/22/23 & 30 \\ \hline 1/23/23 & 28 \\ \hline 1/24/23 & 21 \\ \hline 1/25/23 & 21 \\ \hline 1/26/23 & 24 \\ \hline 1/27/23 & 30 \\ \hline 1/28/23 & 33 \\ \hline 1/29/23 & 31 \\ \hline 1/30/23 & 27 \\ \hline 1/31/23 & 21 \\ \hline 2/1/23 & 19 \\ \hline 2/2/23 & 24 \\ \hline 2/3/23 & 29 \\ \hline 2/4/23 & 33 \\ \hline \end{tabular} Representitives Revenue Daily Customers