Question 1: Time Series Regression Consider a dataset representing the monthly sales of a retail store over the past two years. The goal is to build a time series regression model to predict future sales based on various factors. The dataset includes the following variables: - Month: The month of the observation (January, February, ..., December). - Sales: The monthly sales in dollars. - Advertising Expenses: The amount spent on advertising for that month. - Number of Promotions: The count of promotional events held during the month. -Previous Month's Sales: The sales of the previous month. a. Explain the concept of time series regression and why it is suitable for this forecasting problem. b. Formulate a regression model equation that predicts the monthly sales based on the given variables. Clearly define each variabie in the equation. Question 2: Time Series Forecasting You are provided with a time series dataset representing the dally temperature readings in a cly for the past five years. Your task is to develop a forecasting model to predict the future temperatures. The dataset includes the following information: - Date: The date of the temperature reading. - Temperature: The recorded temperature in degrees Celsius. a. Explain the key steps involved in the time series forecasting process. b. Choose an appropriate forecasting method for this temperature dataset and justify your choice. Provide a step-by-step outine of how you would implement this forecasting method, including any preprocessing steps that may be necessary