Below is question 1 with A-G and question 2 with A-F, attached is the dataset you need for both problems. Please don not use CHATGPT, professor will check if you use CHATGPT I will not release the funds. 1- Consider the monthly number of International Passenger (in 1000s) for an airline provided in the Excel file. Use Excel to perform following analysis. a. Find all possible 3-month and 6-month moving averages and forecast for February 2026. Find MAD for both forecasts and determine the best moving average forecast. b. Find all possible 3-month weighted moving averages and forecast for February 2026 if weights of 0.2, 0.3, and 0.5 are used with highest weights assigned to the latest months. Find MAD and compare with MADs in part (a). Is the weighted MA forecast better than simple moving averages in part a? c. Use the exponential moving average to find all forecasts and the forecast for February 2026 Assume forecast of 500 for January 2016 and =0.80. Find MAD and compare MAD values in parts (a and b). d. Construct a trend line graph of # Passengers (use smooth line graph option), insert linear trend line, linear equation and R-squared value. Discuss findings (time series components) based on graph. e. Find monthly seasonal relatives (indices) and construct a column of de-seasonalized values for # of Passengers by dividing monthly number of passengers by respective seasonal monthly indices. f. Construct a trend line graph of De-seas # of Passengers (use smooth line graph option), insert the linear trend line, linear equation and R-squared value. Discuss findings based on graph, equation, and R2. Is this model a better fit compared to model in part (d). Explain why? g. Use the linear trend line equation model in part f to find forecasts for February to May 2026 and seasonalize forecasts by multiplying by respective monthly seasonal indices obtained in part (e). 2- Consider the real estate data on 60 homes in a middle-class neighborhood of Southwest Houston provided on the Excel data file. The variables are: Price (Y) in $1000, Square Feet (X1), # of Rooms (X2), # of Bedrooms (X3), # of Bathrooms (X4), Age (X5) a. Construct Scatter plot of all 5 variables (one at a time) vs. Price (Y) (5 graphs), insert best fitted linear equations and comment on the goodness of the fit based on R-squared values. b. Construct the correlation matrix of all 6 variables and rank variables based on absolute values correlations with Price. Discuss correlation of variables against Price in plain language. c. Construct a full model using all independent variables vs. Price (Y), find the equation, R-squared, significant F, and comment on the goodness of the model. Rank the independent variables based on degree of contribution to the model based on their P-values. d. Based on independent variable ranking in part C, select 4 best variables and construct 4-variable multiple linear regression model to predict Price. Make sure to obtain residual values in the model. Outline the regression equation, R-squared, significant F, and comment on the goodness of the model. Make sure to interpret R-Squared and Significant F values. e. Use the residuals output in part (d) and identify top 5 overvalued and 5 undervalued homes in this neighborhood. f. Based on model in part (d), estimate the value for 3000 sq. ft. home with 6 rooms, 3 baths, 4 bedrooms, and age of 15.