hello i just wanna know if my answer to this question is correct or wrong: Consider five years of monthly pro fit for a companyC) Discuss whether a simple exponential smoothing model works well with this data or not.Month SalesJan-16 747Feb-16 697Mar-16 1014Apr-16 1126May-16 1105Jun-16 1450Jul-16 1639Aug-16 1711Sep-16 1307Oct-16 1223Nov-16 975Dec-16 953Jan-17 1024Feb-17 928Mar-17 1442Apr-17 1371May-17 1536Jun-17 2004Jul-17 1854Aug-17 1951Sep-17 1516Oct-17 1642Nov-17 1166Dec-17 1106Jan-18 1189Feb-18 1209Mar-18 1754Apr-18 1843May-18 1769Jun-18 2207Jul-18 2471Aug-18 2288Sep-18 1867Oct-18 1980Nov-18 1418Dec-18 1333Jan-19 1333Feb-19 1370Mar-19 2142Apr-19 2138May-19 2078Jun-19 2960Jul-19 2616Aug-19 2861Sep-19 2237Oct-19 2225Nov-19 1590Dec-19 1659Jan-20 1613Feb-20 1605Mar-20 2349Apr-20 2468May-20 2532Jun-20 3127Jul-20 3288Aug-20 3285Sep-20 2485Oct-20 2723Nov-20 1835Dec-20 1894 here is my answer: Assessing the Applicability of Simple Exponential Smoothing (SES) Model:A time series forecasting technique called SES uses a weighted average of historical observations to predict future values, giving more weight to more recent data. Trend Analysis: Despite some ups and downs over the course of the five years, an overall upward trend is seen. This discrepancy calls into question the applicability of a straightforward exponential smoothing model that depends on a steady trend. Examining Seasonality: Although sales occasionally rise and fall, the seasonality is erratic, making it challenging for the model to appropriately represent and forecast. With such erratic patterns, a basic exponential smoothing model might face difficulties. Evaluation of Variability: The considerable variation in monthly sales points to noisy data, making it more difficult for the model to discern between real trends and chance variations. Randomness Consideration: Since the model might not be able to distinguish between systematic patterns and random noise, forecasting is made more difficult by random fluctuations in sales. In summary:The irregularities, variability, and randomness in the data pose challenges to SES, even though it may be able to partially capture the increasing trend. To get more accurate forecasts, it is therefore advised to use alternative forecasting techniques designed to handle such complexities, such as seasonal ARIMA or dynamic regression models. fuerthermore i dont know if i should use a graph to justify this answer but i found one for this data online and not sure if i should use it or not and here is what they say for why not to use smooth modle:The method of exponential smoothing is not preferred because it cannot predict future values for the year 2022. The method of simple linear regression analysis is preferred over exponential smoothing. Because the method of linear regression analysis is helpful in predicting future values for the year 2022,. in picture 1 is what the expert gave me not sure if that i should use is or not in this question. Feb-20 1605 50Mar-20 2349 51Apr-20 2468 52May-20 2532 53Jun-20 3127 54Jul-20 3288 55Aug-20 3285 56Sep-20 2485 57Oct-20 2723 58Nov-20 1835 59Dec-20 1894 60The method of exponential smoothing is not preferred because it cannot predict the future values for theyear 2022. The method of simple linear regression analysis is preferred over exponential smoothing.Because the method of linear regression analysis is helpful in predicting future values for the year 2022,.SolutionAnswer:The method of exponential smoothing is not preferred because it cannot predict future values for theyear 2022. The method of simple linear regression analysis is preferred over exponential smoothing.Because the method of linear regression analysis is helpful in predicting future values for the year2022,.IS THIS ANSWER HELPFUL? Step 1:Month Sales tJan-16 747 11)Feb-16 697 2 slope-Mar-16 1014 3intercept =26.61 SLOPE(B2:B61,C2:C61)975.42 INTERCEPT(B2:B61,C2:C61)Apr-16 1126 4regression equation: sale = 975.42 + 26.61*xMay-64 1105 5Jun-16 1450 6monthtpredicted salessales predicted =975.42 + 26.61 tJul-16 1639 7Jan-22732918.131=$F$4+$F$3*F8Aug-16 1711 8Feb-22742944.744=$F$4+$F$3*F9Sep-16 1307 9Mar-22752971.356=$F$4+$F$3*F10Oct-16 1223 10Apr-22762997.969=$F$4+$F$3*F11Nov-16 975 11May-22773024.581=$F$4+$F$3*F12Dec-16 953 12Jun-22783051.194=$F$4+$F$3*F13Jan-17 1024 13Jul-22793077.806Feb-17 926 14Aug-22803104.419Mar-17 1442 15Sep-22813131.031Apr-17 1371 16Oct-22823157.644May-17 1536 17Nov-22833184.256=$F$4+$F$3*F14=$F$4+$F$3*F15=$F$4+$F$3*F16=$F$4+$F$3*F17=$F$4+$F$3*F18Jun-17 2004 | 18Dec-22843210.869=$F$4+$F$3*F19Jul-17 1854 19Aug-17 1951 20Sep-17 1516 21Oct-1 1642 22Nov-17 1166|23Dec-17 1106 24Jan-18 1189 25

Question

hello i just wanna know if my answer to this question is correct or wrong: Consider five years of monthly pro fit for a companyC) Discuss whether a simple exponential smoothing model works well with this data or not.Month SalesJan-16 747Feb-16 697Mar-16 1014Apr-16 1126May-16 1105Jun-16 1450Jul-16 1639Aug-16 1711Sep-16 1307Oct-16 1223Nov-16 975Dec-16 953Jan-17 1024Feb-17 928Mar-17 1442Apr-17 1371May-17 1536Jun-17 2004Jul-17 1854Aug-17 1951Sep-17 1516Oct-17 1642Nov-17 1166Dec-17 1106Jan-18 1189Feb-18 1209Mar-18 1754Apr-18 1843May-18 1769Jun-18 2207Jul-18 2471Aug-18 2288Sep-18 1867Oct-18 1980Nov-18 1418Dec-18 1333Jan-19 1333Feb-19 1370Mar-19 2142Apr-19 2138May-19 2078Jun-19 2960Jul-19 2616Aug-19 2861Sep-19 2237Oct-19 2225Nov-19 1590Dec-19 1659Jan-20 1613Feb-20 1605Mar-20 2349Apr-20 2468May-20 2532Jun-20 3127Jul-20 3288Aug-20 3285Sep-20 2485Oct-20 2723Nov-20 1835Dec-20 1894 here is my answer: Assessing the Applicability of Simple Exponential Smoothing (SES) Model:A time series forecasting technique called SES uses a weighted average of historical observations to predict future values, giving more weight to more recent data. Trend Analysis: Despite some ups and downs over the course of the five years, an overall upward trend is seen. This discrepancy calls into question the applicability of a straightforward exponential smoothing model that depends on a steady trend. Examining Seasonality: Although sales occasionally rise and fall, the seasonality is erratic, making it challenging for the model to appropriately represent and forecast. With such erratic patterns, a basic exponential smoothing model might face difficulties. Evaluation of Variability: The considerable variation in monthly sales points to noisy data, making it more difficult for the model to discern between real trends and chance variations. Randomness Consideration: Since the model might not be able to distinguish between systematic patterns and random noise, forecasting is made more difficult by random fluctuations in sales. In summary:The irregularities, variability, and randomness in the data pose challenges to SES, even though it may be able to partially capture the increasing trend. To get more accurate forecasts, it is therefore advised to use alternative forecasting techniques designed to handle such complexities, such as seasonal ARIMA or dynamic regression models.   fuerthermore i dont know if i should use a graph to justify this answer but i found one for this data online and not sure if i should use it or not and here is what they say for why not to use smooth modle:The method of exponential smoothing is not preferred because it cannot predict future values for the year 2022. The method of simple linear regression analysis is preferred over exponential smoothing. Because the method of linear regression analysis is helpful in predicting future values for the year 2022,. in picture 1 is what the expert gave me not sure if that i should use is or not in this question. Feb-20 1605 50Mar-20 2349 51Apr-20 2468 52May-20 2532 53Jun-20 3127 54Jul-20 3288 55Aug-20 3285 56Sep-20 2485 57Oct-20 2723 58Nov-20 1835 59Dec-20 1894 60The method of exponential smoothing is not preferred because it cannot predict the future values for theyear 2022. The method of simple linear regression analysis is preferred over exponential smoothing.Because the method of linear regression analysis is helpful in predicting future values for the year 2022,.SolutionAnswer:The method of exponential smoothing is not preferred because it cannot predict future values for theyear 2022. The method of simple linear regression analysis is preferred over exponential smoothing.Because the method of linear regression analysis is helpful in predicting future values for the year2022,.IS THIS ANSWER HELPFUL? Step 1:Month Sales tJan-16 747 11)Feb-16 697 2 slope-Mar-16 1014 3intercept =26.61 SLOPE(B2:B61,C2:C61)975.42 INTERCEPT(B2:B61,C2:C61)Apr-16 1126 4regression equation: sale = 975.42 + 26.61*xMay-64 1105 5Jun-16 1450 6monthtpredicted salessales predicted =975.42 + 26.61 tJul-16 1639 7Jan-22732918.131=$F$4+$F$3*F8Aug-16 1711 8Feb-22742944.744=$F$4+$F$3*F9Sep-16 1307 9Mar-22752971.356=$F$4+$F$3*F10Oct-16 1223 10Apr-22762997.969=$F$4+$F$3*F11Nov-16 975 11May-22773024.581=$F$4+$F$3*F12Dec-16 953 12Jun-22783051.194=$F$4+$F$3*F13Jan-17 1024 13Jul-22793077.806Feb-17 926 14Aug-22803104.419Mar-17 1442 15Sep-22813131.031Apr-17 1371 16Oct-22823157.644May-17 1536 17Nov-22833184.256=$F$4+$F$3*F14=$F$4+$F$3*F15=$F$4+$F$3*F16=$F$4+$F$3*F17=$F$4+$F$3*F18Jun-17 2004 | 18Dec-22843210.869=$F$4+$F$3*F19Jul-17 1854 19Aug-17 1951 20Sep-17 1516 21Oct-1 1642 22Nov-17 1166|23Dec-17 1106 24Jan-18 1189 25

Accepted Answer

Sales=Intercept+Slope×Month

Here's a breakdown of the key components:

Intercept (975.42): This is…