Quantitative Analysis Homework.

--> First Three Pages (pages 1-3) are the DATA SET and this needs to be used to answer the questions in the following images.

--> The following few pages that contain QUESTIONS (from Question 1 through Question 40) will need to be answered in order by using the data set avaialble in the first three pages of this document.

BUSA 3000: Quantitative Analysis for Business Topie #9 (Time Series) Problem Set Directions: If the professot instructs you to, submit your final anweers uaing the Quimes feature in D2t. If you are instructed to submit this sheet with yout ansaers in the blaniks, then do that instead. 1. The following data set shows the number of certified organic farms in the U.S. for cach year for the period 2001 through 2008. (a) Create A linear forceasting model for these data, which would be a regression of the form Nt=p0+1xt+ct where y is Farms and x is a time trend. Then, complete the forecasting equation below where the blanks are the coefficients (betas). Rourd all coefficients to the ehole number. Farmst = + Trende (b) Create a qtadratic forecasting model for these data, which would be a regression of the form y1=0+1xt+2xt2+t where y is Farmas, x is an annual time trend, and x2 is the squate of that time trend. Then, complete the forecasting equation below where the blank: wre the coetficients (betas). Round all coefficients to the thole number Farmate = Trende + Trend12 (c) Create an exponential forecasting model for these data, which would be a regression of the form ln(y3)=2+1xt+ct. which is the same as the lines forcasting model except y has been logged before running the regression. Then, complete the forecasting equation below. This time, the first blank is called a, which is calcalated as a =e2 where c=2.71828. The blank in the exponeat is 1 - Round your answers to 3 decimal places. Fhrmest = xe1 w Turact (d) Use your forccasting equations (keep the rotunding aborel) to predict the number of farms for the text 3 years tising each method ( 9 total predictions). Round all forecuses to the whole number, 2. The following data set shows the number of new car dealerships in the U.S. for each year for the period 2003 through 2009. (a) Create a lines forecasting model for these data. Then, completeLte forecasting equation below. Round all coefficients to the whole number. Dcalensthips = Trend, (b) Create a quadratic forecasting model for theso data. Then, complete the forecasting equation below. Rotind oll coefficients to the whole number: Dealerships, = Trend t+ x Trend 22 (c) Create an exponential forecesting model for these data. Then, complete the forvecsting equation below. Rownd your answers to 9 decimal places. Dealerships = e (d) Use your forecasting equations (koop the rounding abovel) to predict the number of dealeruhipe for the next 3 years using esch forecasting method ( 9 total predictions). Round all forocasts to the whole number. 3. The following data set shows quarterly sales for a company for the period 2015 through 2019. (B) Create a scasonally-adjusted linear forecasting model for these data using Q1 as the base quarter category, which would be a regression of the form yi=0+1x1+2Q2+ 3Q3+4Q4+1 where y is Sales, x is a time trend, and the Q's are quarter indicators: Then, complete the forecasting equation below where the blanks are the coefficients (betas). Round all coefficients to 1 decimal place. Sales=+1Trendt+Q2+Q4 (b) Use your forecasting equation (keep the rounding abovel) to predict sales for the next 4 quarters (which would be every quarter of 2020 ). Round all forecasts to the whole number. Question 1(a): Which of the following is the correct linear forecasting equation for Farms? Farms =1,586,485+796( Trend ) Farms =5,491+796 (Trend) Farms =6,761+91 (Trend) Farms =91+6,761 (Trend) Farms =796+5,491 (Trend) None of the above Question 1(b): What is B0 in your QUADRATIC model? Your Answer: Answer Question 3 ( 2.5 points) Question 1(b): What is B1 in your QUADRATIC model? Your Answer: Answer Question 1(b): What is B2 in your QUADRATIC model? Your Answer: Answer Question 5 ( 2.5 points) Question 1(c): What is "a" in your EXPONENTIAL forecasting model? Your Answer: Answer Question 6 ( 2.5 points) Question 1(c): What is B1 in your EXPONENTIAL forecasting model? Your Answer: Answer Question 7 (2.5 points) Question 1(d): What was your LINEAR forecast for 2009? Your Answer: Answer Question 1(d): What was your LINEAR forecast for 2010? Your Answer: Answer Question 9 ( 2.5 points) Question 1(d): What was your LINEAR forecast for 2011? Your Answer: Answer Question 1(d): What was your QUADRATIC forecast for 2009? Your Answer: Answer Question 11 ( 2.5 points) Question 1(d): What was your QUADRATIC forecast for 2010? Your Answer: Answer Question 1(d): What was your QUADRATIC forecast for 2011? Your Answer: Answer Question 13 (2.5 points) Question 1(d): What was your EXPONENTIAL forecast for 2009? Your Answer: Answer Question 14 ( 2.5 points) Question 1(d): What was your EXPONENTIAL forecast for 2010? Your Answer: Answer Question 15 ( 2.5 points) Question 1(d): What was your EXPONENTIAL forecast for 2011? Your Answer: Answer Question 16 ( 2.5 points) Question 2(a): What was B0 in your LINEAR model? Your Answer: Answer Question 17 ( 2.5 points) Question 2(a): What was B1 in your LINEAR model? Your Answer: Answer Question 2(b): What was B0 in your QUADRATIC model? Your Answer: Answer Question 19 ( 2.5 points) Question 2(b): What was B1 in your QUADRATIC model? Your Answer: Answer Question 2(b): What was B2 in your QUADRATIC model? Your Answer: Answer Question 21 ( 2.5 points) Question 2(c): What was "a" in your EXPONENTIAL model? Your Answer: Answer Question 2(c): What was B1 in your EXPONENTIAL model? Your Answer: Answer Question 23 (2.5 points) Question 2(d): What was your LINEAR forecast for 2010? Your Answer: Answer Question 2(d): What was your LINEAR forecast for 2011? Your Answer: Answer Question 25 ( 2.5 points) Question 2(d): What was your LINEAR forecast for 2012? Your Answer: Answer Question 2(d): What was your QUADRATIC forecast for 2010? Your Answer: Answer Question 27 ( 2.5 points) Question 2(d): What was your QUADRATIC forecast for 2011? Your Answer: Answer Question 28 ( 2.5 points) Question 2(d): What was your QUADRATIC forecast for 2012? Your Answer: Answer Question 29 ( 2.5 points) Question 2(d): What was your EXPONENTIAL forecast for 2010? Your Answer: Answer Question 2(d): What was your EXPONENTIAL forecast for 2011 ? Your Answer: Answer Question 31 ( 2.5 points) Question 2(d): What was your EXPONENTIAL forecast for 2012? Your Answer: Answer Question 3(a): What is B0 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 33 ( 2.5 points) Question 3(a): What is B1 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 3(a): What is B2 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 35 ( 2.5 points) Question 3(a): What is B3 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 3(a): What is B4 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 37 ( 2.5 points) Question 3(b): What was your forecast for 2020 Quarter 1? Your Answer: Answer Question 38 ( 2.5 points) Question 3(b): What was your forecast for 2020 Quarter 2? Your Answer: Answer Question 39 ( 2.5 points) Question 3(b): What was your forecast for 2020 Quarter 3? Your Answer: Answer Question 3(b): What was your forecast for 2020 Quarter 4? Your Answer: Answer BUSA 3000: Quantitative Analysis for Business Topie #9 (Time Series) Problem Set Directions: If the professot instructs you to, submit your final anweers uaing the Quimes feature in D2t. If you are instructed to submit this sheet with yout ansaers in the blaniks, then do that instead. 1. The following data set shows the number of certified organic farms in the U.S. for cach year for the period 2001 through 2008. (a) Create A linear forceasting model for these data, which would be a regression of the form Nt=p0+1xt+ct where y is Farms and x is a time trend. Then, complete the forecasting equation below where the blanks are the coefficients (betas). Rourd all coefficients to the ehole number. Farmst = + Trende (b) Create a qtadratic forecasting model for these data, which would be a regression of the form y1=0+1xt+2xt2+t where y is Farmas, x is an annual time trend, and x2 is the squate of that time trend. Then, complete the forecasting equation below where the blank: wre the coetficients (betas). Round all coefficients to the thole number Farmate = Trende + Trend12 (c) Create an exponential forecasting model for these data, which would be a regression of the form ln(y3)=2+1xt+ct. which is the same as the lines forcasting model except y has been logged before running the regression. Then, complete the forecasting equation below. This time, the first blank is called a, which is calcalated as a =e2 where c=2.71828. The blank in the exponeat is 1 - Round your answers to 3 decimal places. Fhrmest = xe1 w Turact (d) Use your forccasting equations (keep the rotunding aborel) to predict the number of farms for the text 3 years tising each method ( 9 total predictions). Round all forecuses to the whole number, 2. The following data set shows the number of new car dealerships in the U.S. for each year for the period 2003 through 2009. (a) Create a lines forecasting model for these data. Then, completeLte forecasting equation below. Round all coefficients to the whole number. Dcalensthips = Trend, (b) Create a quadratic forecasting model for theso data. Then, complete the forecasting equation below. Rotind oll coefficients to the whole number: Dealerships, = Trend t+ x Trend 22 (c) Create an exponential forecesting model for these data. Then, complete the forvecsting equation below. Rownd your answers to 9 decimal places. Dealerships = e (d) Use your forecasting equations (koop the rounding abovel) to predict the number of dealeruhipe for the next 3 years using esch forecasting method ( 9 total predictions). Round all forocasts to the whole number. 3. The following data set shows quarterly sales for a company for the period 2015 through 2019. (B) Create a scasonally-adjusted linear forecasting model for these data using Q1 as the base quarter category, which would be a regression of the form yi=0+1x1+2Q2+ 3Q3+4Q4+1 where y is Sales, x is a time trend, and the Q's are quarter indicators: Then, complete the forecasting equation below where the blanks are the coefficients (betas). Round all coefficients to 1 decimal place. Sales=+1Trendt+Q2+Q4 (b) Use your forecasting equation (keep the rounding abovel) to predict sales for the next 4 quarters (which would be every quarter of 2020 ). Round all forecasts to the whole number. Question 1(a): Which of the following is the correct linear forecasting equation for Farms? Farms =1,586,485+796( Trend ) Farms =5,491+796 (Trend) Farms =6,761+91 (Trend) Farms =91+6,761 (Trend) Farms =796+5,491 (Trend) None of the above Question 1(b): What is B0 in your QUADRATIC model? Your Answer: Answer Question 3 ( 2.5 points) Question 1(b): What is B1 in your QUADRATIC model? Your Answer: Answer Question 1(b): What is B2 in your QUADRATIC model? Your Answer: Answer Question 5 ( 2.5 points) Question 1(c): What is "a" in your EXPONENTIAL forecasting model? Your Answer: Answer Question 6 ( 2.5 points) Question 1(c): What is B1 in your EXPONENTIAL forecasting model? Your Answer: Answer Question 7 (2.5 points) Question 1(d): What was your LINEAR forecast for 2009? Your Answer: Answer Question 1(d): What was your LINEAR forecast for 2010? Your Answer: Answer Question 9 ( 2.5 points) Question 1(d): What was your LINEAR forecast for 2011? Your Answer: Answer Question 1(d): What was your QUADRATIC forecast for 2009? Your Answer: Answer Question 11 ( 2.5 points) Question 1(d): What was your QUADRATIC forecast for 2010? Your Answer: Answer Question 1(d): What was your QUADRATIC forecast for 2011? Your Answer: Answer Question 13 (2.5 points) Question 1(d): What was your EXPONENTIAL forecast for 2009? Your Answer: Answer Question 14 ( 2.5 points) Question 1(d): What was your EXPONENTIAL forecast for 2010? Your Answer: Answer Question 15 ( 2.5 points) Question 1(d): What was your EXPONENTIAL forecast for 2011? Your Answer: Answer Question 16 ( 2.5 points) Question 2(a): What was B0 in your LINEAR model? Your Answer: Answer Question 17 ( 2.5 points) Question 2(a): What was B1 in your LINEAR model? Your Answer: Answer Question 2(b): What was B0 in your QUADRATIC model? Your Answer: Answer Question 19 ( 2.5 points) Question 2(b): What was B1 in your QUADRATIC model? Your Answer: Answer Question 2(b): What was B2 in your QUADRATIC model? Your Answer: Answer Question 21 ( 2.5 points) Question 2(c): What was "a" in your EXPONENTIAL model? Your Answer: Answer Question 2(c): What was B1 in your EXPONENTIAL model? Your Answer: Answer Question 23 (2.5 points) Question 2(d): What was your LINEAR forecast for 2010? Your Answer: Answer Question 2(d): What was your LINEAR forecast for 2011? Your Answer: Answer Question 25 ( 2.5 points) Question 2(d): What was your LINEAR forecast for 2012? Your Answer: Answer Question 2(d): What was your QUADRATIC forecast for 2010? Your Answer: Answer Question 27 ( 2.5 points) Question 2(d): What was your QUADRATIC forecast for 2011? Your Answer: Answer Question 28 ( 2.5 points) Question 2(d): What was your QUADRATIC forecast for 2012? Your Answer: Answer Question 29 ( 2.5 points) Question 2(d): What was your EXPONENTIAL forecast for 2010? Your Answer: Answer Question 2(d): What was your EXPONENTIAL forecast for 2011 ? Your Answer: Answer Question 31 ( 2.5 points) Question 2(d): What was your EXPONENTIAL forecast for 2012? Your Answer: Answer Question 3(a): What is B0 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 33 ( 2.5 points) Question 3(a): What is B1 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 3(a): What is B2 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 35 ( 2.5 points) Question 3(a): What is B3 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 3(a): What is B4 in your seasonally-adjusted linear forecasting model? Your Answer: Answer Question 37 ( 2.5 points) Question 3(b): What was your forecast for 2020 Quarter 1? Your Answer: Answer Question 38 ( 2.5 points) Question 3(b): What was your forecast for 2020 Quarter 2? Your Answer: Answer Question 39 ( 2.5 points) Question 3(b): What was your forecast for 2020 Quarter 3? Your Answer: Answer Question 3(b): What was your forecast for 2020 Quarter 4? Your