Time series re... Source Columns (6/0) fear Vehicle sales jas price nemployment SPI dj gas price NS VS 31 12345SN 6 Year 1990 1991 1992 1993 1994 1995 7 8 9 10 1996 1997 1998 1999 11 2000 12 2001 13 2002 15 14 2003 2004 16 2005 17 2006 18 2007 19 2008 20 2009 21 2010 22 2011 23 2012 24 2013 25 2014 26 2015 27 2016 28 2017 29 2018 30 2019 31 2020 Vehicle sales 14137.2 12530.4 13106.6 14185.5 15397.7 15116.8 15456.2 15498.1 15967.3 17415 17811.6 17472.5 17138.5 16968.5 17298.5 17444.7 17048.7 16460.1 13493.3 10602 11772.5 13048.4 14779.5 15882.7 16859.8 17857.3 17878.3 17565.1 17712.8 17488.2 14881.4 gas unemploymen price t 1.22 1.2 1.19 1.17 1.17 1.21 1.29 1.29 1.11 1.22 1.56 1.53 1.44 1.64 1.92 2.34 2.64 2.85 3.32 2.4 2.84 3.58 3.7 3.58 3.43 2.51 2.2 2.47 2.79 2.7 2.24 CPI 130.7 5.6 6.8 136.2 7.5 140.3 6.9 144.5 6.1 148.2 5.6 152.4 5.4 156.9 4.9 160.5 4.5 163 4.2 4 4.7 5.8 6 166.6 172.2 177.1 179.9 184 188.9 5.1 195.3 55 Time series regression example vehicle sales 1990-2020.jmp adj gas price 5.5 16 4.6 201.6 4.6 207.3 5.8 2153 9.3 214.5 9.6 218.1 8.5 224.9 7.9 229.6 6.7 233 5.6 236.7 5 237 240 3.7 3.9 245.1 4.4 251.1 3.7 255.7 6.7 258.8 2.42 2.28 2.20 2.10 2.04 2.05 2.13 2.08 1.76 1.90 2.34 2.24 2.07 2.31 2.63 3.10 3.39 3.56 3.99 2.90 3.37 4.12 4.17 3.98 3.75 2.74 2.37 2.61 2.88 2.73 2.24
Time series regression exampio wehiclo sales 10002020./mp \begin{tabular}{|c|c|c|c|c|c|c|c|} \hline souch & & Year & \begin{tabular}{l} Vohlelo \\ calas \end{tabular} & pales & \begin{tabular}{c} unempleymen \\ t \end{tabular} & CPI & edpas \\ \hline & 1 & 1990 & 141372 & 1,22 & 5.6 & 130.7 & 2.42 \\ \hline & 2 & 1091 & 125304 & 12 & 6.8 & 1562 & 228 \\ \hline & 3 & 1992 & 13106.6 & 1,19 & 7.5 & 140.3 & 220 \\ \hline & 4 & 1993 & 14185.5 & 1,17 & 69 & 144.5 & 2.10 \\ \hline & 5 & 1904 & 15997.7 & 1.17 & 61 & 148.2 & 2.04 \\ \hline & & 1995 & 15116.8 & 121 & 5.6 & 152.4 & 205 \\ \hline & 7 & 1996 & 154562 & 1.29 & 5.4 & 1569 & 2.13 \\ \hline & a & 1997 & 15490.1 & 1.29 & 4.9 & 1605 & 2.06 \\ \hline & 9 & 1998 & 159673 & 1.11 & 4.5 & 163 & 1.76 \\ \hline & 10 & 1999 & 17415 & 1.22 & 42 & 1066 & 1.90 \\ \hline & 11 & 2000 & 178116 & 1.50 & 4 & 172.2 & 2.34 \\ \hline & 12 & 2001 & 17472.5 & 1.53 & 4.7 & 177.1 & 224 \\ \hline Wels sales & 13 & 2000 & 171385 & 1,44 & 58 & 179.9 & 2.07 \\ \hline prose & 14 & 2000 & 169ces.5 & 1.64 & 6 & 184 & 2.31 \\ \hline anploymant & 15 & 2004 & 17206.5 & 1.92 & 5.5 & 169.9 & 2.03 \\ \hline loesporse & 10 & 2005 & 17444.7 & 234 & 8.1 & 195.3 & 3.10 \\ \hline & 17 & 2006 & 17046.7 & 2.04 & 4.6 & 2016 & \\ \hline & 18 & 2007 & 16460.1 & 2.85 & 46 & 2073 & 3.50 \\ \hline & 10 & 2000 & 134933 & 332 & 68 & 214k & 39 \\ \hline & 20 & 2000 & 10002 & 2.4 & 0.3 & 2145 & 200 \\ \hline & 21 & 2010 & 11772.5 & 2.4 & 9.6 & 210.1 & 3.37 \\ \hline & 22 & 2011 & 13048.4 & 3.5a & 8.5 & 224.9 & 4,12 \\ \hline & 23 & 2012 & 147705 & 3.7 & 7.9 & 220.6 & 4.17 \\ \hline & 24 & 2013 & 15027 & 3.58 & 67 & 230 & 3.96 \\ \hline & 25 & 2014 & 16059.8 & 3.43 & 56 & 236.7 & 3.75 \\ \hline & 26 & 2015 & 17657.3 & 2.51 & 5 & 237 & 2.74 \\ \hline & 27 & 2016 & 17 are. 3 & 2.2 & 3.7 & 240 & 2.37 \\ \hline & 20 & 2017 & 17565.1 & 2.47 & 3.9 & 245.1 & 2.61 \\ \hline & 29 & 2018 & 177+2.8 & 2.79 & 4.4 & 251.1 & 2.88 \\ \hline & 30 & 2019 & 174882 & 27 & 3.7 & 2557 & 2.73 \\ \hline & 31 & 2020 & 148b14 & 2.24 & 6.7 & 258.8 & 224 \\ \hline \end{tabular} Develop a model to predict US vehicle sales, in thousands, based on the following variables. Data was collected for 19902020. - US vehicle sales, in thousands - Adjusted gasoline price, $ /gallon, in 2020 \$ - Unemployment rate 1. What type of data set is this? 2. What is the response variable? 3. Which variable has the strongest correlation with vehicle sales? How do you know? 4. State the multiple regression model. 5. Evaluate the model. 6. Predict vehicle sales for 2021 . The unemployment rate was 3.9% and the adjusted gasoline price was $2.96. Time series regression exampio wehiclo sales 10002020./mp \begin{tabular}{|c|c|c|c|c|c|c|c|} \hline souch & & Year & \begin{tabular}{l} Vohlelo \\ calas \end{tabular} & pales & \begin{tabular}{c} unempleymen \\ t \end{tabular} & CPI & edpas \\ \hline & 1 & 1990 & 141372 & 1,22 & 5.6 & 130.7 & 2.42 \\ \hline & 2 & 1091 & 125304 & 12 & 6.8 & 1562 & 228 \\ \hline & 3 & 1992 & 13106.6 & 1,19 & 7.5 & 140.3 & 220 \\ \hline & 4 & 1993 & 14185.5 & 1,17 & 69 & 144.5 & 2.10 \\ \hline & 5 & 1904 & 15997.7 & 1.17 & 61 & 148.2 & 2.04 \\ \hline & & 1995 & 15116.8 & 121 & 5.6 & 152.4 & 205 \\ \hline & 7 & 1996 & 154562 & 1.29 & 5.4 & 1569 & 2.13 \\ \hline & a & 1997 & 15490.1 & 1.29 & 4.9 & 1605 & 2.06 \\ \hline & 9 & 1998 & 159673 & 1.11 & 4.5 & 163 & 1.76 \\ \hline & 10 & 1999 & 17415 & 1.22 & 42 & 1066 & 1.90 \\ \hline & 11 & 2000 & 178116 & 1.50 & 4 & 172.2 & 2.34 \\ \hline & 12 & 2001 & 17472.5 & 1.53 & 4.7 & 177.1 & 224 \\ \hline Wels sales & 13 & 2000 & 171385 & 1,44 & 58 & 179.9 & 2.07 \\ \hline prose & 14 & 2000 & 169ces.5 & 1.64 & 6 & 184 & 2.31 \\ \hline anploymant & 15 & 2004 & 17206.5 & 1.92 & 5.5 & 169.9 & 2.03 \\ \hline loesporse & 10 & 2005 & 17444.7 & 234 & 8.1 & 195.3 & 3.10 \\ \hline & 17 & 2006 & 17046.7 & 2.04 & 4.6 & 2016 & \\ \hline & 18 & 2007 & 16460.1 & 2.85 & 46 & 2073 & 3.50 \\ \hline & 10 & 2000 & 134933 & 332 & 68 & 214k & 39 \\ \hline & 20 & 2000 & 10002 & 2.4 & 0.3 & 2145 & 200 \\ \hline & 21 & 2010 & 11772.5 & 2.4 & 9.6 & 210.1 & 3.37 \\ \hline & 22 & 2011 & 13048.4 & 3.5a & 8.5 & 224.9 & 4,12 \\ \hline & 23 & 2012 & 147705 & 3.7 & 7.9 & 220.6 & 4.17 \\ \hline & 24 & 2013 & 15027 & 3.58 & 67 & 230 & 3.96 \\ \hline & 25 & 2014 & 16059.8 & 3.43 & 56 & 236.7 & 3.75 \\ \hline & 26 & 2015 & 17657.3 & 2.51 & 5 & 237 & 2.74 \\ \hline & 27 & 2016 & 17 are. 3 & 2.2 & 3.7 & 240 & 2.37 \\ \hline & 20 & 2017 & 17565.1 & 2.47 & 3.9 & 245.1 & 2.61 \\ \hline & 29 & 2018 & 177+2.8 & 2.79 & 4.4 & 251.1 & 2.88 \\ \hline & 30 & 2019 & 174882 & 27 & 3.7 & 2557 & 2.73 \\ \hline & 31 & 2020 & 148b14 & 2.24 & 6.7 & 258.8 & 224 \\ \hline \end{tabular} Develop a model to predict US vehicle sales, in thousands, based on the following variables. Data was collected for 19902020. - US vehicle sales, in thousands - Adjusted gasoline price, $ /gallon, in 2020 \$ - Unemployment rate 1. What type of data set is this? 2. What is the response variable? 3. Which variable has the strongest correlation with vehicle sales? How do you know? 4. State the multiple regression model. 5. Evaluate the model. 6. Predict vehicle sales for 2021 . The unemployment rate was 3.9% and the adjusted gasoline price was $2.96
