Hi, please help me make a regression model in excel:

Introduction

COVID-19 has been (as remain as of 2022) a major disruption of the lives across the World. Most specifically COVID-19 itself and the measure taken by governments have affected people's day to day behaviors including mobility, i.e. their ability and desire to go to places.

The provided dataset shows the changes in mobility over two years of pandemic in various boroughs (parts) of London, UK

Dataset

• https://data.london.gov.uk/dataset/google-mobility-by-borough

Directions

1. Build a model to predict the mobility index a month in advance.

Note: Nonlinearity of COVID-19 disruptions should be taken into account when you build you model.

2. Discuss the limitations of the model and whether statistical significance (p-value) of the regression model means predictive power of the model in this case. Propose additional data that may improve the quality of the model.

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Since you can't access the link and I can't upload an excel file, I have included some screenshots of the data so you can make the model as an example for me, and then I can make it for the entire dataset. Can you please tell me what to do about this note "Nonlinearity of COVID-19 disruptions should be taken into account when you build you model" when I make the model for the entire dataset?

Thank you so much

G B D E H area_code retail_and_recreation_percent_change_from_baseline grocery_and_pharmacy_percent_change_from_baseline parks_percent_change_from_baseline transit_stations_percent_change_from_baseline workplaces_percent_change_from_baseline residential_percent_change_from_baseline date area_name E09000021 -20 69 -5 -1 33115 10/15/22 Kingston upon Thames - 30 -31 23 1363 10/15/22 Hammersmith and Fulham E09000013 do w -24 -17 12661 10/15/22 Hackney E09000012 -24 -6 -17 27 -19 3895 10/15/22 Barking and Dagenham 09000002 -9 -17 -9 28245 10/15/22 Tower Hamlets 09000030 -13 -15 -17 -17 21427 10/15/22 Lewisham E09000023 -8 -12 19479 -31 -10 10/15/22 Islington E09000019 -49 E09000004 -17 32 -26 -12 5843 10/15/22 Bexley -26 -16 41 -18 - 8 30193 10/15/22 Wandsworth E09000032 -26 14609 10/15/22 Haringey E09000014 o -10 -16 4 .9 -25 -15 11687 10/15/22 Enfield E09000010 -21 10 3 -11 ON POOHOOOOOOHOOHOHOOOH 27271 10/15/22 Sutton E09000029 -17 -29 -24 -10 - 5 -12 22401 10/15/22 Merton E09000024 -13 -10 16557 10/15/22 Havering E09000016 6817 10/15/22 Brent E09000005 -11 -12 18505 10/15/22 Hounslow E09000018 -9 8765 10/15/22 Camden 09000007 -21 31167 10/15/22 Greenwich 09000011 -5 -18 15583 10/15/22 Harrow E09000015 -13 1071 10/15/22 Ealing E09000009 -11 26297 10/15/22 Southwark E09000028 -22 23375 10/15/22 Newham E09000025 -22 -23 32141 10/15/22 Kensington and Chelsea 09000020 -25 1947 10/15/22 City of London E09000001 -10 7791 10/15/22 Bromley E09000006 -17 -14 144 17531 10/15/22 Hillingdon E09000017 -10 E09000026 -18 -17 -12 24349 10/15/22 Redbridge -24 -10 9739 10/15/22 Croydon E09000008 -20 -14 O HOOOOOOO -18 25323 10/15/22 Richmond upon Thames E09000027 -23 -13 -10 4869 10/15/22 Barnet E09000003 -16 2921 -24 - 8 -18 10/15/22 Westminster E09000033 - 13 -19 -20 29219 10/15/22 Waltham Forest E09000031 o 20453 10/15/22 Lambeth E09000022 -23 -37 -20 -39 2920 10/14/22 Westminster E09000033 135 -17 23374 -18 -25 10/14/22 Newham E09000025 -28 -10 -25 - 32 26296 10/14/22 Southwark E09000028 -24 10712 E09000009 -10 - 12 -30 10/14/22 Ealing -9 -26 15582 10/14/22 Harrow E09000015 -14 30 E09000002 29 -11 -29 3894 10/14/22 Barking and Dagenham -15 -26 -18 40 30192 10/14/22 Wandsworth E09000032 -26 18504 10/14/22 Hounslow E09000018 -7 -30 -41 8764 10/14/22 Camden 09000007 6816 10/14/22 Brent E09000005 -15 10 -17 -29 -43 10/14/22 Tower Hamlets -25 -13 -32 28244 E09000030 - 38 31166 10/14/22 Greenwich E09000011 -6 4 -34 -30 1946 10/14/22 City of London E09000001 -57 -39 21426 10/14/22 Lewisham 09000023 -22 10/14/22 Islington E09000019 - 65 -48 19478 -41 -34 22400 10/14/22 Merton E09000024 -26 -25 27270 10/14/22 Sutton E09000029 -17 -35 - 38 -37 20452 10/14/22 Lambeth E09000022 -23 11686 0/14/22 Enfield E09000010 -21 -30 14608 10/14/22 Haringey E09000014 -20 40 - 38 29218 10/14/22 Waltham Forest E09000031 -13 -21 32 5842 10/14/22 Bexley 09000004 32140 10/14/22 Kensington and Chelsea E09000020 -29 google_activity_by_London_Borou +E G H A B D -18 16556 10/14/22 Havering E09000016 -14 -24 4868 10/14/22 Barnet E09000003 -19 -25 24348 10/14/22 Redbridge E09000026 -17 - 19 4 10/14/22 Hillingdon E09000017 -12 17530 -27 10/14/22 Bromley -15 7790 E09000006 - 18 -34 10/14/22 Kingston upon Thames -49 33114 E09000021 -23 -29 -23 -30 25322 10/14/22 Richmond upon Thames E09000027 -25 9738 10/14/22 Croydon E09000008 -20 - 36 -45 01 \\ U 1 1 1 1 00 00 1 00 00 00 UT O V 13634 10/14/22 Hammersmith and Fulham E09000013 - 33 -31 - 38 12660 10/14/22 Hackney E09000012 -25 -17 -36 10/13/22 Westminster E09000033 -28 2919 -26 -33 -19 -33 20451 10/13/22 Lambeth E09000022 -18 -22 4867 10/13/22 Barne E09000003 -35 41 13633 10/13/22 Hammersmith and Fulham E09000013 -31 -31 -34 12659 10/13/22 Hackney E09000012 -23 -11 - 38 31165 10/13/22 Greenwich E09000011 -14 -22 -34 -7 21425 10/13/22 Lewisham E09000023 -23 -22 10/13/22 City of London E09000001 -40 1945 -34 22399 E09000024 -25 -26 10/13/22 Merton -24 -26 9737 10/13/22 Croydon E09000008 -17 - 63 -42 19477 10/13/22 Islington E09000019 -35 -27 - 38 28243 10/13/22 Tower Hamlets E09000030 -15 -37 -37 -16 27269 10/13/22 Sutton E09000029 - 35 -31 11685 10/13/22 Enfield E09000010 -21 - 28 45 -24 -35 25321 10/13/22 Richmond upon Thames E09000027 -27 O UT UT U1 00 A -13 - 38 29217 10/13/22 Waltham Forest 09000031 -24 14607 10/13/22 Haringey E09000014 -23 -26 10/13/22 Havering E09000016 - 13 -16 16555 -28 -31 5841 10/13/22 Bexley E09000004 -20 -14 -29 -10 6815 10/13/22 Brent E09000005 do -31 -15 3893 10/13/22 Barking and Dagenham E09000002 -17 -32 -21 33113 10/13/22 Kingston upon Thames E09000021 -7 -22 15581 10/13/22 Harrow E09000015 -11 -20 -39 32139 10/13/22 Kensington and Chelsea E09000020 -25 -27 -29 -34 -15 7789 10/13/22 Bromle E09000006 -16 -24 23373 10/13/22 Newham E09000025 -2 -22 -27 10/13/22 Southwark E09000028 -24 26295 -18 - 36 30191 10/13/22 Wandsworth E09000032 28 4 -29 17529 10/13/22 Hillingdon E09000017 -10 -23 -29 10/13/22 Ealing E09000009 - 8 10711 -29 -37 24347 10/13/22 Redbridge E09000026 -15 - 5 -37 8763 10/13/22 Camden E09000007 -27 -13 -25 E09000018 -12 18503 10/13/22 Hounslow -6 -19 10/12/22 Harrow E09000015 -10 101 15580 -34 -31 102 11684 10/12/22 Enfield E09000010 -23 -26 -37 103 10/12/22 Kensington and Chelsea E09000020 -24 32138 -18 - 39 104 10/12/22 Haringey E09000014 5 14606 -26 -34 105 10/12/22 Bromley E09000006 -15 7788 -20 -26 -13 -30 106 16554 10/12/22 Havering E09000016 -24 -40 46 107 1944 10/12/22 City of London E09000001 -31 108 25320 E09000027 -25 10/12/22 Richmond upon Thames -19 -36 109 2918 10/12/22 Westminster E09000033 -28 -18 -21 -15 110 23372 10/12/22 Newham E09000025 -24 -31 111 26294 10/12/22 Southwark E09000028 -25 14 -11 -22 112 18502 10/12/22 Hounslow E09000018 -12 -36 113 E09000007 27 1 -31 8762 10/12/22 Camden 114 10710 10/12/22 Ealing E09000009 google_activity_by_London_Borou +G H D E 35 36 -32 115 27268 10/12/22 Sutton E09000029 -15 -26 - 38 116 22398 10/12/22 Merton E09000024 -25 -25 -19 -26 117 24346 10/12/22 Redbridge E09000026 -24 -17 -32 118 9736 10/12/22 Croydon E09000008 -31 -20 119 20450 E09000022 -3 -29 10/12/22 Lambeth -10 -35 120 17528 10/12/22 Hillingdor E09000017 121 10/12/22 Greenwich E09000011 -19 31164 -22 -32 10/12/22 Kingston upon Thames E09000021 -26 122 33112 -20 -29 -14 10/12/22 Brent -34 43 123 6814 E09000005 -25 -36 124 13632 10/12/22 Hammersmith and Fulham E09000013 -29 -16 125 28242 10/12/22 Tower Hamlets E09000030 -31 -32 -21 - 33 -35 126 5840 10/12/22 Bexley E09000004 -24 127 12658 10/12/22 Hackney E09000012 -28 -34 -15 -32 128 29216 10/12/22 Waltham Forest E09000031 -23 4 129 21424 10/12/22 Lewisham 09000023 -22 -33 130 E09000003 -18 -22 -35 4866 10/12/22 Barnet -27 -30 131 30190 10/12/22 Wandsworth E09000032 -17 -18 -43 132 3892 10/12/22 Barking and Dagenham E09000002 - 65 133 9476 10/12/22 Islington 09000019 -35 -10 -34 -16 -33 134 389 10/11/22 Barking and Dagenham E09000002 -24 -24 135 22397 10/11/22 Merton E09000024 -21 -33 136 21423 10/11/22 Lewisham E09000023 4 -26 -25 137 1943 10/11/22 City of London E09000001 41 -35 -36 -28 138 -20 -29 12657 10/11/22 Hackney E09000012 139 10/09 10/11/22 Ealing E09000009 -12 -29 -37 -24 - 28 140 10/11/22 Kensington and Chelsea E09000020 -20 32137 E09000005 -13 -27 -34 141 6813 10/11/22 Brent 142 7787 10/11/22 Bromley E09000006 -13 A W E G A A A G W E bo U V O W B N N N O O W b S A G s W S H N A E S N W O T a a G A W b W V E N G u b 6 N A R . -35 -37 -16 -65 43 143 27267 10/11/22 Sutton 09000029 144 19475 22 Islington E09000019 - 36 -21 -32 10/11/22 Kingston upon Thames E09000021 -24 -37 145 33111 -3 -19 146 31163 10/11/22 Greenwich E0900001 147 15579 10/11/22 Harrow E09000015 -6 -23 -30 -24 -24 148 26293 10/11/22 Southwark 09000028 -18 9 -32 149 23371 10/11/22 Newham E09000025 -19 - 36 150 11683 10/11/22 Enfield E09000010 10/11/22 Barnet E09000003 -21 -27 151 4865 -9 42 152 17527 10/11/22 Hillingdon 09000017 10/11/22 Hammersmith and Fulham E09000013 -28 153 13631 -18 -26 154 16553 10/11/22 Havering E09000016 -14 - 28 -30 09000022 -19 -30 -37 155 20449 10/11/22 Lambeth -16 10/11/22 Tower Hamlets -30 47 156 28241 E09000030 -25 -28 157 25319 10/11/22 Richmond upon Thames E09000027 -25 -15 -37 158 9735 10/11/22 Croydon E09000008 -7 E09000007 -25 -20 - 35 159 8761 10/11/22 Camden -27 42 160 30189 10/11/22 Wandsworth E09000032 -27 -23 -30 161 -32 14605 10/11/22 Haringey 09000014 5839 10/11/22 Bexley 09000004 -22 162 -18 - 35 - 28 - 39 163 2917 10/11/22 Westminster E09000033 -23 10/11/22 Redbridge -28 -37 164 24345 E09000026 -14 10/11/22 Waltham Forest -16 165 29215 E09000031 -11 -26 E09000018 -9 -10 -24 166 18501 10/11/22 Hounslow 167 15578 10/10/22 Harrow 09000015 - 5 -38 168 20448 10/10/22 Lambeth E09000022 -24 44 -28 -39 169 8760 10/10/22 Camden E09000007 -18 170 27266 10/10/22 Sutton E09000029 34 171 10/10/22 E09000002 google_activity_by_London_Borou +