Find the linear regression activity.

bigmartsales options (width=100) setwd ("D: /Statistics Center/Multivariate Analysis/RA Data") bms=read. csv("bigmartsales. csv") [-1] set . seed (111) #Change this sampledata=bms [sample (1 : nrow (bms) , size=500) , ] head (sampledata) Item_Weight Item_Fat_Content Item_Visibility Item_Type Item_MRP Outlet_Identifier 5094 NA Low Fat 0. 00000000 Fruits and Vegetables 115.3492 OUT027 7892 NA Regular 0. 02561619 Snack Foods 188. 1872 OUT027 979 20.75 Regular 0. 10599465 Breakfast 150.5682 OUT010 8367 13 . 65 Low Fat 0. 06561843 Frozen Foods 47. 6402 OUT035 699 NA Low Fat 0. 16486492 Household 255. 2698 OUT019 2117 12. 15 Low Fat 0. 14526636 Health and Hygiene 224. 0404 OUT049 t_Establishment_Year Outlet_Size Outlet_Location_Type Outlet_Type Item_Outlet_Sales 6094 1985 Medium Tier 3 Supermarket Type3 3823 . 0236 7892 1985 Medium Tier 3 Supermarket Type3 6239 . 8776 979 1998 Tier 3 Grocery Store 762 . 3410 8367 2004 Small Tier 2 Supermarket Type1 459 . 4020 699 1985 Small Tier 1 Grocery Store 761 . 0094 2117 1999 Medium Tier 1 Supermarket Type1 3375 . 6060 Using Im bmsmodel=1m (Item_Outlet_Sales~Item_Fat_Content+Item_Type+Item_MRP+ Outlet_Identifier, data=sampledata) summary (bmsmodel) Call : 1m (formula = Item_Outlet_Sales ~ Item_Fat_Content + Item_Type + [tem_MRP + Outlet_Identifier, data = sampledata) Residuals : Min 1Q Median 3Q Max -2820 . 1 -682.4 -89. 1 605. 1 3635.6 Coefficients: Estimate Std. Error t value Pr(> It|) (Intercept -1404.970 374. 709 -3.749 0.000199 *** Item_Fat_Contentlow fat -57 . 118 810.325 -0.070 0.943835 Item_Fat_ContentLow Fat -232 . 687 267 . 510 -0.870 0.384839 Item_Fat_Contentreg -142. 681 551 . 360 -0.259 0.795919 Item_Fat_ContentRegular 45 . 615 278.374 0. 164 0. 869909Item_TypeBreads 173.552 337.561 0. 514 0.607399 Item_TypeBreakfast 1022.738 381 . 786