store24
Ayan Ghosh
18 December 2017
this is HBR Case Study Store24 (A): Managing Employee Retention
setwd("G:/IIEST 2K15-2K20/Intern/Internship/Resources/Week 3/week 3 day 1")
store <- read.csv(paste("Store24.csv", sep=""))
View(store)
summary(store)## store Sales Profit MTenure
## Min. : 1.0 Min. : 699306 Min. :122180 Min. : 0.00
## 1st Qu.:19.5 1st Qu.: 984579 1st Qu.:211004 1st Qu.: 6.67
## Median :38.0 Median :1127332 Median :265014 Median : 24.12
## Mean :38.0 Mean :1205413 Mean :276314 Mean : 45.30
## 3rd Qu.:56.5 3rd Qu.:1362388 3rd Qu.:331314 3rd Qu.: 50.92
## Max. :75.0 Max. :2113089 Max. :518998 Max. :277.99
## CTenure Pop Comp Visibility
## Min. : 0.8871 Min. : 1046 Min. : 1.651 Min. :2.00
## 1st Qu.: 4.3943 1st Qu.: 5616 1st Qu.: 3.151 1st Qu.:3.00
## Median : 7.2115 Median : 8896 Median : 3.629 Median :3.00
## Mean : 13.9315 Mean : 9826 Mean : 3.788 Mean :3.08
## 3rd Qu.: 17.2156 3rd Qu.:14104 3rd Qu.: 4.230 3rd Qu.:4.00
## Max. :114.1519 Max. :26519 Max. :11.128 Max. :5.00
## PedCount Res Hours24 CrewSkill
## Min. :1.00 Min. :0.00 Min. :0.00 Min. :2.060
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:3.225
## Median :3.00 Median :1.00 Median :1.00 Median :3.500
## Mean :2.96 Mean :0.96 Mean :0.84 Mean :3.457
## 3rd Qu.:4.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:3.655
## Max. :5.00 Max. :1.00 Max. :1.00 Max. :4.640
## MgrSkill ServQual
## Min. :2.957 Min. : 57.90
## 1st Qu.:3.344 1st Qu.: 78.95
## Median :3.589 Median : 89.47
## Mean :3.638 Mean : 87.15
## 3rd Qu.:3.925 3rd Qu.: 99.90
## Max. :4.622 Max. :100.00library(psych)
describe(store)## vars n mean sd median trimmed mad
## store 1 75 38.00 21.79 38.00 38.00 28.17
## Sales 2 75 1205413.12 304531.31 1127332.00 1182031.25 288422.04
## Profit 3 75 276313.61 89404.08 265014.00 270260.34 90532.00
## MTenure 4 75 45.30 57.67 24.12 33.58 29.67
## CTenure 5 75 13.93 17.70 7.21 10.60 6.14
## Pop 6 75 9825.59 5911.67 8896.00 9366.07 7266.22
## Comp 7 75 3.79 1.31 3.63 3.66 0.82
## Visibility 8 75 3.08 0.75 3.00 3.07 0.00
## PedCount 9 75 2.96 0.99 3.00 2.97 1.48
## Res 10 75 0.96 0.20 1.00 1.00 0.00
## Hours24 11 75 0.84 0.37 1.00 0.92 0.00
## CrewSkill 12 75 3.46 0.41 3.50 3.47 0.34
## MgrSkill 13 75 3.64 0.41 3.59 3.62 0.45
## ServQual 14 75 87.15 12.61 89.47 88.62 15.61
## min max range skew kurtosis se
## store 1.00 75.00 74.00 0.00 -1.25 2.52
## Sales 699306.00 2113089.00 1413783.00 0.71 -0.09 35164.25
## Profit 122180.00 518998.00 396818.00 0.62 -0.21 10323.49
## MTenure 0.00 277.99 277.99 2.01 3.90 6.66
## CTenure 0.89 114.15 113.26 3.52 15.00 2.04
## Pop 1046.00 26519.00 25473.00 0.62 -0.23 682.62
## Comp 1.65 11.13 9.48 2.48 11.31 0.15
## Visibility 2.00 5.00 3.00 0.25 -0.38 0.09
## PedCount 1.00 5.00 4.00 0.00 -0.52 0.11
## Res 0.00 1.00 1.00 -4.60 19.43 0.02
## Hours24 0.00 1.00 1.00 -1.82 1.32 0.04
## CrewSkill 2.06 4.64 2.58 -0.43 1.64 0.05
## MgrSkill 2.96 4.62 1.67 0.27 -0.53 0.05
## ServQual 57.90 100.00 42.10 -0.66 -0.72 1.46mean,Sd of Profit,Ctenure,Mtenure
mean(store$Profit)## [1] 276313.6mean(store$MTenure)## [1] 45.29644mean(store$CTenure)## [1] 13.9315apply(store[,3:5],2,sd)## Profit MTenure CTenure
## 89404.07634 57.67155 17.69752top 10 and bottom profitable stores
ascorder<- store[order(store$Profit),]
View(ascorder)
ascorder[1:10,1:5]## store Sales Profit MTenure CTenure
## 57 57 699306 122180 24.3485700 2.956879
## 66 66 879581 146058 115.2039000 3.876797
## 41 41 744211 147327 14.9180200 11.926080
## 55 55 925744 147672 6.6703910 18.365500
## 32 32 828918 149033 36.0792600 6.636550
## 13 13 857843 152513 0.6571813 1.577002
## 54 54 811190 159792 6.6703910 3.876797
## 52 52 1073008 169201 24.1185600 3.416838
## 61 61 716589 177046 21.8184200 13.305950
## 37 37 1202917 187765 23.1985000 1.347023descorder<- store[order(-store$Profit),]
View(descorder)
descorder[1:10,1:5]## store Sales Profit MTenure CTenure
## 74 74 1782957 518998 171.09720 29.519510
## 7 7 1809256 476355 62.53080 7.326488
## 9 9 2113089 474725 108.99350 6.061602
## 6 6 1703140 469050 149.93590 11.351130
## 44 44 1807740 439781 182.23640 114.151900
## 2 2 1619874 424007 86.22219 6.636550
## 45 45 1602362 410149 47.64565 9.166325
## 18 18 1704826 394039 239.96980 33.774130
## 11 11 1583446 389886 44.81977 2.036961
## 47 47 1665657 387853 12.84790 6.636550library("car", lib.loc="~/R/win-library/3.4")##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit scatterplot(store$MTenure,store$Profit,main="Scatterplot of Profit vs MTenure",xlab = "Mtenure",ylab = "Profit")
scatterplot(store$CTenure,store$Profit,main="Scatterplot of Profit vs CTenure",xlab = "Ctenure",ylab = "Profit")
cor(store)## store Sales Profit MTenure CTenure
## store 1.00000000 -0.22693400 -0.19993481 -0.05655216 0.019930097
## Sales -0.22693400 1.00000000 0.92387059 0.45488023 0.254315184
## Profit -0.19993481 0.92387059 1.00000000 0.43886921 0.257678895
## MTenure -0.05655216 0.45488023 0.43886921 1.00000000 0.243383135
## CTenure 0.01993010 0.25431518 0.25767890 0.24338314 1.000000000
## Pop -0.28936691 0.40348147 0.43063326 -0.06089646 -0.001532449
## Comp 0.03194023 -0.23501372 -0.33454148 0.18087179 -0.070281327
## Visibility -0.02648858 0.13065638 0.13569207 0.15651731 0.066506016
## PedCount -0.22117519 0.42391087 0.45023346 0.06198608 -0.084112627
## Res -0.03142976 -0.16672402 -0.15947734 -0.06234721 -0.340340876
## Hours24 0.02687986 0.06324716 -0.02568703 -0.16513872 0.072865022
## CrewSkill 0.04866273 0.16402179 0.16008443 0.10162169 0.257154817
## MgrSkill -0.07218804 0.31163056 0.32284842 0.22962743 0.124045346
## ServQual -0.32246921 0.38638112 0.36245032 0.18168875 0.081156172
## Pop Comp Visibility PedCount Res
## store -0.289366908 0.03194023 -0.02648858 -0.221175193 -0.03142976
## Sales 0.403481471 -0.23501372 0.13065638 0.423910867 -0.16672402
## Profit 0.430633264 -0.33454148 0.13569207 0.450233461 -0.15947734
## MTenure -0.060896460 0.18087179 0.15651731 0.061986084 -0.06234721
## CTenure -0.001532449 -0.07028133 0.06650602 -0.084112627 -0.34034088
## Pop 1.000000000 -0.26828355 -0.04998269 0.607638861 -0.23693726
## Comp -0.268283553 1.00000000 0.02844548 -0.146325204 0.21923878
## Visibility -0.049982694 0.02844548 1.00000000 -0.141068116 0.02194756
## PedCount 0.607638861 -0.14632520 -0.14106812 1.000000000 -0.28437852
## Res -0.236937265 0.21923878 0.02194756 -0.284378520 1.00000000
## Hours24 -0.221767927 0.12957478 0.04692587 -0.275973353 -0.08908708
## CrewSkill 0.282845090 -0.04229731 -0.19745297 0.213672596 -0.15331247
## MgrSkill 0.083554590 0.22407913 0.07348301 0.087475440 -0.03213640
## ServQual 0.123946521 0.01814508 0.20992919 -0.005445552 0.09081624
## Hours24 CrewSkill MgrSkill ServQual
## store 0.02687986 0.04866273 -0.07218804 -0.322469213
## Sales 0.06324716 0.16402179 0.31163056 0.386381121
## Profit -0.02568703 0.16008443 0.32284842 0.362450323
## MTenure -0.16513872 0.10162169 0.22962743 0.181688755
## CTenure 0.07286502 0.25715482 0.12404535 0.081156172
## Pop -0.22176793 0.28284509 0.08355459 0.123946521
## Comp 0.12957478 -0.04229731 0.22407913 0.018145080
## Visibility 0.04692587 -0.19745297 0.07348301 0.209929194
## PedCount -0.27597335 0.21367260 0.08747544 -0.005445552
## Res -0.08908708 -0.15331247 -0.03213640 0.090816237
## Hours24 1.00000000 0.10536295 -0.03883007 0.058325655
## CrewSkill 0.10536295 1.00000000 -0.02100949 -0.033516504
## MgrSkill -0.03883007 -0.02100949 1.00000000 0.356702708
## ServQual 0.05832565 -0.03351650 0.35670271 1.000000000cor(store$Profit,store$MTenure)## [1] 0.4388692cor(store$Profit,store$CTenure)## [1] 0.2576789library(corrgram)
corrgram(store[,1:14],order=FALSE,main ="Corrgram of store variables",lower.panel=panel.shade,upper.panel=panel.pie,text.panel=panel.txt)
cor.test(store$Profit,store$MTenure,method = "pearson")##
## Pearson's product-moment correlation
##
## data: store$Profit and store$MTenure
## t = 4.1731, df = 73, p-value = 8.193e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2353497 0.6055175
## sample estimates:
## cor
## 0.4388692cor.test(store$Profit,store$CTenure,method = "pearson")##
## Pearson's product-moment correlation
##
## data: store$Profit and store$CTenure
## t = 2.2786, df = 73, p-value = 0.02562
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03262507 0.45786339
## sample estimates:
## cor
## 0.2576789the p-value in both the cases are 8.193e-05 and 0.02562 respectively.
fit<-lm(Profit~MTenure,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ MTenure, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -177817 -52029 -8635 50871 188316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 245496.3 11906.4 20.619 < 2e-16 ***
## MTenure 680.3 163.0 4.173 8.19e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80880 on 73 degrees of freedom
## Multiple R-squared: 0.1926, Adjusted R-squared: 0.1815
## F-statistic: 17.41 on 1 and 73 DF, p-value: 8.193e-05fit$coefficients## (Intercept) MTenure
## 245496.2904 680.3475confint(fit)## 2.5 % 97.5 %
## (Intercept) 221766.8830 269225.698
## MTenure 355.4221 1005.273store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 245496.3 304157.3 261748.8 245496.3 248134.2 347504.8 288039.0 245496.3
## 9 10 11 12 13 14 15 16
## 319649.7 266912.9 275989.3 434624.5 245943.4 304939.8 245496.3 261592.3
## 17 18 19 20 21 22 23 24
## 267069.4 408759.1 247821.3 289760.3 261905.3 257993.1 253924.3 275989.3
## 25 26 27 28 29 30 31 32
## 245496.3 245943.4 279588.6 261748.8 258775.5 295394.0 245496.3 270042.7
## 33 34 35 36 37 38 39 40
## 280684.0 265348.0 270668.7 273798.5 261279.3 256115.2 269103.8 329039.1
## 41 42 43 44 45 46 47 48
## 255645.7 247195.3 366305.9 369480.4 277911.9 249699.1 254237.3 266912.9
## 49 50 51 52 53 54 55 56
## 283187.8 254237.3 247821.3 261905.3 284439.7 250034.5 250034.5 248760.2
## 57 58 59 60 61 62 63 64
## 262061.8 248603.7 254550.3 268299.0 260340.4 253767.9 251107.5 245496.3
## 65 66 67 68 69 70 71 72
## 347706.1 323875.0 249073.2 257367.1 275519.8 255645.7 275363.4 331542.9
## 73 74 75
## 273485.5 361901.8 247038.8residuals(fit)## 1 2 3 4 5 6
## 19517.710 119849.657 -39013.799 -35374.290 52345.751 121545.193
## 7 8 9 10 11 12
## 188316.035 115618.710 155075.253 11712.057 113896.691 -105604.531
## 13 14 15 16 17 18
## -93430.402 -43368.790 -41545.290 -65315.312 -1485.430 -14720.147
## 19 20 21 22 23 24
## 13673.729 -20525.346 20678.707 109042.937 23489.653 -8635.309
## 25 26 27 28 29 30
## 36627.710 -34031.402 -49394.559 11287.201 5180.490 38213.043
## 31 32 33 34 35 36
## -33611.290 -121009.725 12061.021 116850.952 51955.321 -54506.461
## 37 38 39 40 41 42
## -73514.332 -52931.195 -47973.791 -106126.087 -108318.728 16876.685
## 43 44 45 46 47 48
## -29072.870 70300.628 132237.110 66080.859 133615.673 17256.057
## 49 50 51 52 53 54
## -87911.808 -3224.327 -10477.271 -92704.293 80578.285 -90242.474
## 55 56 57 58 59 60
## -102362.474 -59525.206 -139881.779 -21002.716 48518.699 87772.012
## 61 62 63 64 65 66
## -83294.398 -51126.860 -12071.542 -24339.290 -46065.054 -177816.977
## 67 68 69 70 71 72
## 112993.816 -21028.103 99873.158 -1442.728 -76834.356 -134770.902
## 73 74 75
## 5707.519 157096.155 49787.174summary(fit)##
## Call:
## lm(formula = Profit ~ MTenure, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -177817 -52029 -8635 50871 188316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 245496.3 11906.4 20.619 < 2e-16 ***
## MTenure 680.3 163.0 4.173 8.19e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80880 on 73 degrees of freedom
## Multiple R-squared: 0.1926, Adjusted R-squared: 0.1815
## F-statistic: 17.41 on 1 and 73 DF, p-value: 8.193e-05fit<-lm(Profit~CTenure,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ CTenure, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -139848 -64869 -9022 45057 222393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258178.4 12814.4 20.148 <2e-16 ***
## CTenure 1301.7 571.3 2.279 0.0256 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 86970 on 73 degrees of freedom
## Multiple R-squared: 0.0664, Adjusted R-squared: 0.05361
## F-statistic: 5.192 on 1 and 73 DF, p-value: 0.02562fit$coefficients## (Intercept) CTenure
## 258178.442 1301.739confint(fit)## 2.5 % 97.5 %
## (Intercept) 232639.4547 283717.429
## CTenure 163.1392 2440.338store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 290468.0 266817.5 264721.9 265170.9 267116.9 272954.6 267715.6 332080.9
## 9 10 11 12 13 14 15 16
## 266069.1 288372.4 260830.0 266817.5 260231.3 262027.5 269212.5 264272.8
## 17 18 19 20 21 22 23 24
## 262925.6 302143.5 280289.3 265919.4 267565.9 291964.8 262626.3 262626.3
## 25 26 27 28 29 30 31 32
## 271607.5 284779.9 280888.0 280289.3 288821.4 288671.7 272206.2 266817.5
## 33 34 35 36 37 38 39 40
## 263225.0 283881.8 277445.2 285378.6 259931.9 260231.3 265320.6 279989.9
## 41 42 43 44 45 46 47 48
## 273703.1 370250.9 265320.6 406774.4 270110.6 265021.3 266817.5 268913.1
## 49 50 51 52 53 54 55 56
## 277295.6 279091.8 267416.2 262626.3 268913.1 263225.0 282085.5 261728.2
## 57 58 59 60 61 62 63 64
## 262027.5 263524.4 276098.1 266518.1 275499.3 267116.9 267116.9 259333.2
## 65 66 67 68 69 70 71 72
## 288671.7 263225.0 262626.3 261129.4 293012.6 263225.0 308131.0 293910.8
## 73 74 75
## 266518.1 296605.1 269511.9residuals(fit)## 1 2 3 4 5
## -25453.9802 157189.5039 -41986.8842 -55048.9437 33363.1300
## 6 7 8 9 10
## 196095.3525 208639.3849 29034.1421 208655.9360 -9747.3631
## 11 12 13 14 15
## 129055.9671 62202.5039 -107718.2864 -456.5258 -65261.4819
## 16 17 18 19 20
## -67995.8247 2658.3552 91895.4646 -18794.2945 3315.6229
## 21 22 23 24 25
## 15018.0705 75071.1504 14787.7278 4727.7278 10516.5349
## 26 27 28 29 30
## -72867.8896 -50694.0422 -7253.2945 -24865.4239 44935.2630
## 31 32 33 34 35
## -60321.2129 -117784.4961 29519.9813 98317.2320 45178.7573
## 36 37 38 39 40
## -66086.6374 -72166.9139 -57047.2864 -44190.6306 -57076.9206
## 41 42 43 44 45
## -126376.0822 -106178.9360 71912.3694 33006.6073 140038.3978
## 46 47 48 49 50
## 50758.7433 121035.5039 15255.8907 -82019.5557 -28078.7990
## 51 52 53 54 55
## -30072.2425 -93425.2722 96104.8907 -103433.0187 -134413.5247
## 56 57 58 59 60
## -72493.1519 -139847.5258 -35923.3913 26970.9398 89552.8765
## 61 62 63 64 65
## -98453.3125 -64475.8700 -28080.8700 -38176.1670 12969.2630
## 66 67 68 69 70
## -117167.0187 99440.7278 -24790.4055 82380.3548 -9022.0187
## 71 72 73 74 75
## -109601.9999 -97138.7668 12674.8765 222392.8683 27314.1442summary(fit)##
## Call:
## lm(formula = Profit ~ CTenure, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -139848 -64869 -9022 45057 222393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258178.4 12814.4 20.148 <2e-16 ***
## CTenure 1301.7 571.3 2.279 0.0256 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 86970 on 73 degrees of freedom
## Multiple R-squared: 0.0664, Adjusted R-squared: 0.05361
## F-statistic: 5.192 on 1 and 73 DF, p-value: 0.02562fit<-lm(Profit~Comp,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ Comp, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -172707 -65521 -24559 56628 209205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 362702 30119 12.042 < 2e-16 ***
## Comp -22807 7520 -3.033 0.00335 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 84830 on 73 degrees of freedom
## Multiple R-squared: 0.1119, Adjusted R-squared: 0.09975
## F-statistic: 9.2 on 1 and 73 DF, p-value: 0.003351fit$coefficients## (Intercept) Comp
## 362702.27 -22807.37confint(fit)## 2.5 % 97.5 %
## (Intercept) 302674.57 422729.970
## Comp -37793.77 -7820.982store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 298889.8 266100.4 260190.7 265680.9 325039.0 290069.6 285661.2 296672.3
## 9 10 11 12 13 14 15 16
## 302544.9 310911.9 288067.5 250857.3 261536.3 266157.4 275025.7 286418.4
## 17 18 19 20 21 22 23 24
## 304276.5 271593.4 299899.5 268086.0 271012.0 260881.9 280720.3 287880.6
## 25 26 27 28 29 30 31 32
## 282488.3 267408.6 277863.3 289868.7 239723.2 281090.1 291983.7 256842.6
## 33 34 35 36 37 38 39 40
## 241617.6 308849.6 280298.8 273091.6 260254.7 266318.5 247608.6 108904.6
## 41 42 43 44 45 46 47 48
## 263157.2 289010.5 279372.3 279944.3 283501.2 290985.4 307446.7 252115.0
## 49 50 51 52 53 54 55 56
## 277998.6 322752.0 306881.8 212512.5 290702.4 277037.5 216979.3 257850.6
## 57 58 59 60 61 62 63 64
## 294887.4 271612.5 287407.1 300676.5 288259.1 266878.2 268357.7 308452.4
## 65 66 67 68 69 70 71 72
## 289286.2 212862.6 280980.2 282766.8 272624.9 291486.6 274749.6 278757.8
## 73 74 75
## 267364.4 309792.6 319685.0residuals(fit)## 1 2 3 4 5
## -33875.7934 157906.6149 -37455.7437 -55558.9347 -24558.9945
## 6 7 8 9 10
## 178980.3881 190693.7569 64442.6763 172180.1425 -32286.9477
## 11 12 13 14 15
## 101818.5334 78162.6949 -109023.2647 -4586.3579 -71074.7086
## 16 17 18 19 20
## -90141.3794 -38692.5301 122445.6414 -38404.5214 1149.0050
## 21 22 23 24 25
## 11571.9558 106154.0560 -3306.2765 -20526.6286 -364.2812
## 26 27 28 29 30
## -55496.6160 -47669.2652 -16832.7017 24232.8442 52516.8792
## 31 32 33 34 35
## -80098.6979 -107809.5757 51127.4409 73349.3862 42325.2494
## 36 37 38 39 40
## -53799.6205 -72489.6728 -63134.4692 -26478.5544 114008.4458
## 41 42 43 44 45
## -115830.2076 -24938.5287 57860.6849 159836.6760 126647.8205
## 46 47 48 49 50
## 24794.5581 80406.3134 32053.9595 -82722.6270 -71739.0080
## 51 52 53 54 55
## -69537.8164 -43311.4539 74315.6204 -117245.5242 -69307.2552
## 56 57 58 59 60
## -68615.6388 -172707.3730 -44011.4939 15661.9209 55394.4770
## 61 62 63 64 65
## -111213.1397 -64237.2306 -29321.7449 -87295.3549 12354.7530
## 66 67 68 69 70
## -66804.6155 81086.8107 -46427.7593 102768.0867 -37283.6112
## 71 72 73 74 75
## -76220.6482 -81985.7704 11828.5618 209205.4154 -22859.0320summary(fit)##
## Call:
## lm(formula = Profit ~ Comp, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -172707 -65521 -24559 56628 209205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 362702 30119 12.042 < 2e-16 ***
## Comp -22807 7520 -3.033 0.00335 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 84830 on 73 degrees of freedom
## Multiple R-squared: 0.1119, Adjusted R-squared: 0.09975
## F-statistic: 9.2 on 1 and 73 DF, p-value: 0.003351fit<-lm(Profit~Pop,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ Pop, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152198 -52285 -17228 43501 235602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.123e+05 1.829e+04 11.611 < 2e-16 ***
## Pop 6.513e+00 1.598e+00 4.077 0.000115 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81240 on 73 degrees of freedom
## Multiple R-squared: 0.1854, Adjusted R-squared: 0.1743
## F-statistic: 16.62 on 1 and 73 DF, p-value: 0.000115fit$coefficients## (Intercept) Pop
## 212323.4932 6.5126confint(fit)## 2.5 % 97.5 %
## (Intercept) 1.758793e+05 2.487676e+05
## Pop 3.328753e+00 9.696448e+00store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 261395.9 268527.2 275463.2 230539.2 344757.2 322555.8 327948.2 347941.9
## 9 10 11 12 13 14 15 16
## 385031.1 319006.4 352670.0 285004.1 304711.2 257247.4 268878.9 257078.1
## 17 18 19 20 21 22 23 24
## 306606.4 237117.0 242730.8 312467.8 303643.2 266247.8 302177.8 271386.3
## 25 26 27 28 29 30 31 32
## 252590.9 277443.0 265420.7 307882.9 286241.5 219591.6 229113.0 275476.2
## 33 34 35 36 37 38 39 40
## 265577.0 283460.6 305850.9 233281.0 270090.3 255026.6 270259.6 228741.8
## 41 42 43 44 45 46 47 48
## 275502.2 226039.0 235085.0 346639.4 328299.9 272793.0 366170.7 267622.0
## 49 50 51 52 53 54 55 56
## 224456.5 307752.6 232681.9 309094.2 257319.0 236726.2 280914.2 301806.6
## 57 58 59 60 61 62 63 64
## 236042.4 267530.8 252903.5 269927.4 231952.5 276277.2 261695.5 323754.1
## 65 66 67 68 69 70 71 72
## 219324.5 219135.7 287557.1 271054.1 245212.1 341331.6 233587.1 232844.7
## 73 74 75
## 253196.6 283395.5 270715.5residuals(fit)## 1 2 3 4 5 6
## 3618.062 155479.765 -52728.155 -20417.237 -44277.223 146494.232
## 7 8 9 10 11 12
## 148406.798 13173.115 89693.856 -40381.401 37215.967 44015.886
## 13 14 15 16 17 18
## -152198.243 4323.589 -64927.915 -60801.083 -41022.410 156922.037
## 19 20 21 22 23 24
## 18764.175 -43232.750 -21059.177 100788.175 -24763.842 -4032.267
## 25 26 27 28 29 30
## 29533.098 -65530.985 -35226.725 -34846.880 -22285.508 114015.445
## 31 32 33 34 35 36
## -17227.977 -126443.180 27167.973 98738.372 16773.052 -13989.041
## 37 38 39 40 41 42
## -82325.259 -51842.614 -49129.587 -5828.759 -128175.230 38032.970
## 43 44 45 46 47 48
## 102147.968 93141.635 81849.118 42987.012 21682.346 16547.016
## 49 50 51 52 53 54
## -29180.468 -56739.628 4662.118 -139893.223 107698.950 -76934.207
## 55 56 57 58 59 60
## -133242.201 -112571.623 -113862.384 -39929.807 50165.493 86143.556
## 61 62 63 64 65 66
## -54906.471 -73636.230 -22659.517 -102597.087 82316.461 -73077.673
## 67 68 69 70 71 72
## 74509.946 -34715.124 130180.875 -87128.595 -35058.134 -36072.697
## 73 74 75
## 25996.426 235602.498 26110.531summary(fit)##
## Call:
## lm(formula = Profit ~ Pop, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152198 -52285 -17228 43501 235602
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.123e+05 1.829e+04 11.611 < 2e-16 ***
## Pop 6.513e+00 1.598e+00 4.077 0.000115 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81240 on 73 degrees of freedom
## Multiple R-squared: 0.1854, Adjusted R-squared: 0.1743
## F-statistic: 16.62 on 1 and 73 DF, p-value: 0.000115fit<-lm(Profit~PedCount,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ PedCount, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -131878 -57678 -1538 45741 200501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 156254 29373 5.320 1.09e-06 ***
## PedCount 40561 9415 4.308 5.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80370 on 73 degrees of freedom
## Multiple R-squared: 0.2027, Adjusted R-squared: 0.1918
## F-statistic: 18.56 on 1 and 73 DF, p-value: 5.057e-05fit$coefficients## (Intercept) PedCount
## 156253.57 40560.82confint(fit)## 2.5 % 97.5 %
## (Intercept) 97713.52 214793.63
## PedCount 21796.96 59324.69store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 277936.0 277936.0 277936.0 237375.2 359057.7 318496.9 359057.7 277936.0
## 9 10 11 12 13 14 15 16
## 318496.9 277936.0 359057.7 318496.9 237375.2 237375.2 318496.9 277936.0
## 17 18 19 20 21 22 23 24
## 318496.9 277936.0 277936.0 237375.2 277936.0 277936.0 318496.9 277936.0
## 25 26 27 28 29 30 31 32
## 277936.0 237375.2 237375.2 318496.9 237375.2 237375.2 237375.2 277936.0
## 33 34 35 36 37 38 39 40
## 237375.2 318496.9 277936.0 237375.2 277936.0 237375.2 318496.9 318496.9
## 41 42 43 44 45 46 47 48
## 277936.0 237375.2 277936.0 318496.9 277936.0 277936.0 359057.7 237375.2
## 49 50 51 52 53 54 55 56
## 196814.4 318496.9 318496.9 277936.0 237375.2 237375.2 277936.0 277936.0
## 57 58 59 60 61 62 63 64
## 237375.2 237375.2 277936.0 318496.9 196814.4 318496.9 277936.0 277936.0
## 65 66 67 68 69 70 71 72
## 196814.4 277936.0 277936.0 237375.2 277936.0 318496.9 196814.4 196814.4
## 73 74 75
## 277936.0 318496.9 318496.9residuals(fit)## 1 2 3 4 5
## -12922.04629 146070.95371 -55201.04629 -27253.22247 -58577.69393
## 6 7 8 9 10
## 150553.12989 117297.30607 83178.95371 156228.12989 688.95371
## 11 12 13 14 15
## 30828.30607 10523.12989 -84862.22247 24195.77753 -114545.87011
## 16 17 18 19 20
## -81659.04629 -52912.87011 116102.95371 -16441.04629 31859.77753
## 21 22 23 24 25
## 4647.95371 89099.95371 -41082.87011 -10582.04629 4187.95371
## 26 27 28 29 30
## -25463.22247 -7181.22247 -45460.87011 26580.77753 96231.77753
## 31 32 33 34 35
## -25490.22247 -128903.04629 55369.77753 63702.12989 44687.95371
## 36 37 38 39 40
## -18083.22247 -90171.04629 -34191.22247 -97366.87011 -95583.87011
## 41 42 43 44 45
## -130609.04629 26696.77753 59296.95371 121284.12989 132212.95371
## 46 47 48 49 50
## 37843.95371 28795.30607 46793.77753 -1538.39865 -67483.87011
## 51 52 53 54 55
## -81152.87011 -108735.04629 127642.77753 -77583.22247 -130264.04629
## 56 57 58 59 60
## -88701.04629 -115195.22247 -9774.22247 25132.95371 37574.12989
## 61 62 63 64 65
## -19768.39865 -115855.87011 -38900.04629 -56779.04629 104826.60135
## 66 67 68 69 70
## -131878.04629 84130.95371 -1036.22247 97456.95371 -64293.87011
## 71 72 73 74 75
## 1714.60135 -42.39865 1256.95371 200501.12989 -21670.87011summary(fit)##
## Call:
## lm(formula = Profit ~ PedCount, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -131878 -57678 -1538 45741 200501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 156254 29373 5.320 1.09e-06 ***
## PedCount 40561 9415 4.308 5.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80370 on 73 degrees of freedom
## Multiple R-squared: 0.2027, Adjusted R-squared: 0.1918
## F-statistic: 18.56 on 1 and 73 DF, p-value: 5.057e-05fit<-lm(Profit~Res,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ Res, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -151243 -62419 -9467 57891 245575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 345696 51305 6.738 3.18e-09 ***
## Res -72273 52363 -1.380 0.172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 88860 on 73 degrees of freedom
## Multiple R-squared: 0.02543, Adjusted R-squared: 0.01208
## F-statistic: 1.905 on 1 and 73 DF, p-value: 0.1717fit$coefficients## (Intercept) Res
## 345695.67 -72272.97confint(fit)## 2.5 % 97.5 %
## (Intercept) 243445.6 447945.74
## Res -176631.5 32085.58store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 273422.7 273422.7 273422.7 273422.7 345695.7 273422.7 273422.7 273422.7
## 9 10 11 12 13 14 15 16
## 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7
## 17 18 19 20 21 22 23 24
## 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7
## 25 26 27 28 29 30 31 32
## 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7
## 33 34 35 36 37 38 39 40
## 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7
## 41 42 43 44 45 46 47 48
## 273422.7 273422.7 273422.7 345695.7 273422.7 273422.7 273422.7 273422.7
## 49 50 51 52 53 54 55 56
## 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7
## 57 58 59 60 61 62 63 64
## 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7
## 65 66 67 68 69 70 71 72
## 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7 273422.7
## 73 74 75
## 273422.7 273422.7 345695.7residuals(fit)## 1 2 3 4 5
## -8408.6944 150584.3056 -50687.6944 -63300.6944 -45215.6667
## 6 7 8 9 10
## 195627.3056 202932.3056 87692.3056 201302.3056 5202.3056
## 11 12 13 14 15
## 116463.3056 55597.3056 -120909.6944 -11851.6944 -69471.6944
## 16 17 18 19 20
## -77145.6944 -7838.6944 120616.3056 -11927.6944 -4187.6944
## 21 22 23 24 25
## 9161.3056 93613.3056 3991.3056 -6068.6944 8701.3056
## 26 27 28 29 30
## -61510.6944 -43228.6944 -386.6944 -9466.6944 60184.3056
## 31 32 33 34 35
## -61537.6944 -124389.6944 19322.3056 108776.3056 49201.3056
## 36 37 38 39 40
## -54130.6944 -85657.6944 -70238.6944 -52292.6944 -50509.6944
## 41 42 43 44 45
## -126095.6944 -9350.6944 63810.3056 94085.3333 136726.3056
## 46 47 48 49 50
## 42357.3056 114430.3056 10746.3056 -78146.6944 -22409.6944
## 51 52 53 54 55
## -36078.6944 -104221.6944 91595.3056 -113630.6944 -125750.6944
## 56 57 58 59 60
## -84187.6944 -151242.6944 -45821.6944 29646.3056 82648.3056
## 61 62 63 64 65
## -96376.6944 -70781.6944 -34386.6944 -52265.6944 28218.3056
## 66 67 68 69 70
## -127364.6944 88644.3056 -37083.6944 101970.3056 -19219.6944
## 71 72 73 74 75
## -74893.6944 -76650.6944 5770.3056 245575.3056 -48869.6667summary(fit)##
## Call:
## lm(formula = Profit ~ Res, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -151243 -62419 -9467 57891 245575
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 345696 51305 6.738 3.18e-09 ***
## Res -72273 52363 -1.380 0.172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 88860 on 73 degrees of freedom
## Multiple R-squared: 0.02543, Adjusted R-squared: 0.01208
## F-statistic: 1.905 on 1 and 73 DF, p-value: 0.1717fit<-lm(Profit~Hours24,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ Hours24, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153138 -64315 -11246 52884 237458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 281540 25976 10.84 <2e-16 ***
## Hours24 -6222 28343 -0.22 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89980 on 73 degrees of freedom
## Multiple R-squared: 0.0006598, Adjusted R-squared: -0.01303
## F-statistic: 0.0482 on 1 and 73 DF, p-value: 0.8268fit$coefficients## (Intercept) Hours24
## 281540.417 -6222.385confint(fit)## 2.5 % 97.5 %
## (Intercept) 229769.66 333311.17
## Hours24 -62708.91 50264.14store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 275318.0 275318.0 275318.0 275318.0 275318.0 281540.4 275318.0 275318.0
## 9 10 11 12 13 14 15 16
## 275318.0 281540.4 275318.0 281540.4 275318.0 275318.0 275318.0 281540.4
## 17 18 19 20 21 22 23 24
## 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0 281540.4
## 25 26 27 28 29 30 31 32
## 275318.0 275318.0 275318.0 281540.4 275318.0 275318.0 275318.0 281540.4
## 33 34 35 36 37 38 39 40
## 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0
## 41 42 43 44 45 46 47 48
## 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0 275318.0
## 49 50 51 52 53 54 55 56
## 275318.0 281540.4 275318.0 275318.0 275318.0 275318.0 275318.0 281540.4
## 57 58 59 60 61 62 63 64
## 275318.0 275318.0 275318.0 275318.0 275318.0 281540.4 275318.0 275318.0
## 65 66 67 68 69 70 71 72
## 275318.0 275318.0 275318.0 275318.0 275318.0 281540.4 275318.0 275318.0
## 73 74 75
## 275318.0 281540.4 275318.0residuals(fit)## 1 2 3 4 5 6
## -10304.032 148688.968 -52583.032 -65196.032 25161.968 187509.583
## 7 8 9 10 11 12
## 201036.968 85796.968 199406.968 -2915.417 114567.968 47479.583
## 13 14 15 16 17 18
## -122805.032 -13747.032 -71367.032 -85263.417 -9734.032 118720.968
## 19 20 21 22 23 24
## -13823.032 -6083.032 7265.968 91717.968 2095.968 -14186.417
## 25 26 27 28 29 30
## 6805.968 -63406.032 -45124.032 -8504.417 -11362.032 58288.968
## 31 32 33 34 35 36
## -63433.032 -132507.417 17426.968 106880.968 47305.968 -56026.032
## 37 38 39 40 41 42
## -87553.032 -72134.032 -54188.032 -52405.032 -127991.032 -11246.032
## 43 44 45 46 47 48
## 61914.968 164462.968 134830.968 40461.968 112534.968 8850.968
## 49 50 51 52 53 54
## -80042.032 -30527.417 -37974.032 -106117.032 89699.968 -115526.032
## 55 56 57 58 59 60
## -127646.032 -92305.417 -153138.032 -47717.032 27750.968 80752.968
## 61 62 63 64 65 66
## -98272.032 -78899.417 -36282.032 -54161.032 26322.968 -129260.032
## 67 68 69 70 71 72
## 86748.968 -38979.032 100074.968 -27337.417 -76789.032 -78546.032
## 73 74 75
## 3874.968 237457.583 21507.968summary(fit)##
## Call:
## lm(formula = Profit ~ Hours24, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153138 -64315 -11246 52884 237458
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 281540 25976 10.84 <2e-16 ***
## Hours24 -6222 28343 -0.22 0.827
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89980 on 73 degrees of freedom
## Multiple R-squared: 0.0006598, Adjusted R-squared: -0.01303
## F-statistic: 0.0482 on 1 and 73 DF, p-value: 0.8268fit<-lm(Profit~Visibility,data = store)
summary(fit)##
## Call:
## lm(formula = Profit ~ Visibility, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152838 -63359 -10946 43839 243980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226431 43855 5.163 2.02e-06 ***
## Visibility 16196 13840 1.170 0.246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89180 on 73 degrees of freedom
## Multiple R-squared: 0.01841, Adjusted R-squared: 0.004966
## F-statistic: 1.369 on 1 and 73 DF, p-value: 0.2457fit$coefficients## (Intercept) Visibility
## 226430.94 16195.67confint(fit)## 2.5 % 97.5 %
## (Intercept) 139028.99 313832.90
## Visibility -11388.14 43779.49store$Profit## [1] 265014 424007 222735 210122 300480 469050 476355 361115 474725 278625
## [11] 389886 329020 152513 261571 203951 196277 265584 394039 261495 269235
## [21] 282584 367036 277414 267354 282124 211912 230194 273036 263956 333607
## [31] 211885 149033 292745 382199 322624 219292 187765 203184 221130 222913
## [41] 147327 264072 337233 439781 410149 315780 387853 284169 195276 251013
## [51] 237344 169201 365018 159792 147672 189235 122180 227601 303069 356071
## [61] 177046 202641 239036 221157 301641 146058 362067 236339 375393 254203
## [71] 198529 196772 279193 518998 296826fitted(fit)## 1 2 3 4 5 6 7 8
## 275018.0 291213.6 275018.0 291213.6 258822.3 275018.0 258822.3 291213.6
## 9 10 11 12 13 14 15 16
## 258822.3 291213.6 258822.3 291213.6 275018.0 291213.6 275018.0 275018.0
## 17 18 19 20 21 22 23 24
## 275018.0 307409.3 258822.3 275018.0 275018.0 291213.6 275018.0 258822.3
## 25 26 27 28 29 30 31 32
## 275018.0 275018.0 275018.0 275018.0 275018.0 275018.0 258822.3 275018.0
## 33 34 35 36 37 38 39 40
## 275018.0 291213.6 291213.6 275018.0 275018.0 275018.0 258822.3 275018.0
## 41 42 43 44 45 46 47 48
## 258822.3 275018.0 291213.6 275018.0 307409.3 291213.6 258822.3 275018.0
## 49 50 51 52 53 54 55 56
## 291213.6 275018.0 258822.3 275018.0 258822.3 275018.0 291213.6 258822.3
## 57 58 59 60 61 62 63 64
## 275018.0 291213.6 275018.0 275018.0 275018.0 275018.0 275018.0 291213.6
## 65 66 67 68 69 70 71 72
## 275018.0 258822.3 275018.0 291213.6 275018.0 275018.0 258822.3 258822.3
## 73 74 75
## 291213.6 275018.0 291213.6residuals(fit)## 1 2 3 4 5 6
## -10003.960 132793.368 -52282.960 -81091.632 41657.713 194032.040
## 7 8 9 10 11 12
## 217532.713 69901.368 215902.713 -12588.632 131063.713 37806.368
## 13 14 15 16 17 18
## -122504.960 -29642.632 -71066.960 -78740.960 -9433.960 86629.696
## 19 20 21 22 23 24
## 2672.713 -5782.960 7566.040 75822.368 2396.040 8531.713
## 25 26 27 28 29 30
## 7106.040 -63105.960 -44823.960 -1981.960 -11061.960 58589.040
## 31 32 33 34 35 36
## -46937.287 -125984.960 17727.040 90985.368 31410.368 -55725.960
## 37 38 39 40 41 42
## -87252.960 -71833.960 -37692.287 -52104.960 -111495.287 -10945.960
## 43 44 45 46 47 48
## 46019.368 164763.040 102739.696 24566.368 129030.713 9151.040
## 49 50 51 52 53 54
## -95937.632 -24004.960 -21478.287 -105816.960 106195.713 -115225.960
## 55 56 57 58 59 60
## -143541.632 -69587.287 -152837.960 -63612.632 28051.040 81053.040
## 61 62 63 64 65 66
## -97971.960 -72376.960 -35981.960 -70056.632 26623.040 -112764.287
## 67 68 69 70 71 72
## 87049.040 -54874.632 100375.040 -20814.960 -60293.287 -62050.287
## 73 74 75
## -12020.632 243980.040 5612.368summary(fit)##
## Call:
## lm(formula = Profit ~ Visibility, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152838 -63359 -10946 43839 243980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 226431 43855 5.163 2.02e-06 ***
## Visibility 16196 13840 1.170 0.246
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 89180 on 73 degrees of freedom
## Multiple R-squared: 0.01841, Adjusted R-squared: 0.004966
## F-statistic: 1.369 on 1 and 73 DF, p-value: 0.2457the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05)- 1.Ctenure (0.025) 2.Comp(0.003) 3.Pop(0.0001) 4.PedCount(0.034) In () p-value are indicated (Ans)
the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05)- 1.Mtenure(0.0552) 2.Res(0.1717) 3.Hours24(0.8268) 4.Visibility(0.2457) In () p-value are indicated (Ans)
The expected change in the Profit at a store, if the Manager’s tenure i.e. number of months of experience with Store24, increases by one month is 680.3475 units.(Ans)
The expected change in the Profit at a store, if the Crew’s tenure i.e. number of months of experience with Store24, increases by one month is 1301.739 units.(Ans)
Excecutive Report:- So,finally I come to an end of the data analysis of the data frame called store24.Here the variables used for the data frames are :- -store id, -Sales(Fiscal Year 2000 Sales), -profit(Fiscal Year 2000 Profit before corporate overhead allocations, rent, and depreciation), -Mtenure(Average manager tenure during FY-2000 where tenure is defined as the number of months of experience with Store24), -Ctenure(Average crew tenure during FY-2000 where tenure is defined as the number of months of experience with Store24), -Comp(Number of competitors per 10,000 people within a ½ mile radius), -Pop(Population within a ½ mile radius), -Visible(5-point rating on visibility of store front with 5 being the highest), -PedCount(5-point rating on pedestrian foot traffic volume with 5 being the highest), -Hours24(Indicator for open 24 hours or not) & -Res(Indicator for located in residential vs. industrial area).
Here we find the correlation co-efficient between (Profit,Mtenure) & (Profit,Ctenure) the values are 0.4388692 & 0.2576789 respectively.
The expected change in the Profit at a store, if the Manager’s tenure i.e. number of months of experience with Store24, increases by one month is 680.3475 units.(Ans)
The expected change in the Profit at a store, if the Crew’s tenure i.e. number of months of experience with Store24, increases by one month is 1301.739 units.(Ans)
Besides this,i have done run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}. by running in all cases,the first result i get these:-
the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05)- 1.Ctenure (0.025) 2.Comp(0.003) 3.Pop(0.0001) 4.PedCount(0.034) In () p-value are indicated (Ans)
the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05)- 1.Mtenure(0.0552) 2.Res(0.1717) 3.Hours24(0.8268) 4.Visibility(0.2457) In () p-value are indicated (Ans)
For regression in (Profit,Mtenure):- -adjusted R-squared i get 0.1815 -multiple R-squared i get 0.1926 -p-value: 8.193e-05
For regression in (Profit,Ctenure) -adjusted R-squared i get 0.05361 -multiple R-squared i get 0.0664 -p-value: 0.02562
For regression in (Profit,Comp) -adjusted R-squared i get 0.09975 -Multiple R-squared: 0.1119 -p-value: 0.003351
For regression in (Profit,Pop) -adjusted R-squared i get 0.1743 -multiple R-squared i get 0.1854 -p-value: 0.000115
For regression in (Profit,PedCount) -adjusted R-squared i get 0.1918 -multiple R-squared i get 0.2027 -p-value: 5.057e-05
For regression in (Profit,Res) -adjusted R-squared i get 0.01208 -multiple R-squared i get 0.02543 -p-value: 0.1717
For regression in (Profit,Hours24) -adjusted R-squared i get -0.01303 -Multiple R-squared: 0.0006598 -p-value: 0.8268
here the r-squared is negative i.e the corresponding variable doesn’t help at all in predicting the profit values.In real,it’s obvious because here Hours24 indicates whether the shop remains open for 24 hours or not.Obviously how ’ll it predict the the profit values For regression in (Profit,Visibility) adjusted R-squared i get 0.004966 multiple R-squared i get 0.01841 p-value: 0.2457
some of the cases i can clearly the fit is not very good due to poor values multiple R-squared values and p-values of F-Statistic.For some cases,although the r-sqaured value is not high but the p-value suggests that fit is good.
In all the cases,the adjusted R-sqaured values are not so good,the modulus of residual values of profit differs by a large margin becuase the profit values in all the stores are also large numbers. So i can say that it’s all overall not a good model to predict the profit values.(Ans)
Thank You!