Publications by Kunal Dolas
Homework6_2
Using the fat data, fit the model described in section 4.2 of the Faraway text using the following methods: Least squares, Least absolute deviations, and Huber method. require(faraway) ## Loading required package: faraway data(fat, package="faraway") head(fat) ## brozek siri density age weight height adipos free neck chest abdom hip ## 1 1...
1341 sym R (11004 sym/27 pcs) 1 img
homework5_1_kd
Using the cheddar data, fit a linear model with taste as the response and the three other variables as predictors. require(faraway) ## Loading required package: faraway data(cheddar, package="faraway") head(cheddar) ## taste Acetic H2S Lactic ## 1 12.3 4.543 3.135 0.86 ## 2 20.9 5.159 5.043 1.53 ## 3 39.0 5.366 5.438 1.57 ## 4 4...
948 sym R (3016 sym/18 pcs)
homework5_2_kd
library(faraway) data("faithful", package = "faraway") ## Warning in data("faithful", package = "faraway"): data set 'faithful' not found head(faithful) ## eruptions waiting ## 1 3.600 79 ## 2 1.800 54 ## 3 3.333 74 ## 4 2.283 62 ## 5 4.533 85 ## 6 2.883 55 lmod<-lm(eruptions~waiting, data=f...
6 sym R (967 sym/11 pcs) 1 img
HW4_1
For the swiss data (?swiss for documentation), fit a model with Fertility as the response and the other variables as predictors. Perform regression diagnostics on this model to answer the following questions. Display any plots that are relevant. Do not provide any plots about which you have nothing to say. Suggest possible improvements or correct...
2000 sym R (12109 sym/72 pcs) 24 img
homework5_3_kd
For the prostate data, fit a model with lpsa as the response and the other variables as predictors. library(faraway) data("prostate", package = "faraway") head(prostate) ## lcavol lweight age lbph svi lcp gleason pgg45 lpsa ## 1 -0.5798185 2.7695 50 -1.386294 0 -1.38629 6 0 -0.43078 ## 2 -0.9942523 3.3196 58 -...
1302 sym R (17265 sym/24 pcs)
Homework6_1
Using the divusa data, fit a regression model with divorce as the response and unemployed, femlab, marriage, birth, and military as predictors. require(faraway) ## Loading required package: faraway data(divusa, package="faraway") head(divusa) ## year divorce unemployed femlab marriage birth military ## 1 1920 8.0 5.2 22.70 92.0 ...
1200 sym R (4208 sym/25 pcs) 2 img
Homework8_2
2.Take the fat data, and use the percentage of body fat, siri, as the response and the other variables, except brozek and density, as potential predictors. Remove every tenth observation from the data for use as a test sample. Use the remaining data as a training sample building the following models: (a) Linear regression with all predictors requ...
769 sym R (76251 sym/58 pcs) 5 img
Homework7_1
The aatemp data come from the U.S. Historical Climatology Network. They are the annual mean temperatures (in degrees F) in Ann Arbor, Michigan going back about 150 years. Is there a linear trend? library(faraway) data(aatemp) aatemp ## year temp ## 1 1854 49.15 ## 2 1855 46.52 ## 3 1871 48.80 ## 4 1881 47.95 ## 5 1882 47.31 ## ...
1859 sym R (15620 sym/49 pcs) 17 img
Homework7_2
Use the pressure data to fit a model with pressure as the response and temperature as the predictor. Use the Box-Cox method to determine the best transformation on the response. library(faraway) data(pressure) plot(pressure$temperature, pressure$pressure) lmodt <- lm(pressure ~ temperature, data=pressure) library(MASS) boxcox(lmodt, plotit=T)...
318 sym R (1168 sym/7 pcs) 5 img
Homework8_1
Use the seatpos data with hipcenter as the response. Fit a model with all eight predictors. Comment on the effect of leg length on the response. library(faraway) data(seatpos, package="faraway") head(seatpos) ## Age Weight HtShoes Ht Seated Arm Thigh Leg hipcenter ## 1 46 180 187.2 184.9 95.2 36.1 45.3 41.3 -206.300 ## 2 31 ...
1223 sym R (13116 sym/39 pcs) 2 img