I only need f and g. This uses the common intro statistics file FORBES40. Best-p
ID: 3074641 • Letter: I
Question
I only need f and g. This uses the common intro statistics file FORBES40.
Best-paid CEOs. Forbes magazine conducts an annual survey of the salaries of chief executive officers. In addition to salary information Forbes collects and reports personal data on the CEOs. The data on 40 CEOs is saved in the FORBES40 file on the course web site. Do most CEOs have advanced degrees, such as master's degrees or doctorates? (a) Use the R software to construct a frequency table for the highest degree earned by these CEOs. (2%) construct the graphs. Do most CEOs have advanced degrees? (3%) trend? Explain. Again, use R to construct the graph. (2%) expect to find in the intervals x 0.75s, x 2.5s, x 4s? (2%) Compare your results with those of part d. (2%) the disribution of the age of the CEOs. (4%) CEOs. Interpret your results. (3%) (b) Use appropriate graphical methods to summarize the highest degree earned. Use R to (c) Use a graph to portray the relationship between a CEOs salary and age. Do you detect a (d) According to Chebyshev's rule, what percentage of the salary measurements would you (e) What percentage of the salary measurements actually fall into the intervals of part d? (f) Find the mean, quartiles and mode of the age of the CEOs. Comment on the skewness of (g) Use the R software to compute the 15th, 50th and 88th percentiles of the salaries of theExplanation / Answer
Solution:
I got the data as
Rcode to get mean is
import the file and create the dataframe
library(readxl)
FORBES40 <- read_excel("C:/Users/M1045151/Downloads/FORBES40.xls")
View(FORBES40)
dim(FORBES40)
mean(FORBES40$Age)
quantile(FORBES40$Age,probs=c(0,0.25,0.50,0.75,1))#to get quartiles
fivenum(FORBES40$Age)
Modeforage <- table(FORBES40$Age)
names(Modeforage)[which(Modeforage==max(Modeforage))]
library(e0171)
require(e1071)
skewness(FORBES40$Age)
ANSWERS:
Mean=59.025
mode=60
Quartiles
0% 25% 50% 75% 100%
43.00 55.75 59.50 63.00 72.00
min value=43
Q1=55.75
Q2=59.50
Q3=63
max=72
age is negatively skewed .
Solutione:
quantile(FORBES40$Salary, c(.15, .50, .88))
15% 50% 88%
29.2790 36.9800 94.1508
15 th percentile=29.270
50 th percentile=36.9800
88 th percentile= 94.1508
CEO Company Salary Age Degree 1 Fairbank CapitalOne 249.42 55 MBA 2 Semel Yahoo 230.55 63 MBA 3 Silverman Cendant 139.96 65 Law 4 Karatz KBHome 135.53 60 Law 5 Fuld LehmanBros 122.67 60 MBA 6 Irani OccidentalPetro 80.73 71 PhD 7 Ellison Oracle 75.33 61 None 8 Thompson Symantec 71.84 57 Masters 9 Crawford CaremarkRx 69.66 57 Bachelor 10 Mozilo Countrywide 68.96 67 Bachelor 11 Chambers CiscoSystems 62.99 56 MBA 12 Dreier RylandGroup 56.47 58 Bachelor 13 Frankfort Coach 55.99 60 MBA 14 Hovnanian HovnanianEnt 47.83 48 MBA 15 Drosdick Sunoco 46.19 62 Masters 16 Toll TollBrothers 41.31 65 Law 17 Ulrich Target 39.64 63 Bachelor 18 Rollins Dell 39.32 53 MBA 19 Cazalot MarathonOil 37.48 55 Bachelor 20 Novak YumBrands 37.42 53 Bachelor 21 Papa EOGResources 36.54 59 MBA 22 Termeer Genzyme 36.38 60 MBA 23 Adkerson FreeportCopper 35.41 59 MBA 24 Sharer Amgen 34.49 58 Masters 25 Sugarman IStar 32.94 43 MBA 26 David UnitedTech 32.73 64 MBA 27 Simpson XTOEnergy 32.19 57 MBA 28 Lanni MGMMirage 31.54 63 MBA 29 Jacobs Qualcomm 31.44 64 PhD 30 Bollenbach HiltonHotels 31.43 63 MBA 31 Mulva ConocoPhillips 31.34 59 MBA 32 Mack MorganStanley 31.23 61 Bachelor 33 Williams Aetna 30.87 57 Masters 34 Lesar Halliburton 29.36 53 MBA 35 Hanway Cigna 28.82 54 MBA 36 Cayne BearStearns 28.4 72 None 37 Amos Aflac 27.97 54 Bachelor 38 Thiry DaVita 27.89 50 MBA 39 Rowe Exelon 26.9 60 Law 40 Cornelius Guidant 25.18 62 MBA