NEGOCIOS Y MARKETING

Este analisis consta de 101 observaciones de impacto donde muestran la inversion, conversiones e impacto.

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
datos <- read.csv("datos_negocios_extra_3_marketing (1).csv")
head(datos)
##        Estrategia Inversion Conversiones Impacto
## 1             SEO  24425.76          489    0.23
## 2             SEO  38382.77           99    0.30
## 3  Redes Sociales  26007.83          262    0.59
## 4 Email Marketing  48328.58          429    0.75
## 5   Publicidad TV  16902.05          437    0.74
## 6   Publicidad TV  35587.91          236    0.70
summary(datos)
##   Estrategia          Inversion      Conversiones      Impacto      
##  Length:100         Min.   : 5062   Min.   : 51.0   Min.   :0.1100  
##  Class :character   1st Qu.:18409   1st Qu.:155.5   1st Qu.:0.3000  
##  Mode  :character   Median :28834   Median :270.5   Median :0.5300  
##                     Mean   :28582   Mean   :272.3   Mean   :0.5377  
##                     3rd Qu.:40271   3rd Qu.:394.2   3rd Qu.:0.7325  
##                     Max.   :49850   Max.   :496.0   Max.   :0.9800
library(ggplot2)
ggplot(datos, aes(x = Impacto, y = Inversion, fill = Impacto)) +
  geom_boxplot() +
  labs(title = "Distribución de tipo de impacto",
       x = "Impacto",
       y = "Inversion") +
  theme_minimal()
## Warning: Orientation is not uniquely specified when both the x and y aesthetics are
## continuous. Picking default orientation 'x'.
## Warning: Continuous x aesthetic
## ℹ did you forget `aes(group = ...)`?
## Warning: The following aesthetics were dropped during statistical transformation: fill.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

Este gráfico muestra la variabilidad en la inversion de cada impacto Se pueden observar diferencias en la mediana y la dispersión de los datos.

ggplot(datos, aes(x = Impacto)) +
  geom_histogram(binwidth = 5, fill = "skyblue", color = "black", alpha = 0.7) +
  geom_density(aes(y = ..density.. * 5), color = "blue", size = 1) +
  labs(title = "Distribución de tipo de estrategia",
       x = "Impacto",
       y = "inversion") +
  theme_minimal()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Intervalo de confianza para la media

Interpretar el intervalo obtenido y evaluar si es posible hacer inferencias sobre el total de sucursales.

Estrategia_SEO <- filter(datos, datos$Estrategia == "SEO")
Estrategia_SEO
##    Estrategia Inversion Conversiones Impacto
## 1         SEO  24425.76          489    0.23
## 2         SEO  38382.77           99    0.30
## 3         SEO  29693.40          140    0.76
## 4         SEO  14362.31          473    0.62
## 5         SEO  26889.22          163    0.95
## 6         SEO  49276.06          222    0.26
## 7         SEO  40056.37          222    0.34
## 8         SEO  28337.71          132    0.26
## 9         SEO   7317.87          160    0.73
## 10        SEO  36189.79          457    0.26
## 11        SEO  12253.26          216    0.73
## 12        SEO  11577.62          157    0.48
## 13        SEO  41682.41          414    0.66
## 14        SEO  18633.47          270    0.47
## 15        SEO  23332.85          490    0.19
## 16        SEO  20078.20           99    0.87
## 17        SEO   7148.64          218    0.57
## 18        SEO  49084.62          273    0.13
## 19        SEO  10549.41           67    0.62
## 20        SEO  13474.85          398    0.44
## 21        SEO  12904.20          375    0.20
media_Estrategia_SEO <- mean(Estrategia_SEO$Impacto)  
sd_Estrategia_SEO <- sd(Estrategia_SEO$Impacto)  
n_A <- nrow(Estrategia_SEO)  
error_media_A <- qt(0.975, df = n_A - 1) * sd_Estrategia_SEO / sqrt(n_A)  


IC_media_A <- c(media_Estrategia_SEO - error_media_A, media_Estrategia_SEO + error_media_A)
cat("Media de Impacto:", media_Estrategia_SEO, "\n")
## Media de Impacto: 0.4795238
cat("Intervalo de confianza para la media Estrategia SEO de los Impactos mensuales en Estrategia SEO:", media_Estrategia_SEO, "\n")
## Intervalo de confianza para la media Estrategia SEO de los Impactos mensuales en Estrategia SEO: 0.4795238