Sesión 4.1
Preparación
library(stargazer)
library(officer)
library(flextable)
library(haven)
library(Hmisc)
library(jtools)
Model 1
bdatos1 <- read_dta("base_datos_1.dta")
modelo1 <- lm(ventas ~ gasto_marketing, data = bdatos1)
summ(modelo1)
|
Observations
|
70
|
|
Dependent variable
|
ventas
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
22.46
|
|
R²
|
0.25
|
|
Adj. R²
|
0.24
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
19015.77
|
2374.35
|
8.01
|
0.00
|
|
gasto_marketing
|
2.23
|
0.47
|
4.74
|
0.00
|
|
Standard errors: OLS
|
Model 2
bdatos2 <- read_dta("base_datos_2.dta")
modelo2 <- lm(beneficio ~ costos_produccion, data = bdatos2)
summ(modelo2)
|
Observations
|
70
|
|
Dependent variable
|
beneficio
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
238.48
|
|
R²
|
0.78
|
|
Adj. R²
|
0.77
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
47224.45
|
1308.57
|
36.09
|
0.00
|
|
costos_produccion
|
-1.31
|
0.08
|
-15.44
|
0.00
|
|
Standard errors: OLS
|
Model 3
bdatos3 <- read_dta("base_datos_3.dta")
modelo3 <- lm(satisfaccion_cliente ~ calidad_producto, data = bdatos3)
summ(modelo3)
|
Observations
|
70
|
|
Dependent variable
|
satisfaccion_cliente
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
439.46
|
|
R²
|
0.87
|
|
Adj. R²
|
0.86
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
52.89
|
3.82
|
13.86
|
0.00
|
|
calidad_producto
|
9.71
|
0.46
|
20.96
|
0.00
|
|
Standard errors: OLS
|
Model 4
bdatos4 <- read_dta("base_datos_4.dta")
modelo4 <- lm(productividad ~ horas_capacitacion, data = bdatos4)
summ(modelo4)
|
Observations
|
70
|
|
Dependent variable
|
productividad
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
29.57
|
|
R²
|
0.30
|
|
Adj. R²
|
0.29
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
66.36
|
5.36
|
12.38
|
0.00
|
|
horas_capacitacion
|
0.96
|
0.18
|
5.44
|
0.00
|
|
Standard errors: OLS
|
Model 5
bdatos5 <- read_dta("base_datos_5.dta")
modelo5 <- lm(ingresos ~ gasto_publicidad, data = bdatos5)
summ(modelo5)
|
Observations
|
70
|
|
Dependent variable
|
ingresos
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
124.84
|
|
R²
|
0.65
|
|
Adj. R²
|
0.64
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
39220.11
|
1648.53
|
23.79
|
0.00
|
|
gasto_publicidad
|
1.88
|
0.17
|
11.17
|
0.00
|
|
Standard errors: OLS
|
Model 6
bdatos6 <- read_dta("base_datos_6.dta")
modelo6 <- lm(participacion_mercado ~ precio_producto, data = bdatos6)
summ(modelo6)
|
Observations
|
70
|
|
Dependent variable
|
participacion_mercado
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
73.23
|
|
R²
|
0.52
|
|
Adj. R²
|
0.51
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
487.49
|
24.10
|
20.23
|
0.00
|
|
precio_producto
|
-3.90
|
0.46
|
-8.56
|
0.00
|
|
Standard errors: OLS
|
Model 7
bdatos7 <- read_dta("base_datos_7.dta")
modelo7 <- lm(retorno_inversion ~ inversion_investigacion, data = bdatos7)
summ(modelo7)
|
Observations
|
70
|
|
Dependent variable
|
retorno_inversion
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
125.88
|
|
R²
|
0.65
|
|
Adj. R²
|
0.64
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
30622.00
|
1860.26
|
16.46
|
0.00
|
|
inversion_investigacion
|
0.10
|
0.01
|
11.22
|
0.00
|
|
Standard errors: OLS
|
Model 8
bdatos8 <- read_dta("base_datos_8.dta")
modelo8 <- lm(costo_adquisicion ~ gasto_publicidad_adquisicion, data = bdatos8)
summ(modelo8)
|
Observations
|
70
|
|
Dependent variable
|
costo_adquisicion
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
52.07
|
|
R²
|
0.43
|
|
Adj. R²
|
0.43
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
1661.16
|
164.93
|
10.07
|
0.00
|
|
gasto_publicidad_adquisicion
|
-0.58
|
0.08
|
-7.22
|
0.00
|
|
Standard errors: OLS
|
Model 9
bdatos9 <- read_dta("base_datos_9.dta")
modelo9 <- lm(tasa_conversion ~ experiencia_usuario, data = bdatos9)
summ(modelo9)
|
Observations
|
70
|
|
Dependent variable
|
tasa_conversion
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
555.71
|
|
R²
|
0.89
|
|
Adj. R²
|
0.89
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
-0.17
|
0.12
|
-1.45
|
0.15
|
|
experiencia_usuario
|
0.54
|
0.02
|
23.57
|
0.00
|
|
Standard errors: OLS
|
Model 10
bdatos10 <- read_dta("base_datos_10.dta")
modelo10 <- lm(crecimiento_ventas ~ estrategia_precios, data = bdatos10)
summ(modelo10)
|
Observations
|
70
|
|
Dependent variable
|
crecimiento_ventas
|
|
Type
|
OLS linear regression
|
|
F(1,68)
|
32.52
|
|
R²
|
0.32
|
|
Adj. R²
|
0.31
|
|
|
Est.
|
S.E.
|
t val.
|
p
|
|
(Intercept)
|
5002.33
|
179.45
|
27.88
|
0.00
|
|
estrategia_precios
|
9.93
|
1.74
|
5.70
|
0.00
|
|
Standard errors: OLS
|