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mydata <- read.csv("customer_segmentation.csv")
# we read the dataset using the read.csv function.
# we saved our original data as customer_segmentation.csv
# I suggest that you use the same document name
summary(lm( Celebrities_BrandPromotion ~ Social_MediaP +Brand_popularity + Fashion + factor(Gender), data = mydata)) ##
## Call:
## lm(formula = Celebrities_BrandPromotion ~ Social_MediaP + Brand_popularity +
## Fashion + factor(Gender), data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9768 -1.9192 -0.8513 1.8238 4.6849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5735 1.8978 1.883 0.0709 .
## Social_MediaP -0.2389 0.3080 -0.776 0.4448
## Brand_popularity 0.4044 0.3926 1.030 0.3125
## Fashion -0.4256 0.5030 -0.846 0.4052
## factor(Gender)2 1.3281 0.8957 1.483 0.1502
## factor(Gender)3 -1.1496 2.4493 -0.469 0.6427
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.315 on 26 degrees of freedom
## Multiple R-squared: 0.1652, Adjusted R-squared: 0.004644
## F-statistic: 1.029 on 5 and 26 DF, p-value: 0.4214#we use CS_helpful as a dependent variable and all other variables except ID as predictors.
# think about the dependent variables you will be using. This may require a little bit domain
#specific knowledge in marketing.