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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(branding ~social_media, data = mydata)) ##
## Call:
## lm(formula = branding ~ social_media, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1379 -0.5603 0.4397 0.8621 1.7069
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.8707 0.8521 3.369 0.00277 **
## social_media 0.4224 0.2589 1.632 0.11700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.138 on 22 degrees of freedom
## Multiple R-squared: 0.1079, Adjusted R-squared: 0.0674
## F-statistic: 2.662 on 1 and 22 DF, p-value: 0.117#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.