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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(Age ~ Preference, data = mydata)) ## Warning in summary.lm(lm(Age ~ Preference, data = mydata)): essentially perfect
## fit: summary may be unreliable##
## Call:
## lm(formula = Age ~ Preference, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.831e-15 0.000e+00 0.000e+00 0.000e+00 1.831e-15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.000e+00 5.854e-16 5.125e+15 < 2e-16 ***
## Preference -1.831e-15 5.033e-16 -3.638e+00 0.00243 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.686e-16 on 15 degrees of freedom
## Multiple R-squared: 0.4848, Adjusted R-squared: 0.4505
## F-statistic: 14.12 on 1 and 15 DF, p-value: 0.001902#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.