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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(CS_helpful ~. -ID, data = mydata)) ##
## Call:
## lm(formula = CS_helpful ~ . - ID, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39668 -0.14511 0.02472 0.14090 0.31831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.02492 0.60776 -1.686 0.130206
## Recommend 0.16584 0.13359 1.241 0.249616
## Come_again -0.08160 0.15428 -0.529 0.611218
## Product_needed 0.25910 0.08930 2.901 0.019850 *
## Profesionalism 0.55105 0.17158 3.212 0.012395 *
## Limitation 0.58813 0.10853 5.419 0.000632 ***
## Online_grocery 0.23562 0.10777 2.186 0.060268 .
## delivery 0.12848 0.12554 1.023 0.336046
## Pick_up -0.18384 0.09434 -1.949 0.087156 .
## Find_items -0.21428 0.14441 -1.484 0.176143
## other_shops -0.11142 0.06080 -1.832 0.104245
## Gender 0.43572 0.21782 2.000 0.080474 .
## Age 0.05329 0.12382 0.430 0.678304
## Education 0.02337 0.05384 0.434 0.675679
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3025 on 8 degrees of freedom
## Multiple R-squared: 0.9353, Adjusted R-squared: 0.8302
## F-statistic: 8.896 on 13 and 8 DF, p-value: 0.002149#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.