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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(buy_solar ~. -ID, data = mydata)) ##
## Call:
## lm(formula = buy_solar ~ . - ID, data = mydata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3275 -0.2455 0.1719 0.4496 0.6241
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.20604 2.17486 3.773 0.00544 **
## other_electric 0.07421 0.19094 0.389 0.70769
## Hyundai -0.16674 0.31425 -0.531 0.61011
## Toyota 0.14751 0.33077 0.446 0.66745
## Tesla -0.40455 0.35105 -1.152 0.28243
## convert_solar -1.55498 0.44396 -3.503 0.00805 **
## younger_consumers -0.72646 0.31951 -2.274 0.05259 .
## middleage_consumers -0.01925 0.33297 -0.058 0.95532
## older_consumers NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.776 on 8 degrees of freedom
## Multiple R-squared: 0.7456, Adjusted R-squared: 0.523
## F-statistic: 3.349 on 7 and 8 DF, p-value: 0.05593#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.