Usine Emp Data CSV file to predict salary
emp_data <- read.csv("C:/Users/Pawan Srivastav/Desktop/Data Science/Data Sets/Data Sets/Simple Linear Regression/emp_data.csv") #Importing the data from local drive
View(emp_data) # view the dataSummary of Data
summary(emp_data) #Getting summary of the data (1st moment business decision)## Salary_hike Churn_out_rate
## Min. :1580 Min. :60.00
## 1st Qu.:1618 1st Qu.:65.75
## Median :1675 Median :71.00
## Mean :1689 Mean :72.90
## 3rd Qu.:1724 3rd Qu.:78.75
## Max. :1870 Max. :92.00Bulding a Linear Regression Model
colnames(emp_data) # Getting the column names## [1] "Salary_hike" "Churn_out_rate"emp_model <- lm(Salary_hike ~ Churn_out_rate, data=emp_data) # Create Simple linear model
summary(emp_model) # Getting summary of linear model##
## Call:
## lm(formula = Salary_hike ~ Churn_out_rate, data = emp_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.97 -23.13 -21.41 19.24 75.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2285.365 95.912 23.828 1.02e-08 ***
## Churn_out_rate -8.186 1.304 -6.277 0.000239 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 40.13 on 8 degrees of freedom
## Multiple R-squared: 0.8312, Adjusted R-squared: 0.8101
## F-statistic: 39.4 on 1 and 8 DF, p-value: 0.0002386