Reading and preparing data

df<- read.csv("StockIndexPrice.csv")
dim(df)
## [1] 24  5
head(df)
##   ï..Year Month Iterest_Rate Unemplyment_Rate Stock_Index_Price
## 1    2017    12         2.75              5.3              1464
## 2    2017    11         2.50              5.3              1394
## 3    2017    10         2.50              5.3              1357
## 4    2017     9         2.50              5.3              1293
## 5    2017     8         2.50              5.4              1256
## 6    2017     7         2.50              5.6              1254
names (df)<- c("Year", "Month", "IR", "UR", "SIP")
## Linear regression - single variate
lm1<- lm(df$SIP ~ df$IR)
summary(lm1)
## 
## Call:
## lm(formula = df$SIP ~ df$IR)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -183.892  -30.181    4.455   56.608  101.057 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -99.46      95.21  -1.045    0.308    
## df$IR         564.20      45.32  12.450 1.95e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 75.96 on 22 degrees of freedom
## Multiple R-squared:  0.8757, Adjusted R-squared:  0.8701 
## F-statistic:   155 on 1 and 22 DF,  p-value: 1.954e-11
plot(df$IR, df$SIP)
abline(lm1, col= "blue")

Linear regression - single variate

lm2<- lm(df$SIP ~ df$UR)
summary(lm2)
## 
## Call:
## lm(formula = df$SIP ~ df$UR)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -159.671  -41.996    2.089   72.381  151.226 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   4471.3      304.2    14.7 7.41e-13 ***
## df$UR         -589.0       52.6   -11.2 1.49e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 83.25 on 22 degrees of freedom
## Multiple R-squared:  0.8507, Adjusted R-squared:  0.8439 
## F-statistic: 125.4 on 1 and 22 DF,  p-value: 1.487e-10
plot(df$UR, df$SIP)
abline(lm2, col= "red")

Multi-variate linear regression

lm3<- lm(formula = df$SIP ~ df$IR + df$UR)
summary(lm3)
## 
## Call:
## lm(formula = df$SIP ~ df$IR + df$UR)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -158.205  -41.667   -6.248   57.741  118.810 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   1798.4      899.2   2.000  0.05861 . 
## df$IR          345.5      111.4   3.103  0.00539 **
## df$UR         -250.1      117.9  -2.121  0.04601 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 70.56 on 21 degrees of freedom
## Multiple R-squared:  0.8976, Adjusted R-squared:  0.8879 
## F-statistic: 92.07 on 2 and 21 DF,  p-value: 4.043e-11

###Resources Linear regression http://r-statistics.co/Linear-Regression.html