Computer Data set
sales <- read.csv("D://Users//jayapate//Downloads//Computer_Data.csv")
attach(sales)
plot(sales)
sales1 <- sales[-c(1,7,8,9)]
View(sales1)
plot(sales1)
#To check the correlation of Sales
cor(sales1)
## price speed hd ram screen
## price 1.00000000 0.3009765 0.4302578 0.6227482 0.29604147
## speed 0.30097646 1.0000000 0.3723041 0.2347605 0.18907412
## hd 0.43025779 0.3723041 1.0000000 0.7777263 0.23280153
## ram 0.62274824 0.2347605 0.7777263 1.0000000 0.20895374
## screen 0.29604147 0.1890741 0.2328015 0.2089537 1.00000000
## ads 0.05454047 -0.2152321 -0.3232220 -0.1816697 -0.09391943
## trend -0.19998694 0.4054383 0.5777901 0.2768438 0.18861444
## ads trend
## price 0.05454047 -0.1999869
## speed -0.21523206 0.4054383
## hd -0.32322200 0.5777901
## ram -0.18166971 0.2768438
## screen -0.09391943 0.1886144
## ads 1.00000000 -0.3185525
## trend -0.31855251 1.0000000
colnames(sales1)
## [1] "price" "speed" "hd" "ram" "screen" "ads" "trend"
#Multi Linear Model
model1 <- lm(price~speed+hd+ram+screen+ads+trend)
summary(model1)
##
## Call:
## lm(formula = price ~ speed + hd + ram + screen + ads + trend)
##
## Residuals:
## Min 1Q Median 3Q Max
## -929.36 -191.78 -32.22 134.49 1949.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -246.67547 66.37087 -3.717 0.000204 ***
## speed 8.89391 0.20883 42.590 < 2e-16 ***
## hd 0.70882 0.03091 22.932 < 2e-16 ***
## ram 47.38704 1.18767 39.899 < 2e-16 ***
## screen 126.70240 4.52146 28.022 < 2e-16 ***
## ads 0.96969 0.05671 17.099 < 2e-16 ***
## trend -47.08197 0.67588 -69.660 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 311.7 on 6252 degrees of freedom
## Multiple R-squared: 0.7123, Adjusted R-squared: 0.712
## F-statistic: 2580 on 6 and 6252 DF, p-value: < 2.2e-16