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    install.packages(“rmarkdown”)

    # set seed replace 12345678 with your student IDseed = 17069600
    # loads in data for the full populationpop<-read.csv("HW1.csv", head = TRUE)names(pop) <- c("X", "Y")
    # sets the seed for the random number generatorset.seed(seed)
    # assigns a "random" sample of 12 from the population to 'data'data<-pop[sample(nrow(pop), 12, replace=FALSE),]# use this datadata
    ##      X  Y## 658  9  8## 610  7  6## 794 10  7## 369 10  7## 381  8 10## 624  4  4## 188  8  6## 485  7  6## 914 11  7## 64  10  7## 654 10  8## 531  7  6
    # regressionmodel <- lm(Y ~ X, data=data)summary(model)
    ## ## Call:## lm(formula = Y ~ X, data = data)## ## Residuals:##     Min      1Q  Median      3Q     Max ## -0.9670 -0.5587 -0.3699 -0.0408  3.3495 ## ## Coefficients:##             Estimate Std. Error t value Pr(>|t|)  ## (Intercept)   3.1398     1.6342   1.921   0.0836 .## X             0.4388     0.1894   2.316   0.0430 *## ---## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## ## Residual standard error: 1.241 on 10 degrees of freedom## Multiple R-squared:  0.3492, Adjusted R-squared:  0.2841 ## F-statistic: 5.366 on 1 and 10 DF,  p-value: 0.04303
    # creates plotplot(data$X, data$Y, main=c(paste("Scatterplot")), xlim=c(0,15), ylim=c(0,15), xaxs = "i", yaxs = "i", xlab="X", ylab="Y")abline(lm(Y ~ X, data=data))

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