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Calories and SaltThe data set shown below gives
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</p> <p>The XXXXXXXXXXX XXXXXXX Sodium XXX XXXXXXXX is X.86 (X.XXXXXXX). </p> <p>The XXXXXXX plot XXXXX that the hot XXXX with XXXX XXXXXXXX XXXX XXXX more XXXXXX. XXXXXXXXX, with X unit increase in Calories, XXX XXXXXX of sodium XXXX increase X.212 XX. XXXXX seems to XX XXX XXXXXXX (107, 144). </p> <p>The correlation XXXXXXXXXXX suggests XXXX XXXXX XX a XXXXXX positive correlation XXXXXXX calories XXX XXXXXX. XX it XXXXXXXX XX analysis. XXXXXXX, the p-value XX XXX XXXXXXXXXX model XX X.XXX-06, which XXXXXXXX XXXX XXXXXXXX significantly XXXXXXXXXX with XXXXXX. </p> <p>XXXX XXX outlier was removed, XXX model becomes </p> <p>y= 46.X+2.XXXX </p> <p>The XXX scatter XXXX is </p> <p><img XXX="XXXX:///C:\Users\uqzqiao1\XXXXXXX\Local\Temp\msohtmlclip1\XX\XXXXXXXXXXXXX.jpg" width="XXX" XXXXXX="541"> </p> <p>The correlation XXXXXXX Sodium and Calories XXXXXXX 0.83 (X.8338989). </p> <p>XXX new XXXX XXXXX XXXXX that the XXX XXXX XXXX XXXX XXXXXXXX will have more sodium. XXXXXXXXX, with 1 XXXX XXXXXXXX in Calories, the XXXXXX of sodium XXXX XXXXXXXX 2.401 XX. </p> <p>The XXXXXXXXXXX coefficient still suggests that XXXXX XX a strong XXXXXXXX XXXXXXXXXXX between XXXXXXXX and XXXXXX. XXX new p-XXXXX of the regression model is X.96e-XX, which XXXXX suggests XXXX XXXXXXXX significantly correlated XXXX XXXXXX. So it XXXXX supports my analysis. </p> <p>XXX analyses XXXXXXXX suggested XXXX XXX dogs with more calories will XXXX XXXX XXXXXX. XX other words, XXXXX is a XXXXXX correlation between XXXXX two variables. However, we cannot reach XXX conclusion XXXX there is a XXXXXX XXXXXXXXXXXX XXXXXXX these two XXXXXXXXX. The correlation XXXXXXX XXXX can XXXXXX XXXX a XXXXXXXX correlation XXXX a confounding/latent XXXXXXXX which correlated with XXXXX two XXXXXXXXX, respectively. Besides, XXXX XXXXX is a XXXXXX XXXXXXXXXXXX XXXXXXX these two XXXXXXXXX, the direction is XXXX to XXXXXXX. </p> <p> </p> <p>########################X XXXX </p> <p>#XXXXXXX data </p> <p>x=read.table("data", XXXXXX=F) </p> <p>XXXXXXXX(x)=c("Brand", "Calories", "Sodium") </p> <p>#XXXXXX XXXXXXXXXX (XXX) </p> <p>model=lm(XXXXXX~Calories, XXXX=x) </p> <p>#XXXXXXX XXXX </p> <p>plot(x$Calories, x$Sodium, pch=16, cex = 1.3, col = "blue", </p> <p> main = "SODIUM AGAINST CALORIES", XXXX="XXXXXX (mg)", XXXX="XXXXXXXX") </p> <p>abline(model) </p> <p>#XXXXXXXXXXX XXXXXXXXXXX </p> <p>cor(x$Sodium, x$XXXXXXXX) </p> <p> </p> <p># XXXXXX outlier </p> <p>x2=x[-XX,] </p> <p># Model </p> <p>model=lm(Sodium~XXXXXXXX, data=XX) </p> <p>#Scatter plot </p> <p>plot(x2$XXXXXXXX, x2$XXXXXX, pch=XX, XXX = 1.3, col = "XXXX", </p> <p> XXXX = "SODIUM XXXXXXX CALORIES", XXXX="XXXXXX (mg)", XXXX="XXXXXXXX") </p> <p>XXXXXX(XXXXX) </p> <p>#XXXXXXXXXXX coefficient </p> <p>cor(x2$Sodium, XX$XXXXXXXX) </p> <p></p>">
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