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Calories and SaltThe data set shown below gives
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</p> <p>The correlation between XXXXXX XXX Calories XX X.86 (X.XXXXXXX). </p> <p>XXX XXXXXXX plot shows that the XXX XXXX XXXX XXXX XXXXXXXX XXXX XXXX XXXX XXXXXX. XXXXXXXXX, with 1 XXXX increase in Calories, the amount XX XXXXXX will increase X.212 mg. XXXXX seems XX be XXX outlier (107, 144). </p> <p>The XXXXXXXXXXX XXXXXXXXXXX suggests that there is a strong XXXXXXXX XXXXXXXXXXX between calories XXX XXXXXX. XX it XXXXXXXX XX analysis. XXXXXXX, the p-XXXXX of the regression model XX X.04e-06, which XXXXXXXX XXXX XXXXXXXX significantly correlated XXXX XXXXXX. </p> <p>When the XXXXXXX XXX XXXXXXX, the model XXXXXXX </p> <p>y= XX.X+2.401x </p> <p>The XXX XXXXXXX plot XX </p> <p><img src="file:///X:\Users\uqzqiao1\XXXXXXX\XXXXX\Temp\XXXXXXXXXXXX\XX\clip_image004.jpg" width="XXX" height="541"> </p> <p>XXX XXXXXXXXXXX XXXXXXX XXXXXX and XXXXXXXX XXXXXXX 0.XX (0.8338989). </p> <p>The new plot XXXXX XXXXX that the XXX dogs with XXXX XXXXXXXX will XXXX more sodium. Generally, with X unit increase in XXXXXXXX, the amount of sodium will increase 2.XXX XX. </p> <p>The correlation coefficient still XXXXXXXX that there XX a XXXXXX positive XXXXXXXXXXX between calories and sodium. XXX XXX p-XXXXX XX XXX XXXXXXXXXX model XX X.96e-05, which still XXXXXXXX that calories significantly XXXXXXXXXX with sodium. So it XXXXX supports my XXXXXXXX. </p> <p>Our XXXXXXXX robustly XXXXXXXXX XXXX hot XXXX with more XXXXXXXX XXXX XXXX more sodium. XX other words, XXXXX XX a XXXXXX XXXXXXXXXXX XXXXXXX these XXX XXXXXXXXX. XXXXXXX, we XXXXXX reach the XXXXXXXXXX XXXX there is a causal XXXXXXXXXXXX XXXXXXX these two XXXXXXXXX. The XXXXXXXXXXX between them XXX result XXXX a spurious XXXXXXXXXXX XXXX a XXXXXXXXXXX/XXXXXX XXXXXXXX XXXXX correlated with these XXX variables, respectively. Besides, XXXX XXXXX XX a XXXXXX relationship XXXXXXX XXXXX XXX variables, XXX XXXXXXXXX XX hard to predict. </p> <p> </p> <p>########################R XXXX </p> <p>#Prepare data </p> <p>x=XXXX.table("data", header=F) </p> <p>XXXXXXXX(x)=c("Brand", "Calories", "Sodium") </p> <p>#Linear XXXXXXXXXX (XXX) </p> <p>XXXXX=XX(XXXXXX~Calories, data=x) </p> <p>#Scatter XXXX </p> <p>XXXX(x$XXXXXXXX, x$XXXXXX, XXX=XX, XXX = 1.3, col = "blue", </p> <p> XXXX = "SODIUM XXXXXXX XXXXXXXX", XXXX="Sodium (mg)", XXXX="Calories") </p> <p>abline(XXXXX) </p> <p>#XXXXXXXXXXX coefficient </p> <p>cor(x$Sodium, x$Calories) </p> <p> </p> <p># Remove XXXXXXX </p> <p>x2=x[-13,] </p> <p># XXXXX </p> <p>model=lm(Sodium~XXXXXXXX, XXXX=XX) </p> <p>#XXXXXXX XXXX </p> <p>XXXX(XX$XXXXXXXX, XX$XXXXXX, XXX=XX, XXX = X.X, col = "XXXX", </p> <p> XXXX = "XXXXXX XXXXXXX CALORIES", XXXX="XXXXXX (XX)", xlab="XXXXXXXX") </p> <p>abline(model) </p> <p>#XXXXXXXXXXX XXXXXXXXXXX </p> <p>XXX(x2$Sodium, x2$Calories) </p> <p></p>">
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