Use the “summary” function to get summary statistics for all columns in the “mtcars” dataset.
Your final output should resemble the following:
# mpg cyl disp hp # Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 # 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5 # Median :19.20 Median :6.000 Median :196.3 Median :123.0 # Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 # 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0 # Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 # drat wt qsec vs # Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000 # 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 # Median :3.695 Median :3.325 Median :17.71 Median :0.0000 # Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375 # 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 # Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000 # am gear carb # Min. :0.0000 Min. :3.000 Min. :1.000 # 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000 # Median :0.0000 Median :4.000 Median :2.000 # Mean :0.4062 Mean :3.688 Mean :2.812 # 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000 # Max. :1.0000 Max. :5.000 Max. :8.000
Exercise: 16-A
Use the “lm” function to create a linear model using the “ChickWeight” dataset. Your model should predict the “weight” variable using the “Diet” and “Time” variables.
Name your linear model “lm” and then view a summary of your model using the “summary” function. The output of your summary should look like this:
# Call:# lm(formula = weight ~ Diet + Time, data = ChickWeight)# Residuals:# Min 1Q Median 3Q Max # -136.851 -17.151 -2.595 15.033 141.816 # Coefficients:# Estimate Std. Error t value Pr(>|t|) # (Intercept) 10.9244 3.3607 3.251 0.00122 ** # Diet2 16.1661 4.0858 3.957 8.56e-05 ***# Diet3 36.4994 4.0858 8.933 < 2e-16 ***# Diet4 30.2335 4.1075 7.361 6.39e-13 ***# Time 8.7505 0.2218 39.451 < 2e-16 ***# ---# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# Residual standard error: 35.99 on 573 degrees of freedom# Multiple R-squared: 0.7453, Adjusted R-squared: 0.7435 # F-statistic: 419.2 on 4 and 573 DF, p-value: < 2.2e-16
Exercise: 17-A
Create a density plot using the “Nile” dataset.
Answers
Answer: 15-A
Here’s how you can accomplish this task:
summary(mtcars)
mpg cyl disp hp
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
Median :19.20 Median :6.000 Median :196.3 Median :123.0
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
drat wt qsec vs
Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
Median :3.695 Median :3.325 Median :17.71 Median :0.0000
Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
am gear carb
Min. :0.0000 Min. :3.000 Min. :1.000
1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
Median :0.0000 Median :4.000 Median :2.000
Mean :0.4062 Mean :3.688 Mean :2.812
3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :1.0000 Max. :5.000 Max. :8.000
Answer: 16-A
You can create your model with the following code:
lm <-lm(weight ~ Diet + Time, data = ChickWeight)summary(lm)
Call:
lm(formula = weight ~ Diet + Time, data = ChickWeight)
Residuals:
Min 1Q Median 3Q Max
-136.851 -17.151 -2.595 15.033 141.816
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.9244 3.3607 3.251 0.00122 **
Diet2 16.1661 4.0858 3.957 8.56e-05 ***
Diet3 36.4994 4.0858 8.933 < 2e-16 ***
Diet4 30.2335 4.1075 7.361 6.39e-13 ***
Time 8.7505 0.2218 39.451 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35.99 on 573 degrees of freedom
Multiple R-squared: 0.7453, Adjusted R-squared: 0.7435
F-statistic: 419.2 on 4 and 573 DF, p-value: < 2.2e-16
Answer: 17-A
You can create your density plot with the following code:
