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p4exercises.qmd
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p4exercises.qmd
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# Exercises {.unnumbered}
## Questions
::: callout
## Exercise: 15-A
Use the "summary" function to get summary statistics for all columns in the "mtcars" dataset.
Your final output should resemble the following:
``` r
# 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
```
:::
::: callout
## 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:
``` r
# 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
```
:::
::: callout
## Exercise: 17-A
Create a density plot using the "Nile" dataset.
:::
## Answers
::: callout
## Answer: 15-A
Here's how you can accomplish this task:
```{r}
summary(mtcars)
```
:::
::: callout
## Answer: 16-A
You can create your model with the following code:
```{r}
lm <- lm(weight ~ Diet + Time, data = ChickWeight)
summary(lm)
```
:::
::: callout
## Answer: 17-A
You can create your density plot with the following code:
```{r}
plot(density(Nile))
```
:::