STA258 Week 1 Practice Questions
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STA258 Week 1 Practice Questions
1. Tobin's Q is a corporate financial measure. It is defined as
Q 
ℎ
ℎ
′ 
ℎ
ℎ
The file tobinQ.csv contains Tobin’s Q for a sample of 50 American corporations.
a. Read the data into RStudio.
b. Use R to obtain summary statistics of the data.
c. Plot a histogram of the data.
d. Plot a boxplot of the data.
e. Plot a QQ-plot of the data.
f. Is the distribution skewed or symmetric? If it’s skewed is it skewed to the left or the right?
g. Does it appear that this data could have come from a normal distribution?
2. Henry Cavendish was a British scientist. In 1798 he was the first experiment to measure the force of gravity between masses, and the first to yield accurate values for the gravitational constant (6.67300 × 10-11 m3 kg-1 s-2). In his work he made 29 measures of the density ofthe earth.
The file density.csv contains Cavendish’s 29 measures of the earth’s density.
a. Read the data into RStudio.
b. Use R to obtain summary statistics of the data.
c. Plot a histogram of the data.
d. Plot a boxplot of the data.
e. Plot a QQ-plot of the data.
f. Is the distribution skewed or symmetric? If it’s skewed is it skewed to the left or the right?
g. Does it appear that this data could have come from a normal distribution?
3. The Organisation of Economic Cooperation and Development (OECD) gathers data from OECD countries to promote policies that aims to improve the economic and social well-being of people around the world. The “Social Protection and Well-being” in the OECD program, which includes a yearly collection of data “Better Life Index (BLI). Educational Attainment in BLI (2017) measured the percentage of adults aged 25 to 64 holding at least an upper secondary degree over the same age as defined by ISCED Classification (International Standard Classification of Education).
The file EduAttain.csv contains percent educational attainment for 35 OECD countries.
a. Read the data into RStudio.
b. Use R to obtain summary statistics of the data.
c. Plot a histogram of the data.
d. Plot a boxplot of the data.
e. Plot a QQ-plot of the data.
f. Is the distribution skewed or symmetric? If it’s skewed is it skewed to the left or the right?
g. Does it appear that this data could have come from a normal distribution?
4. Generate a random sample of n=60 from a N(3, 42) distribution. This is how to do this:
rnorm(60,3,4)
a. Use R to obtain summary statistics of the data.
b. Plot a histogram of the data.
c. Plot a boxplot of the data.
d. Plot a QQ-plot of the data.
e. Is the distribution skewed or symmetric? If it’s skewed is it skewed to the left or the right?
f. Would you conclude that your sample could come from a normal distribution? Note: we know it’s a normal sample, but the plots may or may not reflect this.
5. Generate a random sample of n=40 from a gamma (
= 3, 
= 4) distribution. This is
how to do this:
rgamma(40,3,4)
a. Use R to obtain summary statistics of the data.
b. Plot a histogram of the data.
c. Plot a boxplot of the data.
d. Plot a QQ-plot of the data.
e. Is the distribution skewed or symmetric? If it’s skewed is it skewed to the left or the right?
f. Would you conclude that your sample could come from a normal distribution? Note: we know it’s not a normal sample, but the plots may or may not reflect this.
6. Generate a random sample of n=80 from a uniform (
= -3, 
= 4) distribution. This is
how to do this:
runif(80,-3,4)
a. Use R to obtain summary statistics of the data.
b. Plot a histogram of the data.
c. Plot a boxplot of the data.
d. Plot a QQ-plot of the data.
e. Is the distribution skewed or symmetric? If it’s skewed is it skewed to the left or the right?
f. Would you conclude that your sample could come from a normal distribution? Note: we know it’s not a normal sample, but the plots may or may not reflect this.
2022-03-11