Chapter 6 Quiz (Data Science)

Why is the mean a good model?

• Because the mean is a model that balances the residuals and minimizes the sum of squared residuals

• Because the mean is the best model for all categorical variables

• Because the mean is the only true statistical model that can represent a population parameter

• Because the mean is the best model whenever you make a visualization of data

Tags

CCSS.HSS.ID.B.6A

If a data point is very far away from the mean, what would you expect for the residual?

• When farther away, the more variable the residual

• When farther away, the more positive the residual

• When a data point is very far away from the mean, the residual should be 0, because the mean balances the residuals.

• When farther away, the larger the absolute value of the residual

Tags

CCSS.HSS.ID.B.6B

What's true of the distribution of any variable, if your model is the mean of that variable?

• The distribution of the variable is more narrow than the distribution of its residual.

• The distribution of the variable is the same shape as the distribution of its residual.

• The distribution of the variable is always centered on a number lower than the distribution of the residual is centered on.

• The distribution of the variable is always centered on 0, whereas the center of the distribution of its residual is unpredictable.

Tags

CCSS.HSS.ID.B.6B

If you ran the R code, what would you be able to tell from the output?

• How much error there is around the empty model

• the mean

• All of the above

If you ran the R code, what would you be able to tell from the output?

• How much error there is around the empty model

• The sum of the squared residuals

• The sum of squares

• all of the above

The sum of squares gets larger as:

• The variation increases

• The sample size increases

• The spread of the distribution increases

• All of the above

Tags

CCSS.6.SP.B.5C

Let's say you've calculated the sum of squares for Thumb in the Fingers data frame. What would the advantage be of dividing that number by n−1?

• It turns the sum of squares into a measure of spread.

• You can use it to compare error across samples of different sizes.

• You would have calculated the population variance.

• None of the above. There's no advantage of dividing SS by n-1.

What function would you use to calculate the variance?

• lm( )

• variance( )

• vari( )

• var( )

What R code will output the standard deviation for Thumb sizes in the Fingers data frame?

• sd(Thumb$ Fingers

• sqrt(var(Thumb)

• favstats(Fingers$Thumb)

• all of the above

If you've calculated the standard deviation for thumb sizes for students in the Fingers data frame, what have you found?

• Roughly the total squared deviations from the mean.

• Roughly the average squared deviation from the mean.

• Roughly the average deviation from the mean.

• None of the above