Chapter 3 Review Statistics

What is the formula to calculate the RESIDUAL at a particular value of the explanatory variable?

For r= -0.87.

What is the direction and strength of the linear trend of the data?

• Direction: Positive

  Strength: Fairly Strong

• Direction: Negative

  Strength: Weak

• Direction: Positive

  Strength: Weak

• Direction: Negative

  Strength: Fairly Strong

Answer explanation

For every increase in minute by 1, the hunger level increases by 5.

For r=0.2.

What is the direction and strength of the linear trend of the data?

• Direction: Positive

  Strength: Weak

• Direction: Positive

  Strength: Moderately Strong

• Direction: Negative

  Strength: Weak

• Direction: Negative

  Strength: Moderately Strong

The addition of WHAT new point would STRENGTHEN the correlation coefficient?

A point that ___________________________ the ____________ trend of the data

(separate the two words you would use to fill in the blank with a comma)

Answer explanation

"Outliers in the pattern strengthen r"

A scatterplot that appears to go up and right with a few outliers will be described to have which of the following directions:

• Positive

• Negative

• Neither positive nor negative. Just a scatter.

True or False.

To make a proper scatterplot: after plotting the data points, you DON'T connect the dots.

• True

• False

Answer explanation

We never connect the dots! We're looking to see if, overall, the dots are following some identifiable form (linear, exponential, parabolic, etc). If we connect the dots, we may observe an un-identifiable curve!

A good LSRL has:

Answer explanation

We want the typical distances between the actual and predicted y-values to be small

We want MOST of the variability in the y-values to be accounted for by the LSRL

Interpret the y-intercept in this context.

Answer explanation

At 0 minutes, the hunger level is 4.

Consider a residual plot with the following characteristic:

There is a clear positive-negative-positive pattern among the residuals.

"Linear" is a GOOD or BAD form to model the data?

• GOOD - we want to see a pattern in our residual plot

• BAD - any pattern that can be assessed from the residual plot is an indicator that the data does not follow a linear form well