Question 1

y - is how you calculate the

options:

• predicted value
• residual
• correlation
• line of best fit

Question 2

If the line of best fit is above the dot (actual value) then what can we say about the residual?

options:

zero

can't tell

positive residual

negative residual

Question 3

the line of best fit is: predicted grade = 66.3 + 3.1 * hours studied Interpret the slope

options:

As the predicted grade goes up by 1 the number of hours studied goes up by 66.3

As the number of hours studied goes up by 1 the predicted grade goes up by 66.3

As the number of hours studied goes up by 1 the predicted grade goes up by 3.1

As the predicted grade goes up by 1 the number of hours studied goes up by 3.1

Question 4

the line of best fit is:

predicted grade = 66.3 + 3.1 * hours studied

A student that doesn't study is expected to get a grade of

Question 5

the line of best fit is:

predicted income = 5189*GPA +44977.5

A student that graduates with a 3.5 GPA is expected to have an income of...

(No commas)

Question 6

The slope and correlation always

options:

• have different signs.
• have the same sign.
• can't tell anything about the sign each problem is different.

Question 7

Given that the correlation between X and Y is 0.84, the mean and standard deviation of X are 4.3 and 3.8, the mean and the standard deviation of Y is 2.7 and 5.6 respectively. Find the slope for the line of best fit. [____] round to 2 d.p.

Question 8

Given that the correlation between X and Y is 0.84, the mean and standard deviation of X are 4.3 and 3.8, the mean and the standard deviation of Y is 2.7 and 5.6 respectively. Find the y-intercept for the line of best fit. [____] round to 1 d.p.

Question 8 options:

Question 9

the line of best fit is:

predicted grade = 66.3 + 3.1 * hours studied

Calculate the residual for a student that studied for 5 hours and had a grade of 80 [____]

Question 10

the line of best fit is:

predicted income = 5189*GPA +44977.5

Calculate the residual for a student that graduates with a 3.0 GPA and has an income of 65000 [____] no commas

Question 11

R-Square (R²) is calculated by

options:

• Squaring the correlation
• Square root of the correlation
• Double the correlation
• Half the correlation

Question 12

R-Square (R²) is always between

Question 12 options:

- infinity and + infinity

0 and infinity

-1 and 1

0 and 1

Question 13

Linear model:

predicted age = 0.1 * weight + 15

r = 0.7

How much variation (R-Square) is accounted for by the model? [____]%

Question 14

Linear model:

predicted grade = 95 - 2 * (number of shows watched)

r = - 0.9

How much variation is accounted for by the model? [____]%

Question 15

predicted y = 23.4 - 12 x

R²= 36%

The correlation (r) between X and Y is [____]

hint: same sign as slope

Question 15 options:

Question 16

predicted y = 23.4 - 12 x

R2 = 36%

The average of all 81 residuals is...?

Hint: keep in mind that the sum of all errors is always 0.

Question 17

When looking at a residual plot we want the points to be scattered with no identifiable pattern.

options:

True

False

Question 18

Based on the residual plot we can say that it seems the model

options:

• is a good fit because there is an apparent pattern.
• is a good fit because there is NO apparent pattern.
• is NOT a good fit because there is NO apparent pattern.
  

Question 18 (1 point) Versus Fits (response is HandSpan) W 2 res 1 0 Residual -1 -2 -3- -4 17 18 19 20 21 22 23 24 25 Fitted Value