## (Solved) Math 2311 EMCF Homework 7 Sections 5.6 Honework will NOT be accepted through email or in person. Homework must be submitted through CourseWare,...

Hello, I needÂ help with these questions excluding number 6 & 7. I have until 11 tonight to complete it.

Math 2311 EMCF Homework 7
Sections 5.4-5.6
Honework will NOT be accepted through email or in person. Homework must be submitted through
CourseWare, https://www.casa.uh.edu, before the deadline. Submit this assignment on CourseWare under
â€œEMCFâ€ and choose ehw7. 1. In the least-squares regression line, the desired sum of the errors (residuals) should be
a.
b.
c.
d.
e. positive
negative
zero
maximized
none of these 2. Suppose that a least squares regression line equation is yË† = 1.65 âˆ’ 2.20x and the actual y value
corresponding to x = 10 is âˆ’19 What is the residual value corresponding to y = âˆ’19?
a.
b.
c.
d.
e. 1.35
âˆ’1.35
2.10
âˆ’2.10
none of these 3. The equation of the least squares regression line for a set of given points is yË† = 1.30 + 0.73x. What is
the residual for the point (4, 7)?
a.
b.
c.
d.
e. 2.41
âˆ’2.41
2.78
âˆ’2.78
none of these 4. A prediction of the worldâ€™s population in the year 2088 is an example of _________.
a.
b.
c.
d.
e. An outlier
Seasonality
Extrapolation
Correlation
none of these 5. An observation that causes the values of the slope and the intercept in the line of best fit to be
considerably different from what they would be if the observation were removed from the data set is
said to be
a.
b.
c.
d.
e. A causation variable
A common response
Extrapolation
Influential
A residual 1 In the Mosaic data set CoolingWater, water was poured into a mug and a temperature probe inserted into
the water with a few seconds of the pour. The time in minutes, time, and the temperature in Celsius, temp,
were recorded.
6. Find the equation of the LSRL for this data.
a.
b.
c.
d.
e. yË† = 64.27 âˆ’ 0.22x
yË† = 64.27 + 0.22x
yË† = 255.66 âˆ’ 3.60x
yË† = 3.60 + 0.22x
none of these 7. Plot the residuals for the CoolingWater data. What does the pattern of the residuals tell you about the
linear model?
a.
b.
c.
d.
e. The evidence is inconclusive.
The residual plot confirms the linearity of the model.
The residual plot does not confirm the linearity of the model.
The residual plot clearly contradicts the linearity of the model.
none of these Use the variables temp (average outdoor temperature in F for a billing cycle) and kwh (the electricity usage
for a billing cycle) from the data set called Utilities (from Mosaic data) for problems 8-11.
8. Which variable should be the explanatory variable?
a. temp
b. kwh
9. What is the value or the coefficient of determination?
a.
b.
c.
d.
e. -0.0798778
0.0063805
0.9201222
0.0798778
none of these 2 10. Which of the following is the residual plot for this data? (b) âˆ’600 200 0 800 400 (a) 10 30 50 70 10 30 70 (d) 0 40 20 80 (c) 50 0 50 150 0 50 150 e. none of these
11. Would you conclude that this LSRL is a good model?
a. Yes
b. No
a.
b.
c.
d.
e. There is a reversal in direction of a comparison when data is transformed with powers.
There is a reversal in direction of a comparison when data from several groups is combined.
The LSRL is used to predict data that is far from the other explanatory values.
None of these. 3 Use the following information to answer questions 13 - 15: The following two-way table describes the
preferences in music genre and ice-cream flavors for a random sample of 100 people. Pop
Country
Rock Vanilla
20
8
15 Chocolate
5
15
2 Strawberry
10
12
13 13. What percent of the sample likes rock music?
a.
b.
c.
d.
e. 35%
43%
30%
22%
none of these 14. What percent of vanilla lovers like country music?
a.
b.
c.
d.
e. 18.6%
68.2%
22.7%
28.6%
none of these 15. What percent of people from this survey like both pop music and chocolate ice-cream?
a.
b.
c.
d.
e. 5%
35%
22%
63%
none of these 4

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