Week 6 checkpoint help gm533

Week 6 Checkpoint Help GM533

B Heard


13.8 THE REAL ESTATE SALES PRICE CASE • A real estate agency collects data concerning y

= the sales price of a house (in thousands of dollars), and x = the home size (in hundreds of square feet). The data are given in the table below. The MINITAB output from fitting a least squares regression line to the data is on the next page.


a) By using the formulas illustrated in Example 13.2 (see page 497) and the data provided, verify that (within rounding) b0 = 48.02 and b1 = 5.700, as shown on the MINITAB output.

I put the data in Excel and did the math there. The formulas are provided in the text.


Size (x) Price (y)

23 180

11 98.1

20 173.1

17 136.5

15 141

21 165.9

24 193.5

13 127.8

19 163.5

25 172.5

Week 6 Checkpoint Help GM533Size (x) Price (y) xy x^2

23 180 4140 52911 98.1 1079.1 12120 173.1 3462 40017 136.5 2320.5 28915 141 2115 22521 165.9 3483.9 44124 193.5 4644 57613 127.8 1661.4 16919 163.5 3106.5 36125 172.5 4312.5 625

Sum of x's Sum of y's sum of xy's sum of x^2's (sum of x's)^2188 1551.9 30324.9 3736 35344

n10

SSxy SSxx1149.18 201.6

b1 y bar x bar5.700298 155.19 18.8

b048.0244

y hat = b0 + b1*xy hat = 48.0244 plus 5.70029762 x


b) Interpret the meanings of b0 and b1. Does the interpretation of b0 make practical sense?

The b1 is 5.70 which basically is saying for every 100 square feet the average sales price increases that much

b0 is the y intercept when x is zero. In other words, it says that a house with zero square feet would cost about 48 thousand dollars. No, this doesn’t make sense (I will talk about this).


c) Write the least squares prediction equation.

y hat = 48.02 + 5.7x

y hat = b0 + b1*xy hat = 48.0244 plus 5.70029762 x


d) Use the least squares line to obtain a point estimate of the mean sales price of all houses having 2,000 square feet and a point prediction of the sales price of an individual house having 2,000 square feet.

Plug and chug (insert 20 for x remember the size was in 100’s of square feet)

y hat = 48.02 + 5.7(20) = 162.02 (in thousands)So the predicted price would be $162,020


13.21 THE STARTING SALARY CASEThe MINITAB output of a simple linear

regression analysis of the data set for this case (see Exercise 13.4 on page 501) is given in Figure 13.11. Recall that a labeled MINITAB regression output is on page 509.



bo (Part a)b1


SSE (Part b)s^2

s


sb1 (Part c)t

t = b1/sb1 (show)


df (Part d)


Table fromhttp://www.statsoft.com/textbook/distribution-tables/#t(I just search on Internet, you have one in text)

(Part d continued)t.025 = 2.57 compared to 14.44 ?

Reject because it’s way out there in the rejection region Reject H0, there is strong evidence of something going on with x and y

http://www.statsoft.com/textbook/distribution-tables/#t

Table fromhttp://www.statsoft.com/textbook/distribution-tables/#t(I just search on Internet, you have one in text)

(Part e)t.005 = 4.03 compared to 14.44 ?

Reject because it’s still way out there in the rejection region Reject H0, there is strong evidence of something going on with x and y (Very strong relationship)

http://www.statsoft.com/textbook/distribution-tables/#t


f) p value was .000 agrees with previous two to reject at all alphas. Very very strong evidence of an x and y relationship

g) 95% CI Just use what you have found aboveThe interval is b1 +/- t.025 sb1h) 99% CI Just use what you have found aboveThe interval is b1 +/- t.005 sb1


sbo (Part i)t

t = b0/sb0 (show)

bo


j) p value was .000, reject at all alphas. Very very strong evidence of an x and y relationship

k) Use the formulas and the data! (should give you the same answer you got in part c for sb1 and in part i for sbo.


13.30 THE FUEL CONSUMPTION CASEThe following partial MINITAB regression output

for the fuel consumption data relates to predicting the city’s fuel consumption (in MMcf of natural gas) in a week that has an average hourly temperature of 40°F.



Part a


Part b


c) Remembering that s = .6542; SSxx = 1,404.355; n = 8, hand calculate the distance value when x0 = 40. Remembering that the distance value equals , use s and from the computer output to calculate (within rounding) the distance value using this formula. Note that, because MINITAB rounds sy, the first hand calculation is the more accurate calculation of the distance value.


Distance Value (dv) = 1/8 + (40-43.98)2 / 1404.355 = 0.1363

AndDistance Value (dv) = (0.241 / 0.6542)2 = 0.1357


d) Remembering that for the fuel consumption data b0 = 15.84 and b1 = -.1279, calculate (within rounding) the confidence interval of part a and the prediction interval of part b.

CI: 15.84 - 0.1279 (40) ± 2.447*0.6542*√() = [10.13299, 11.31501] 0.1363

For PI, just substitute 1.1363 for 0.1363


e) Remember you are predicting for one day, so use prediction interval.

Since 9.01 < 9.595 and 12.43 > 11.847 thecity cannot be ____ % confident it won’t pay a

fine. (Fill in the blank)

Week 6 Checkpoint Help GM533THE FRESH DETERGENT CASE In Exercises 13.50 through 13.55, we give MINITAB and Excel outputs of simple linear regression

analyses of the data sets related to six previously discussed case studies. Using the appropriate computer output,

a Use the explained variation and the unexplained variation as given on the computer output to calculate (within rounding) the F (model) statistic.

b Utilize the F (model) statistic and the appropriate critical value to test H0 : β1 = 0 versus Hα : α1 ≠ 0 by setting a equal to .05. What do you conclude about the regression relationship between y and x?

c Utilize the F (model) statistic and the appropriate critical value to test H0 : β1 = 0 versus Hα : β1 ≠ 0 by setting a equal to .01. What do you conclude about the regression relationship between y and x?

d Find the p -value related to F (model) on the computer output and report its value. Using the p -value, test the significance of the regression model at the .10, .05, .01, and .001 levels of significance. What do you conclude?

e Show that the F (model) statistic is (within rounding) the square of the t statistic for testing H0 : β1 = 0 versus Hα : b1 ≠ 0. Also, show that the F.05 critical value is the square of the t025 critical value.

Note that in the lower right hand corner of each output we give (in parentheses) the number of observations, n, used to perform the regression analysis and the t statistic for testing H0 : β1 = 0 versus Hα : β1 ≠ 0.


Part a

Used a table at http://www.statsoft.com/textbook/distribution-tables/#f (I was lazy)

http://www.statsoft.com/textbook/distribution-tables/#f


F.05 =4.196, reject H0 (df1 (top) = 1, df2 (left) = 28). Looks like there is STRONG evidence of a significant relationship between x and y. Maybe we should send out a private detective.

Used a table at http://www.statsoft.com/textbook/distribution-tables/#f (I was lazy)

c) F.01 =7.636, reject H0 (df1 (top) = 1, df2 (left) = 28). Looks like there is STRONG (Very because of .01) evidence of a significant relationship between x and y. Maybe we should send out two private detectives. (the private detective statement is meant to be a joke)

http://www.statsoft.com/textbook/distribution-tables/#f


d) p-value is ______ (smaller than all levels of significance), reject H0 . Again, pick your “ly” ending word that means there is definitely strong evidence of a significant relationship between x and y. (I will not mention private detectives anymore).


Part eSquare this number, you should see that it gives you a result within rounding error.Then get your table value for t.025 and verify(t.025)2 = 4.19 = F.05


I will post these in the statcave atwww.facebook.com/statcave

http://www.facebook.com/statcave

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Week 6 checkpoint help gm533