4/26/02 252y0231 (Page layout view!) ECO252 QBA2 THIRD HOUR EXAM April 18, 2002 I. (10+ points) Do all the following; Name KEY Hour of Class Registered (Circle) MWF TR 10 12 12:30 2:00 1. Hand in your computer printouts for problems 2 and 3.(5 points – 3 point penalty for not handing in). remember that the ANOVA printout must be completed, using a 5% significance level, for full credit. I should be able to tell what is tested and what are the conclusions. 2. a. In particular, is the interaction between car and driver significant? Which numbers made you think that? (2) b. Create two confidence intervals for the difference between the means for cars 3 and 4, one that is valid alone, and one that is valid simultaneously with other similar intervals. Do these intervals show a significant difference between these two means? Why? (4) Solution: The only parts of the solution to computer problem 2 that you need are: Tabulated Statistics ROWS: car 1 2 3 4 ALL COLUMNS: driver 1 2 3 ALL 42.000 32.000 30.667 31.333 34.000 25.000 28.000 45.000 24.667 30.667 12.667 29.333 28.333 54.667 31.250 26.556 29.778 34.667 36.889 31.972 CELL CONTENTS -- mpg:MEAN MTB > twoway 'mpg''car''driver' Two-way Analysis of Variance Analysis of Variance for mpg Source DF SS car 3 590.3 driver 2 76.1 Interaction 6 3227.9 Error 24 336.7 Total 35 4231.0 MS 196.8 38.0 538.0 14.0 To complete the printout, divide through the MS column by MSW 14 and place the results in the in the F column. Then look up the corresponding values of F in 5% lines on the F table. Source DF SS MS F.05 H0 F car 3 590.3 196.8 14.057s F 3,24 3.01 Car means identical driver 2 76.1 38.0 2.714ns .05 2,24 F.05 6,24 F.05 3.40 Driver means identical Interaction 6 3227.9 538.0 38.428s 2.51 No interaction Error 24 336.7 14.0 Total 35 4231.0 The first and the third null hypotheses are rejected. a) Since 38.428 is larger than 2.51, we reject the hypothesis that there is no interaction and say that there is significant interaction. b) Cars 3 and 4 are in the rows. There are R 4 rows, C 3 columns and P 3 measurements per cell. Of course RC ( P 1) 432 24, the number of degrees of freedom for 'within' or 'error.' From the outline, we have for Bonferroni confidence intervals for row means 2MSW 1 2 x1 x 2 t RC P 1 .This becomes, for m 1, 2m PC 2MSW 214 .0 34 .667 36 .889 2.064 2.222 2.064 3.111 PC 9 2.22 3.64 This indicates no significant difference. 3 4 x 3 x 4 t 24 2 1 4/16/02 252y0231 For Scheffe intervals for row means use 1 2 x1 x 2 R 1FR 1, RC P 1 2MSW PC . So 3F.053,24 214 .0 2.222 33.013.111 2.22 5.30 . This 3 4 34 .667 36 .889 9 indicates no significant difference. c. In your income and education regression, (i) Explain what coefficients are significant and why? (2) (ii) What income would you predict for someone with 2 years of education? (1) (iii) Make a confidence interval for the income of someone with 2 years of education using some of the information generated by Minitab below. (2) Descriptive Statistics Variable Educ N 32 Mean 12.000 Median 12.000 TrMean 12.071 Variable Educ Min 4.000 Max 20.000 Q1 8.000 Q3 16.000 StDev 4.363 SEMean 0.771 Column Sum of Squares Sum of squares (uncorrected) of Educ = 5198.0 Solution: The relevant output is: Regression Analysis The regression equation is Income = 5078 + 732 Educ Predictor Constant Educ Coef 5078 732.4 s = 2855 Stdev 1498 117.5 R-sq = 56.4% t-ratio 3.39 6.23 p 0.002 0.000 R-sq(adj) = 55.0% i) So we can state that, since the p-values are both below .05, that both coefficients are significant at the 5% level. ii) The regression can be written as Income 5078 732 Educ or Income 5078 732 .4 Educ . So Income 5078 732 (2) 6542 or Income 5078 732 .4(2) 6542 .8 . iii) From the outline The Confidence Interval is Y0 1 Yˆ0 t sYˆ , where sY2ˆ s e2 n X 0 X 2 X 2 nX 2 1 2 12 2 8151025 1 100 1636249 .2 and s 1636249 .2 1279 .16 . 2855 2 32 5198 3212 2 Yˆ 32 590 30 If we use t n2 t .025 2.042 , we get Y0 6542.8 2.0421279.16 6542 2612 . 2 Please note the following from the 252 home page: The rule on p-value: If the p-value is less than the significance level (alpha) reject the null hypothesis; if the pvalue is greater than or equal to the significance level, do not reject the null hypothesis. Significance This is a topic that was covered under hypothesis tests. Probably the first reference I made to this was even earlier when I said that a parameter is significant if it is not zero. I later said that a null 2 hypothesis often says that a parameter or a difference between parameters is insignificant. If a result is significant we reject the null hypothesis. To put this more generally, a result is (statistically) significant if it is larger or smaller than would be expected by chance alone. Thus in the case of a regression coefficient the measure of significance could be the p-value, which tells us the probability of getting our actual result or something more extreme if we assume that the population value of the coefficient is zero. If the pvalue is small (below our significance level), then it is unlikely that our assumption about the coefficient is correct and we say that the coefficient is significant (or significantly different from zero). Of course, the various hypothesis tests that we have discussed here are also often ways of proving significance. 3 4/16/02 252y0231 II. Do at least 4 of the following 5 Problems (at least 10 each) (or do sections adding to at least 40 points Anything extra you do helps, and grades wrap around) . Show your work! State H 0 and H1 where applicable. Never say 'yes' or 'no' without a statistical test. 1. On the following pages there are printouts from two computer problems. a. The One-way ANOVA Problem ( Albright, Winston, Zappe - abbreviated): An automobile parts producer has instituted an employee empowerment program in five plants. Random samples of employees in each plant are asked to rate the success of the program on a 1 to 10 scale. 10 being the highest rating. They want to know if the program is being implemented with equal success at each plant and are thus looking to see if there is a significant difference between mean ratings at each plant. They are assuming that the results are distributed according to Normal distributions with similar variances. (i) Indicate what hypothesis was tested, what the p-value was and whether, using the p-value, you would reject the null if () the significance level was 5% and () the significance level was 1%. Explain why. Does this mean that the success was equal in all plants? (3) (ii) Do a 'normal' and a Scheffe confidence interval .05 for the difference between the means in the two plants that were most successful. Do these intervals indicate a difference in the success of the program between these two plants? Why? (4.5). (iii) The printout gives 95% confidence intervals for the means for each plant. Find the numbers for the confidence interval for 'South.' Why is this interval larger than the others? (2.5) (iv) I would question whether ANOVA was appropriate for this problem because there is no evidence that the underlying populations are Normally distributed. What method would I prefer for this problem? (1) One-way ANOVA problem Worksheet size: 100000 cells MTB > RETR 'C:\MINITAB\2X0231-1.MTW'. Retrieving worksheet from file: C:\MINITAB\2X0231-1.MTW Worksheet was saved on 4/ 9/2002 MTB > print c1-c5 Data Display Row south midwest n-east s-west west 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 7 1 8 7 2 9 3 8 5 7 4 7 6 10 3 9 10 8 4 3 2 7 7 5 10 10 6 3 5 2 6 4 5 2 7 8 7 7 5 5 4 4 2 4 5 5 3 3 3 5 5 6 4 7 10 7 6 7 7 4 4 7 8 9 10 4 10 5 6 6 6 6 6 3 4 8 6 2 4 5 6 4 7 4 3 5 4 7 6 4 4 4/16/02 252y0231 MTB > AOVOneway c1 c2 c3 c4 c5. One-Way Analysis of Variance Analysis of Variance Source DF SS Factor 4 57.45 Error 85 386.15 Total 89 443.60 Level south midwest n-east s-west west Pooled StDev = N 11 26 14 18 21 MS 14.36 4.54 Mean 5.545 6.000 4.286 6.722 5.048 StDev 2.697 2.623 1.267 2.081 1.532 F 3.16 p 0.018 Individual 95% CIs For Mean Based on Pooled StDev ---------+---------+---------+------(--------*-------) (-----*-----) (-------*------) (------*-----) (------*-----) ---------+---------+---------+------- 2.131 Solution: a) (i) All one-way ANOVAs test for equality of the means of the populations represented by the columns, so H 0 is 1 2 3 4 5 . The p-value is 1.8%, so we reject the null hypothesis at the 5% significance level, but not the 1% level. If we reject the null hypothesis we say that the success level was not the same at all the plants. (ii) The Midwest and the Southwest plants were the most successful. From the outline if we desire a single interval and we want the difference between means of column 1 and column 2. 1 2 x1 x2 t n m s 2 85 2 4 x.2 x4 t.025 s 1 1 , where s MSW . This becomes n1 n 2 1 1 6.000 6.722 1.988 4.54 094017 0.722 1.988 0.653 0.722 1.299 26 18 If we desire intervals that will simultaneously be valid for a given confidence level for all possible intervals 1 1 between column means, use 1 2 x1 x2 m 1Fm1,n m s , which becomes n n 2 1 1 1 2 4 x2 x4 5 1F4,85 4.54 6.000 6.722 5 12.48 4.54 094017 0.722 2.058 26 18 since both these intervals include zero, there is no significant difference. (iii) If we use the 'normal' formula for the difference between two means, we get 1 x1 tn m s 2 1 n1 1 5.545 1.277 . It is the largest interval because we divide the pooled 11 standard deviation by the square root of n1 , which is the smallest of all the sample sizes. (iv) If the underlying distribution is not normal, use the Kruskal-Wallis test to compare medians 1 5.545 1.988 4.54 b. The Regression Problem: This relates the number of shares in thousands to the age of board members of a corporation. (i) Looking at significance tests and the value of R-squared, how successful is this regression? Why? Why shouldn't this surprise you? (3) (ii) Note that c1 contains 'shares' and that c4 contains predicted values of 'shares.' Add a regression line to the graph. (1) 5 4/16/02 252y0231 (ii) What equation relates the number of shares owned to the age of the board member? How many shares does it say that we should expect a 82-year old board member to own? Would you take this seriously? Why? (2) Regression Problem Worksheet size: 100000 cells MTB > RETR 'C:\MINITAB\2X0231-5.MTW'. Retrieving worksheet from file: C:\MINITAB\2X0231-5.MTW Worksheet was saved on 4/10/2002 MTB > #252sols3 MTB > print c1 c2 Data Display Row shares age 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 7.9 66.4 29.7 60.5 10.4 28.7 86.9 121.1 35.3 2.8 74.4 11.1 9.1 19.1 18.8 3.1 96.5 47.0 31.1 53 60 69 49 67 68 46 62 63 55 57 71 66 70 66 57 54 64 56 MTB > plot c1*c2 (plot omitted) MTB > regress c1 on 1 c2 c3 c4 Regression Analysis The regression equation is shares = 154 - 1.88 age Predictor Constant age Coef 154.14 -1.881 Stdev 64.88 1.062 s = 33.04 R-sq = 15.6% Analysis of Variance SOURCE Regression Error Total DF 1 17 18 SS 3425 18557 21982 t-ratio 2.38 -1.77 p 0.030 0.094 R-sq(adj) = 10.6% MS 3425 1092 F 3.14 Unusual Observations Obs. age shares Fit Stdev.Fit 8 62.0 121.10 37.52 7.71 R denotes an obs. with a large st. resid. MTB > MTB > SUBC> SUBC> SUBC> SUBC> MTB > p 0.094 Residual 83.58 St.Resid 2.60R plot c4*c2 (plot omitted) plot c4*c2 c1*c2; symbol; type 3 1; color 8 9; overlay. end 6 4/16/02 252y0231 C4 100 50 0 50 60 70 age Solution: b ) (i) This is a very unsuccessful regression - surely the author could have found a better predictor of the number of shares owned than age! R 2 is very small on a zero to one scale and the p-value for the slope is above 5%. The regression seems to say that the number of shares owned declines as the board member gets older. I see no reason why this should be true. (ii) To add a regression line, just connect the x's. (iii) The regression equation says shares = 154 - 1.88 age. If a board member is 82 shares 154 1.8882 0.16 . Of course, you can't own negative shares, and the fact that the oldest board member is 71 might lead us to feel that we have exceeded our competence. Basically the low R 2 leaves us unsure whether we should take any of its results seriously. 7 4/16/02 252y0231 2. A researcher believes that the data below has a Normal distribution with a mean of 80 and a standard x x 80 deviation of 5. For your convenience the values of z are computed for you. 5 a. Use a chi-squared test to find out if the distribution is correct. (9) b. Is there a better way to do this problem than chi-squared? Why? Do it. (5) c. Assume that, instead of using population means given above, we actually checked the data and found that x 80 and s 5. How would this change what we did in a)? (1) d. Assume that, instead of using population means given above, we actually checked the data and found that x 80 and s 5. How would this change what we did in b)? (1) Observed x interval z interval Frequency below 70 below -2.0 3 70-74 -2.0 to -1.2 20 74-78 -1.2 to -0.4 53 78-82 -0.4 to 0.4 52 82-86 0.4 to 1.2 46 above 86 above 1.2 26 200 Solution: H 0 : N 80,5 a) We find the cumulative distribution of z , Fe , and use it to find the frequency f e .We then find E f e n , where n 200 . Fe is the cumulative probability. In the first column Fe 2.0 Pz 2.0 .5 P2.0 z 0 .5 .4772 .0228 and Fe 1.2 Pz 1.20 .5 P0 z 1.2 .5 .3849 .8849 . f e is the difference between successive values of Fe . For example, P2.0 z 1.2 .Pz 1.2 Pz 2.0 .1151 .0228 .0923 x interval Fe fe z interval O E below 70 below -2.0 3 .0228 .0228 4.56 70-74 -2.0 to -1.2 20 .1151 .0923 18.46 74-78 -1.2 to -0.4 53 .3446 .2295 45.90 78-82 -0.4 to 0.4 52 .6554 .3108 62.16 82-86 0.4 to 1.2 46 .8849 .2295 45.90 above 86 above 1.2 26 1.0000 .1151 23.02 200 1.0000 200.00 We use the O and E to do a conventional chi-squared analysis. In the right column is the short-cut method. Row O E 1 2 3 4 5 6 3 20 53 52 46 26 200 4.56 18.46 45.90 62.16 45.90 23.02 200.00 E O 1.5600 -1.5400 -7.1000 10.1600 -0.1000 -2.9800 0.00 E O2 2.434 2.372 50.410 103.226 0.010 8.880 E O 2 E 0.53368 0.12847 1.09826 1.66064 0.00022 0.38577 3.80704 O2 E 1.9737 21.6685 61.1983 43.5006 46.1002 29.3658 203.8071 For the Chi-Squared Method, we could have had to merge two cells, because the first E was below 5. 2 However, the small value of the first row term in the E O E column indicates that there was no need to do this. We thus have 6 - 1 = 5 degrees of freedom. The value of Chi-squared that we computed is 3.80704 or 203.8071-200 = 3.8071. From the Chi-squared table .2055 11 .0705 . This is more than our computed 2 , so do not reject H 0 . Note - there was a typo here that made the top of the first group -2.8. This should 8 4/16/02 252y0231 not have affected you answers, but it did. The interval below -2.8 creates an interval where the E is extremely low so that the first two intervals should have been merged to prevent a false rejection. None of you did this and there was a minor penalty. b) For most problems where the population mean and standard deviation are given the best method is Kolmogorov-Smirnov Fe is copied from part a) and O is made into a Cumulative distribution Fo by dividing through by n 200 and adding down the column. D is the difference between the two cumulative distributions. Row O 1 2 3 4 5 6 3 20 53 52 46 26 200 O n 0.015 0.100 0.265 0.260 0.230 0.130 1.000 Fo Fe D 0.015 0.115 0.380 0.640 0.870 1.000 0.0228 0.1151 0.3446 0.6554 0.8849 1.0000 0.0078 0.0001 0.0354 0.0154 0.0149 0.0000 For the Kolmogorov-Smirnov Method the 5% critical value is 1.36 0.096 . This is less than the 200 maximum value of D , which is .0454, so reject H 0 . c) If the sample mean and standard deviation have been computed from the data, we would lose 2 degrees of freedom. We would go ahead exactly as before until the time came to look up chi-squared which would now have 3 degrees of freedom. d) We would go ahead exactly as in b) until the time came to use a table. We would find our critical value on the Lilliefors table. 9 4/16/02 252y0231 3. (Weirs) A maker of stain removers is testing the effectiveness of four different formulations of a new product. Columns represent formulations 1-4 of the product and the 6 rows represent different stains (Creosote, crayon, motor oil, grape juice, ink, coffee). Each formulation is rated on a 1-10 scale for its effectiveness. Stain 1 2 3 4 5 6 Sum Count Form 1 Form 2 Form 3 Form 4 2 7 3 6 9 10 7 5 4 6 1 4 9 7 4 5 6 8 4 4 9 4 2 6 39 42 21 30 6 6 6 6 Sum of Squares 299 314 sum count 18 4 31 4 15 4 25 4 22 4 21 4 132 24 24 Sum of squares 98 255 69 171 132 137 862 95 a. Assume that the parent distribution is Normal and compare the mean ratings for the four formulations, noting the fact that it is cross-classified. Use .10 . (14) Note: If you wish to ignore that the fact that the data is classified by stain type, indicate this now and compare the column means assuming that the data is four independent random samples from a Normal distribution.(10). ( .10 ) b. Using the same significance level, assume that Formulation 1 is the current formula and use Scheffe intervals to see which formulations have mean ratings that differ significantly from the current formulation. (4) c. Using a significance level of 15%, repeat the analysis in b) using Bonferroni intervals. (4) Solution: If the parent distribution is Normal use ANOVA, if it's not Normal, use Friedman or Kruskal-Wallis. If the samples are independent random samples use 1-way ANOVA or Kruskal Wallis. If they are cross-classified, use Friedman or 2-way ANOVA. a) 2-way ANOVA (Blocked by stain) ‘s’ indicates that the null hypothesis is rejected. Stain Form 1 Form 2 Form 3 Form 4 sum count mean Sum of squares x i.. n i SS x1 x2 x3 x4 x i. x i2. 1 2.00 7.0 3.00 6.0 18.0 4 4.50 98 20.250 2 9.00 10.0 7.00 5.0 31.0 4 7.75 255 60.062 3 4.00 6.0 1.00 4.0 15.0 4 3.75 69 14.063 4 9.00 7.0 4.00 5.0 25.0 4 6.25 171 39.063 5 6.00 8.0 4.00 4.0 22.0 4 5.50 132 30.250 6 9.00 4.0 2.00 6.0 21.0 4 5.25 137 27.562 Sum 39.00 +42.0+21.00 +30.0 =132.0 24 5.50 862 191.250 6 +6 +6 +6 =24 nj 6.50 299.00 42.25 x j SS x 2j From the above x 7.0 3.50 5.0 5.5 x +314.0+95.00 +154.0 =862.0 +49.0 +12.25 +25.0 =128.5 x 132 , n 24 , SST x x 132 5.5 . SSC n 24 n x 2 j j 2 ij x 2 ij 862 .0 , x ( SSW SST SSC SSR 52.0 ) x 2 .j 1 2 .85 and 2 2 SSR 191 .25 n x 862 .0 24 5.52 862 .0 726 .0 136 .0 . n x 6128 .5 24 5.52 771 .0 726 .0 45 .0 . ANOVA. 2 i. n x 2 i i. This is SSB in a one way n x 4191 .25 24 5.52 765 .0 726 .0 39 .0 2 10 4/16/02 252y0231 Source SS DF MS F.10 F Rows (Stains) 39.0 5 7.80 2.25 Columns(Formulas) 45.0 3 15.00 4.33 H0 F 5,15 2.27 ns F 3,15 2.49 s Row means equal Column means equal Within (Error) 52.0 15 3.46667 Total 136.0 23 So the formulations (column means) are significantly different. One way ANOVA (Not blocked by stain) Source SS Columns(Formulas) 45.0 ( SSW SST SSB .91.0 ) DF MS F 3 15.00 3.2967 F.10 H0 F 3,20 2.38 s Column means equal Within (Error) 91.0 20 4.55 Total 136.0 23 Once again, the formulations (column means) are significantly different. b) This resembles problem F2. The formulas are given in the outline. R 6 is the number of rows, C 4 is the number of columns and P 1 is the number of observations per cell. Note that if P 1 , replace RC P 1 with R 1C 1 6 14 1 15 , which is the error degrees of freedom above. The Scheffe’ formula for column means is 1 2 x1 x2 becomes 1 2 x1 x2 C 1FC 1,R 1C 1 2MSW R C 1FC 1, RCP 1 2MSW PR x1 x2 , which 3 1F3,15 2MSW 6 23.46667 x1 x2 5.755 x1 x2 2.40 . Since the formula works 6 regardless of the column number, we get the following 3 contrasts. 1 2 6.50 7.00 2.40 0.50 2.40 x1 x2 22.49 1 3 6.50 3.50 2.40 3.00 2.40 1 4 6.50 5.00 2.40 1.50 2.40 Since the error part of the formula (2.40) is larger than the difference between sample means in two cases, there is no significant difference there. However, Formulation 3 is significantly worse than Formulation 1. Note - Most people who took the exam early were told to use a significance level of .01 in parts a) and b). this meant that you used F(5, 15) = 4.56 and F(3,15) = 5.42. The results would be that none of the tests or intervals would show significant differences. 2MSW c) The Bonferroni formula for column means is 1 2 x1 x2 t RC P 1 . Note that if 2m PR P 1 , replace RC P 1 with R 1C 1 6 14 1 15 . If .15 and we are doing m 3 intervals, 2m .15 23 .025. The Bonferroni formula becomes 23.46667 2MSW 15 2 MSW x1 x2 2.131 x1 x2 t.025 2m 6 R R x1 x2 2.29 . Once again, substitute the sample means. 1 2 6.50 7.00 2.29 0.50 2.29 1 3 6.50 3.50 2.29 3.00 2.29 1 4 6.50 5.00 2.29 1.50 2.29 In spite of the smaller, and probably more appropriate, intervals, the results are the same as in b) We will stick with the old formulation. 1 2 x1 x2 tR 1C 1 11 4/16/02 252y0231 3(ctd.). d. Actually, when Weirs presented the data in the previous problem, repeated below, he assumed that the underlying distribution was not Normal. So compare the median ratings using a 10% significance level. (6) Stain 1 2 3 4 5 6 Sum Count Sum of Squares Form 1 Form 2 Form 3 Form 4 2 7 3 6 9 10 7 5 4 6 1 4 9 7 4 5 6 8 4 4 9 4 2 6 39 42 21 30 6 6 6 6 299 314 sum count 18 4 31 4 15 4 25 4 22 4 21 4 132 24 24 Sum of squares 98 255 69 171 132 137 862 95 Solution: d) This becomes a Friedman test. We rank the data within rows. H 0 : Columns from same distribution Row 1 2 3 4 Row 1 2 1 2 3 4 5 6 2 9 4 9 6 9 7 10 6 7 8 4 3 7 1 4 4 2 6 5 4 5 4 6 1 2 3 4 5 6 Sum 1 4 3 4 2.5 4 4 3 3 4 4.0 2 17.5 21 3 4 2 3 2 1 1 2.5 1 2 1.5 1.5 1 3.0 8.5 13.0 There are r 6 rows and c 4 columns. Check: The rank sums must add to r cc 1 45 6 60 . 2 2 Since 17.5 + 21 + 8.5 + 13.0 = 60, we are all right. The Friedman Statistic is 12 12 1 F2 SR 2 3r c 1 17.5 2 21 2 8.5 2 13 2 365 988 .5 90 8.85 . r c c 1 645 10 The Friedman Table has no values for c 4 and r 6 , so we use a chi-squared table with c 1 3 degrees of freedom. Since .10 , the table gives us a critical value of 6.2514. . Since our computed chisquared is larger than the table value, we reject H 0 . Note - if you were told to use a significance level of .01, you would have gotten a critical value of 11.3449 and would not have rejected the null hypothesis. 12 4/16/02 252y0231 4. Use methods appropriate to testing goodness of fit. a. Test the hypothesis that the numbers below came from a Normal distribution. Use a 10% significance level. (6) note that Minitab says the following: mean 294.444 stdev 52.6548 n 9.00000 b. Test the hypothesis that the numbers below came from a Normal distribution with a mean of 230 and a standard deviation of 50 (6) 235 219 269 277 289 298 330 354 379 Solution: a) H 0 : N ?, ? H 1 : Not Normal Because the mean and standard deviation are unknown, this is a Lilliefors problem. Note that data must be in order for the Lilliefors or K-S method to work. From the data we found that x 294 .444 and xx s 52.6548 . t . F t actually is computed from the Normal table. For example s Fe 219 Px 219 Pz 1.43 Pz 0 P1.43 z 0 .5 .1664 .3336 . D is the difference (absolute value) between the two cumulative distributions. O x Row O Fe FO t D n 1 219 -1.43 0.0764 1 0.111111 0.11111 0.034711 2 235 -1.13 0.1292 1 0.111111 0.22222 0.093022 3 269 -0.48 0.3156 1 0.111111 0.33333 0.017733 4 277 -0.33 0.3707 1 0.111111 0.44444 0.073744 5 289 -0.10 0.4602 1 0.111111 0.55556 0.095356 6 298 0.07 0.5279 1 0.111111 0.66667 0.138767 7 330 0.68 0.7517 1 0.111111 0.77778 0.026078 8 354 1.13 0.8708 1 0.111111 0.88889 0.018089 9 379 1.61 0.9463 1 0.111111 1.00000 0.053700 The maximum deviation is 0.138767. The Lilliefors table for .10 and n 9 gives a critical value of 0.249. Since our maximum deviation does not exceed the critical value, we do not reject H 0 . b) H0 :N 230 ,50 H 1 : Not N 230 ,50 Because the population mean and standard deviation are known, this is a Kolmogorov-Smirnov problem. x z . Row x z Fe O O FO D n 1 219 -0.22 0.4129 1 0.111111 0.11111 0.301789 2 235 0.10 0.5398 1 0.111111 0.22222 0.317578 3 269 0.78 0.7823 1 0.111111 0.33333 0.448967 4 277 0.94 0.8264 1 0.111111 0.44444 0.381956 5 289 1.18 0.8810 1 0.111111 0.55556 0.325444 6 298 1.36 0.9131 1 0.111111 0.66667 0.246433 7 330 2.00 0.9772 1 0.111111 0.77778 0.199422 8 354 2.48 0.9934 1 0.111111 0.88889 0.104511 9 379 2.98 0.9986 1 0.111111 1.00000 0.001400 The maximum deviation is 0.448967. The Kolmogorov-Smirnov table for .10 and n 9 gives a critical value of 0.387. Since our maximum deviation exceeds the critical value, reject H 0 . 13 4/16/02 252y0231 5. (Weirs) The following data gives years of membership and numbers of shares (in thousands) owned for 8 board members of our corporation. Numbers are the dependent variable and years is the independent variable. Data Display Row share years 1 2 3 4 5 6 7 8 Total 300 408 560 252 288 650 630 522 3610 6 12 14 6 9 13 15 9 84 years shares squared squared 36 90000 144 166464 196 313600 36 63504 81 82944 169 422500 225 396900 81 272484 968 1808396 Note that n 8 and that you will have to compute xy . a. Compute the regression equation Y b0 b1 x to predict thousands of shares owned on the basis of age. (6) b. On the basis of your regression, how many thousands of shares do you expect to be owned by someone who has been on the board for 20 years ? (1) c. Compute R 2 . (4) d. Compute s e . (3) e. Compute s b0 and do a significance test on b0 .(4) f.. Do an interval that shows the average number of shares that would be owned by someone who has been on the board for 20 years. (3) g. Using your SST etc., put together the ANOVA table (6) x 84 , y 3610 , x 968 and y 1808396 . After all this time, trying to get x by squaring x to get xy by multiplying x by y is inexcusable. We compute x y 41238 (See next page) 2 Solution: 2 2 Spare Parts Computation: x 84 x 10 .5 n 8 y y 3610 451 .25 n x nx 968 810.5 86.0 Sxy xy nx y 41238 810 .5451 .25 SSx 8 2 2 2 3333 .0 SSy y 2 ny 1808396 8451 .25 2 2 179383 .5 SST a) b1 Sxy SSx xy nxy 3333 .0 38.7558 x nx 86.0 2 2 b0 y b1 x 451 .25 38.7558 10.5 44.3140 b) Y b0 b1 x becomes Yˆ 44 .3140 38 .7558 x . So if X 0 20, and Yˆ0 44.3140 38.7558X 0 44.3140 38.7558 20 819 .43 is the number of shares that we forecast for someone who has been on the board for 20 years. 14 4/16/02 252y0231 SSR 129173 .1 xy nxy 38.7558 3333 .0 129173 .1 So R SST .7201 or 179383 .5 xy nxy Sxy 3333 .0 ( 0 R 1 always!) .7201 SSxSSy x nx y ny 86.0179383 .5 c) SSR b1 Sxy b1 2 2 2 R 2 2 2 2 2 2 2 s e2 d) SSE SST SSR 179383 .5 12973 .1 50210 .4 s e2 s e2 s e2 y SSy b1 Sxy n2 xy nxy 179383 .5 38.7558 3333 .0 8368 .4 or ny 2 b1 2 1 R SST 1 R y 2 2 n2 y 2 n2 2 ny n2 ny 2 SSE 50210 .4 8368 .4 or n2 6 x b12 2 nx 2 2 6 1 .7201 179383 .5 8368 .2 or 6 So s e 8368 .4 91 .4790 n2 ( s e2 is always positive!) e) H 0: 0 0 H 1 : 0 0 2 1 x2 8368 .4 1 10 .5 s b20 s e2 n SS 8 86 .0 x b 00 b0 0 44 .3140 t 0 0.408 s b0 s b0 108 .51 11774 .144 sb0 11774.144 108.51 Assume that .05 and Make a diagram. Show an almost 6 6 normal curve and that the 'reject region is below t.n2 t .025 2.447 or above t.n2 t.025 2.447 . 2 2 Since 0.408 is between these values, do not reject H 0 . Conclude that f) We found in b) that if x 20 , Yˆ 819.43 . 0 1 s 2yˆ s e2 0 n x 0 x x 2 0 is insignificant. 0 2 nx 2 2 s 2 1 x 0 x e n SS x 2 8368 .4 1 20 10 .5 8 86.0 8828 .0 So Y0 Yˆ0 t 2 s y0 819 .43 2.447 99 .1363 819 243 . s y0 8828.0 99.1363 . g) From the previous page or above, SSR 129173 .1 , SST 179383 .5 and SSE 50210 .4 . H 0 is that there is no relation between Y and X . Source SS DF MS F F.05 Regression 129173.1 Error (Within) Total 50210.4 179383.5 1 129173.1 6 7 8368.4 15.436 F 1,6 5.99 ns Since the computed F is larger than the table F, reject H 0 . Appendix: Computation of column sums. Row i 1 2 3 4 5 6 7 8 Sum share years C3 C4 2 xy 1800 4896 7840 1512 2592 8450 9450 4698 41238 y x x 300 408 560 252 288 650 630 522 3610 6 12 14 6 9 13 15 9 84 36 144 196 36 81 169 225 81 968 C5 y2 90000 166464 313600 63504 82944 422500 396900 272484 1808396 15 4/16/02 252y0231 It's worthwhile looking at the computer output for this exercise. MTB > RETR 'C:\MINITAB\2X0231-4.MTW'. (Retrieves previously stored data) Retrieving worksheet from file: C:\MINITAB\2X0231-4.MTW Worksheet was saved on 4/12/2002 MTB > Execute 'C:\MINITAB\252SOLS.MTB' 1. (Executes previously stored commands) Executing from file: C:\MINITAB\252SOLS.MTB Regression Analysis The regression equation is share = 44 + 38.8 years Predictor Constant years Coef 44.3 38.756 s = 91.48 Stdev 108.5 9.864 R-sq = 72.0% t-ratio 0.41 3.93 p 0.697 0.008 R-sq(adj) = 67.3% Analysis of Variance SOURCE Regression Error Total DF 1 6 7 SS 129173 50210 179383 MS 129173 8368 F 15.44 p 0.008 16