Question

The bullsh sentment of individual investors was 27.6% AAr Ja mal, measures were based on a poll conducted by the American Association of Individual Investors. Assume that each bulish sentiment measure was based on a sample of 240 investors a. Develop a 95% conf dence interval for the dmerence between the bullish sentinert measures for the rost recent two weeks (to 3 de deals) February 2009). The bullsh sentment was reported to be 48.7% one week earier and 39.7% one month earter. The sentiment 296 b. Develop hypotheses so that rejection of the mull hypothesis will alow us to conclude that the most recent bullish sentiment is weaker than that of one month earlier Ho: P-Pgreater thes or equel to e conduct a hypothesis test of part (b) using ơ-01. what is your condision, we reject Ho and conclude tht builiss sentiment has declined over the one-menth
Given are five observations for two variables, x and y i 3 6 7 10 14 The estimated regression equation for these data is 9 = 0.2 + 2.6. a. Compute SSE, SST, and SSR using the following equations (to 1 decimal). SSE = Σ(yi-yo SST (y,-5)2 SSE 父 SST 3 SSR b. Compute the coefficient of determination (to 3 decimals). Does this least squares line provide a good fit? Yes, the least squares line provides a very good fit c. Compute the sample correlation coefficient (to 4 decimals).
The following data show the brand, price (5), and the overall score for 6 stereo headphones that were tested by Consumer Reports. The overall score is based on sound noise reduction Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ?-25.857 + 0.291x where x-price (S) and y-overall score. Score Brand Bose Sculicandy Price 76 16071 61 57 30 35 Round your answers to three decimal places 85 70 60 45 JVC a. Compute SST, SSR, and sse SSE b. Compute the coefficient of determinstion.Comment on the goodness of R c. What is the value of the sample correlation coefficient?
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Answer #1

Q2) x and y

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.98270763
R Square 0.965714286
Adjusted R Square 0.954285714
Standard Error 0.894427191
Observations 5
ANOVA
df SS MS F Significance F
Regression 1 67.6 67.6 84.5 0.002722613
Residual 3 2.4 0.8
Total 4 70
Coefficients Standard Error t Stat P-value Lower 95%
Intercept 0.2 0.938083152 0.213200716 0.844836846 -2.785399261
x 2.6 0.282842712 9.192388155 0.002722613 1.699868255

a)

SSE = 2.4

SST = 70

SSR = 67.6

b)

R Square 0.965714286

r^2 = 0.966

c)

Multiple R 0.98270763

r = 0.9827

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