18 complete)Where the number in parentheses is the homoskedastic standard error for the regression coefficient B,(0.069)Suppose you wanted to test the hypothesis that B, equals zero at the 1%, 5% and 10% level. That is,Ho: B1=0 vs. H1: B1#0%3DReport the t-statistic and p-value for this test.The t-statistic is(Round your response to two decimal places)The p-value is(Round your response to three decimal places)Can you reject the null hypothesis at the 5% significance level?O A. Yes.B. No.Construct a 95% confidence interval for the slope coefficient B,.The 95% confidence interval for the slope coefficient B, is [ D(Round your response to three decimal places)Click to select your answer(s).d.DELLP Type here to search What-If Forecasta日UngAdvancedColumns cOAnalysis SheetESubData TypesSort & FilterData ToolsForecastH.一KLNSUMMARY OUTPUTRegression StatisticsMultiple R0.009746989R Square9.50038E-05Adjusted R Square-0.010108108Standard Error1.798411671Observations100ANOVAdfSSMSSignificance FRegression0.0301152560.030115256 0.0093112570.923324618Residual98316.95988473.234284538Total99316.99t StatP-valueLower 95%Upper 95%Lower 95.0% Upper 95.0%CoefficientsStandard Error13.0336395114.019421513.0336395114.01942150.2483744454.46023549 4.51479E-75Intercept13.52653051-0.0066804770.06923143 -0.096494857 0.923324618 -0.144067996 0.130707042 -0.144067996 0.130707042DistanceLL

Compute the test statistic by substituting b1=-0.0067 and s.eb1=0.0692 from the output table in the…

SUMMARY OUTPUT Regression Statistics Multiple R 0.009746989 R Square 9.50038E-05 Adjusted R Square -0.010108108 Standard Error 1.798411671 Observations 100 ANOVA df SS MS Significance F Regression 0.030115256 0.030115256 0.009311257 0.923324618 Residual 98 316.9598847 3.234284538 Total 99 316.99 Lower 95.0% Upper 95.0% 14.0194215 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 13.52653051 0.24837444 54.46023549 4.51479E-75 13.03363951 14.0194215 13.03363951 -0.006680477 0.06923143 -0.096494857 0.923324618 -0.144067996 0.130707042 -0.144067996 0.130707042 Distance

SUMMARY OUTPUT Regression Statistics Multiple R 0.009746989 R Square 9.50038E-05 Adjusted R Square -0.010108108 Standard Error 1.798411671 Observations 100 ANOVA df SS MS Significance F Regression 0.030115256 0.030115256 0.009311257 0.923324618 Residual 98 316.9598847 3.234284538 Total 99 316.99 Lower 95.0% Upper 95.0% 14.0194215 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 13.52653051 0.24837444 54.46023549 4.51479E-75 13.03363951 14.0194215 13.03363951 -0.006680477 0.06923143 -0.096494857 0.923324618 -0.144067996 0.130707042 -0.144067996 0.130707042 Distance

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
bservations on years of education and hourly wages are sampled for 32 individuals and the regression wage=f(education) yields estimates β^1=12.5 and β^2=3.7. At α=0.01 what is the critical value for a left-tailed test of β2? A. 2.750 B. 5% C. none of the answers provided are correct because a left-tailed t-test has a negative critical value. D. 1.697 E. 2.457
n a regression analysis involving 25 observations, the following estimated regression equation was developed.ŷ = 10 – 18x1 + 3x2 + 14x3Also, the following standard errors and the sum of squares were obtained.Sb1 = 3 Sb1 = 6 Sb1 = 7SST = 4,800 SSE = 1,296If we are interested in testing for the significance of the relationship among the variables (i.e., significance of the model), the critical value of F at α = .05 is _____. a. 3.10 b. 3.07 c. 2.78
In a random sample of 235 registered voters, 191 said they planned on voting in the next presidential election. Test the claim that more than 75% of registered voters plan on voting. Use a 0.01 level of significance. Find the P-value for a right tailed test that produces a test statistic of z = 1.945. What does rejection of H0: ρ = 0 imply for the regression equation yˆ=b0+b1xy^=b0+b1x? Explain in 1 – 2 complete sentences.
5) The following results are from data where the dependent variable is SALARY, the independent variables are AGE, EDUCATION, and FEMALE which is a dummy variable = 1 for females and = 0 for males. e) What is the null and alternative hypothesis that Excel is testing in reporting the t-stats above? f) Is the regression equation as a whole significant at the 1% level of significance? How do you know? g) What is the effect on the income of a person being a female versus male? h) Is there a significant amount of evidence (say at the 10% level of significance) that an additional year of education increases income by more than $2000? Be sure to state your null and alternative hypotheses, calculate your test statistic, and find the critical value.
Researchers are investigating the association betweenthe temperature in degrees and sales sales of hot dogs(in hundreds) at an outdoor sports stadium for a randomsample of days. They constructed a 95% confidenceinterval for the slope of the regression line for predictingSales from Temperature, obtaining (-0.24, 1.68). Whichstatement is correct?A) There is not a significant association because theinterval contains 0.B) The association is probably positive, because most ofthe interval is above 0.C) The association must be positive, because the intervalcontains 1.D) The researchers cannot make any conclusion, becausethe margin of error is too large.E) The researchers made an error, because the upperendpoint is greater than 1.
Just parts d,e anf f of question 1 d) We conduct a simple regression of size on hhinc, now using robust standard errors. The regression output is reported in Table 2. Why are the estimated coefficients in Table 2 equal to the estimated coefficients in Table 1? Do the conclusions on the statistical significance at 5% of the coefficient of hhinc change between Tables 1 and 2? e) Now, using both the critical value and the p-value, test whether the coefficient of hhinc is statistically significant at 1%, by using the results in Table 1. Repeat the test by using the results in Table 2. Compare the results based on Table 1 with the results based on Table 2. Consider the results in Table 2. Compute the 99% confidence interval for hhinc. f) Consider the results in Table 2.How would you decide whether 0 is contained in the 99% confidence interval for the coefficient of hhinc, without actually computing the confidence interval?
Which of the following expressions is the correct way to express an interpretation for an OLS regression coefficient of -0.9? - This question is based on Data Analysis A. For every one unit increase in X there is - on average - a 0.9 unit decrease in Y. B. For every one unit increase in Y there is - on average - a 0.9 unit decrease in X. C. For every one unit increase in Y there is - on average - a 0.9 unit increase in X. D. For every one unit increase in X there is - on average - a 0.9 unit increase in Y.
In a regression analysis involving 25 observations, the following estimated regression equation was developed.ŷ = 10 – 18x1 + 3x2 + 14x3Also, the following standard errors and the sum of squares were obtained.Sb1 = 3 Sb1 = 6 Sb1 = 7SST = 4,800 SSE = 1,296If we are interested in testing for the significance of the relationship among the variables (i.e., significance of the model), the critical value of F at α = .05 is _____.
The head width (in) and weight (lb) is measured for a random sample of 20 bears.The data shows that the mean head width is 6.9 inches, mean weight is 214.3 lb, and thecorrelation r = 0.879 and its p-value is less than 0.0001. The suggested linear regressionequation is WEIGHT = -212 + 61.9 WIDTH.(a) How is the best predicted weight value of a given head width found with this data found?(b) For the preceding part, why?(c) Find the best predicted weight given a bear with a head with of 6.5 inches.
The attached results are for a multiple regression study of smartphone addiction (SSA-SV) proneness in relation to 1) Gender 2)Age 3) Anxiety (GAD-7). I just want to clarify that age is statistically significant with p = 0.006; is this correct? What's throwing me off is the notation "< .001" on the top line under the p category. I was under the impression that the p-value would need to be less than 0.001.
The value of the sample correlation coefficient are for these data is approximately -0.642. Answer the following. Carrier intermediate computations to at least four decimal places, and Roger answers as specified. a. What is the value of the slope of the least squares regression line for these data? Round your answer to at least two decimal points.b. What is the value of the y-intercept of the least squares regression line for these data? Round your answer to at least two decimal places.