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- What is the R2 value for the relationship between Height and Weight?Given are five observations collected in a regression study on two variables. xi 2 6 9 13 20 yi 7 18 9 26 23 Compute b0 and b1 (to 1 decimal).b1 b0 Complete the estimated regression equation (to 1 decimal).^y = + x Use the estimated regression equation to predict the value of y when x = 6 (to 1 decimal).^y =Suppose that you run a correlation and find the correlation coefficient is 0.338 and the regression equation is ˆy=−12.7+4.3xy^=-12.7+4.3x.The mean for the xx data values was 7, and the mean for the y data values was 17.A T Test for the slope of the regression line is performed, and the p-value is greater than the level of significance of 0.05. Use the appropriate method to predict the y value when x is 4.4.
- 29)8- According to the summary result of linear regression model between A and B obtained from R given below, we can fit a regression line. Assume that A has any value. If we decrease the value of A by 3, how would Y be affected? Call: Im (formula = B - A) Residuals: Min 10 Median 30 Маx -16.340 -10.793 -9.653 -8.502 58.325 Coefficients: Estimate Std. Error t value Pr (>[t]) (Intercept) 19.6315 20.6457 0.951 0.373 A 9.9609 0.3717 26.800 2.58e-08 *** --- Signif. codes: O *** 0.001 1** 0.01 1** 0.05 '.' 0.1 '' 1 Residual standard error: 26.46 on 7 degrees of freedom Multiple R-squared: 0.9903, Adjusted R-squared: 0.989 F-statistic: 718.2 on 1 and 7 DF, p-value: 2.58le-08 a) 49.5142 decrease b) 29.8827 increase c) 58.8945 decrease 29.8827 decrease 58.8945 increase24. A bivariate regression was conducted to determine the time to failure (seconds) based on the number of push-ups conducted, what term describes when the line of best fit crosses the vertical axis? Outlier Y-intercept Intercept
- The article "The Undrained Strength of Some Thawed Permafrost Soils"+ contained the accompanying data on the following. y = shear strength of sandy soil (kPa) x₁ = depth (m) x₂ = water content (%) The predicted values and residuals were computed using the estimated regression equation y = -156.50 - 16.52x₁ + 13.83x₂ + 0.100x3 -0.258x4 +0.496x5 ₁², x₁ = x₂², and x5 = X1X2• where x3 = y X1 14.7 8.8 31.4 48.0 36.5 26.9 25.6 36.9 25.8 x2 10.0 6.2 39.2 16.0 6.8 39.3 20.7 16.8 7.0 38.4 7.4 33.8 38.8 8.3 33.9 16.9 6.4 27.0 8.1 33.2 24.9 10.0 12.8 28.0 16.0 4.4 26.2 37.9 7.3 2.8 34.7 1.9 36.5 Predicted y 22.86 46.10 27.38 11.23 13.47 16.80 23.55 25.24 15.75 24.47 15.19 29.93 15.51 8.00 Residual -8.16 1.90 -1.78 -1.23 2.53 0.00 -2.85 13.56 1.15 2.53 0.81 -5.03 -8.21 4.80 (a) Use the given information to calculate SSResid, SSTO, and SSRegr. (Round your answers to four decimal places.) SSTO = SSResid = SSRegr = (b) Calculate R² for this regression model. (Round your answer to three decimal…10. You estimated a regression with the following output. Source | SS df MS Number of obs = 333 -------------+---------------------------------- F(1, 331) = 4608.21 Model | 32636494.1 1 32636494.1 Prob > F = 0.0000 Residual | 2344225.8 331 7082.25316 R-squared = 0.9330 -------------+---------------------------------- Adj R-squared = 0.9328 Total | 34980719.9 332 105363.614 Root MSE = 84.156 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 30.79902 .4537022 67.88 0.000 29.90652 31.69153 _cons | 20.85313 42.1964 0.49 0.621 -62.1538 103.8601…If the absolute value of regression coefficient of a predictor in SLR is more than that of predictor, when a second predictor is added, what attribute describes the relationship between the original variable and the added variable Answer in few lines .not very big answer needed
- 4. A simple linear regression is fit to a dataset. Unfortunately, the corresponding ANOVA table is not complete because some quantities in the table are missing due to unknown digital errors. Calculate the missing values, denoted by "?", in the ANOVA table based on the other available values. Is this regression model significant (α = 0.05)? Source Regression Residual Total df ? ? 21 SS ? ? ? MS = SS/df ? 1.13 F-Ratio 430.65 p-value ?You estimate a regression equation Y = a + b1X1 + b2X2 + b3X3 on a data set of 300 observations and obtain the following results: R-squared = 0.83 standard error of regression = 45.O F-statistic = 93.4 (p-value = .0007) Coefficient Estimates t-statistic for Estimate p-value in(_) a = 100.0 b1 = 25.0 12.53 (p < .001) 0.82 (p - 0.61) 4.73 (p < .01) 14.63 (p < .001) The null hypothesis that b1 = b2 = b3 = 0. b2 = 15.0 b3 = 10.0 is strongly rejected using 5% critical value cannot be rejected using 5% critical value is in the range of no clear determination using 5% critical valueHI, I'm doing a regression output with Hayes Model 1 Based on the information below the 95% CI [-.6020, .065] is statistically significant but the p = .114 is this a correct assumption? (b1) of -.268, t = -1.58, p = .114, 95% CI [-.6020, .065]. I asked this question originally with the wrong numbers sorry I corrected it... thank you