1)Write the regression equation that represents the above equation. Y = 9.804326378 + 0.65795*X1 + 0.200358 X2 Height of an individual = 9.8043 + 0.6579*Mother's Height + 0.2004* Father's Height 2)Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)? Regression StatisticsMultiple RR SquareAdjusted R SquarStandard Error0.6310719920.3982518590.3657249322.914527039Observations40ANOVAdfSSMSF Significance FRegressionResidual208.0084392 104.0042 12.24376 8.30181E-0537314.2953108 8.494468Total39522.30375CoefficientsStandard Errort StatP-value9.804326378InterceptMother's HeightFather's Height12.39987353 0.79068 0.434170.6579528150.147476295 4.461414 7.34E-050.2003584370.138223638 1.449524 0.155615

The F test is used to test whether the regression model is effective or not. In this test the null…

Write the regression equation that represents the above equation. Y = 9.804326378 + 0.65795*X1 + 0.200358 X2 Height of an individual = 9.8043 + 0.6579*Mother's Height + 0.2004* Father's Height 2)Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)?

Write the regression equation that represents the above equation. Y = 9.804326378 + 0.65795X1 + 0.200358 X2 Height of an individual = 9.8043 + 0.6579Mother's Height + 0.2004* Father's Height 2)Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)?

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 5PPS

See similar textbooks

Similar questions

A) Which point from the data has the largest residual? (x,y) B) Explain what the residual means in context. Is this point an outlier? An influential point? The residual means that when the swim time is______, the observed heart rate is about _____ beats less than the predicted rate. When this point is removed, it has an effect on the regression line, so it is influential. The point is not an outlier, because the residual is less than twice the standard deviation.
Using a TI 84 calculator find the least square regression line, treating the length of the right humerus x as the explanatory, variable, and the length of the right tibia as the response variable
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7193267530.719326753 R Square 0.5174309770.517430977 Adjusted R Square 0.4991055710.499105571 Standard Error 8.6995238488.699523848 Observations 165165 ANOVA dfdf SSSS MSMS F� Significance F� Regression 66 12,821.56512,821.565 2136.9282136.928 28.235728.2357 8.9037E-238.9037E-23 Residual 158158 11,957.71111,957.711 75.68275.682 Total 164164 24,779.27624,779.276 Step 1 of 2 : How many independent variables are included in the regression model
Determine whether the intercept coefficient (1664.46) is statistically significant at the 5 percent level. SUMMARY OUTPUT Regression Statistics Multiple R 0.971 R-Square A Adjusted R-Square B Standard Error 30.462 Observations 51 ANOVA df SS MS F Significance F Regression C 747851.57 373925.79 402.98 9.89E-31 Residual 48 D 927.91 Total 50 792391.11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95%…
Which of the following is FALSE? * The regression model assumes the errors (residuals) are normally distributed. Data point below the regression line, the residual is negative. The errors (residuals) in a regression model are assumed to have a zero mean. The errors (residuals) in a regression model are assumed to have increasing mean. In regression analysis, the regression line Minimize the sum of distances between the actual value x and the predicted x values. Minimize the perpendicular distance between the regression line and each data point. Minimize the sum of the squared residuals between the actual x and the predicted x values. Minimize the sum of the squared residuals between the actual y and the predicted y values. The smaller the distance between observed data points and the regression line, the smaller the sum of squares regression (SSR). TRUE FALSE
It appears that there is a significant relationship between cyberbullying (X) and internet trolling (Y). Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .889a .790 .748 1.50555 a. Predictors: (Constant), CyberBullying ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 42.667 1 42.667 18.824 .007b Residual 11.333 5 2.267 Total 54.000 6 a. Dependent Variable: InternetTrolling b. Predictors: (Constant), CyberBullying Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .333 1.639 .203 .847 CyberBullying 1.333 .307 .889 4.339 .007 a. Dependent Variable: InternetTrolling State hypothesis for the correlation: Is the correlation significant using α = .05? State the conclusion in APA format.…
A) Which point from the data has the largest residual? B) Explain what the residual means in context. Is this point an outlier? An influential point? The residual means that when the swim time is______, the observed heart rate is about _____ beats less than the predicted rate. When this point is removed, it has an effect on the regression line, so it is influential. The point is not an outlier, because the residual is less than twice the standard deviation.
Instrumental variables regression is a way to estimate regression coefficientswhen one or more regressors are correlated with the error term. Explain How?
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is
Simple linear regression results: Dependent Variable: Grade (Marks)Independent Variable: AbsencesGrade (Marks) = 89.100971 - 3.1906331 AbsencesSample size: 30R (correlation coefficient) = -0.93164951R-sq = 0.86797081Estimate of error standard deviation: 3.2600128 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 89.100971 1.1464745 ≠ 0 28 77.717357 <0.0001 Slope -3.1906331 0.23516911 ≠ 0 28 -13.567399 <0.0001 Analysis of variance table for regression model: Source DF SS MS F-stat P-value Model 1 1956.2835 1956.2835 184.07431 <0.0001 Error 28 297.57514 10.627684 Total 29 2253.8587 a) Interpret the slope in terms of abscences and grade. b) Does y-intercept have meaning? If so, what is it?
Which of the following is FALSE? * The residuals in a regression model are assumed to have a zero mean. Data point below the regression line, the residual is negative. The residuals in a regression model are assumed to have increasing mean. The regression model assumes the residuals are normally distributed.
The results shown to the right provide the X-values, residuals, and a residual plot from a regression analysis. Is there any evidence of a pattern in Kthe residuais? Explain Choose the correct answer below OA You Befeudus show decreasing variation OB. Yes, the residuals show a distinct cyclical pattom OC No, the residuals appear to be randomly spread OD Yes, the residuais show a disdect curved pattern 2 3 4 X 1 5 6 7 19 10 Residuals 0.69 -0.88 116 -0.66 242 -0.79 0.20 163 -111 178 WAPDAMy 2 14 off O (25