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Your answer is incorrect. Observed monthly heating cost: Your answer is incorrect. Predicted monthly heating cost: Your answer is incorrect. Residual: Your answer is incorrect.

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.…

sample of trucks and their "static weight" and "weight in motion" weights are in thousands of pounds. What percent of the variability in static weight can be explained by the linear model rounded to the nearest 10th?

Please please how to write if based on the following

Q1 Data displayed in this graph were standardized. True or false? Q2 Based on this graph, one can accurately estimate shoe size heritability to be 0.8676. True or false?

I have no idea how to fill in the blank in this regression output... please help

27. Below is some of the regression output from a regression of the price of a copier (expressed in $'s) based on the speed (pages per minute) and whether it copies in color or not (color=0 if it does NOT copy in color and =1 if it does copy in color) SUMMARY OUTPUT                             Regression Statistics             Multiple R 0.919           R Square 0.845           Adjusted R Square 0.801           Standard Error 35.80           Observations 10                         ANOVA               df SS MS F Significance F   Regression 2 48879.2 24439.6 19.1 0.0014   Residual 7 8969.7 1281.4       Total 9 57848.9                         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 238.3…

3. The regression model below estimated the impact of tax on income using 41 observations. a. Is the slope parameters of variable of tax significant at 5 percent level? Explain. b. What the R-square in this regression indicates. const tax Model 1: High-Precision OLS, using observations 1-41 Dependent variable: income Coefficient 1.78191 7.00074 Mean dependent var S.D. dependent var Sum squared resid S.E. of regression R-squared Adjusted R-squared F(1,49) P-value(F) Std. Error 0.880324 0.0575306 T-ratioPvalue 2.024 121.7 0.0484 <0.0001 7.055 8.30 1.14 4.82 9.97 9.96 1.48 1.79 **

Table Analyzed HVC Volume Two-way ANOVA Ordinary Alpha 0.05 Source of Variation. Ager Gender. Interaction 3.893. 0.1020 0.8341 ns 69.29 0.0001 ANOVA table SS DF Interaction 0.01103 11 0.0002890 0.1962 1 0.07566 12 Ager Gender Residual of total variation P value P value summary Significant? 0.2107 ns Difference between column means Mean of Male Mean of Female Difference between SE of difference. 95% CI of difference. Interaction CI Difference between row means. Mean of 25 d 0.1725 Mean of 55 d 0.1810 Difference between SE of difference 95% CI of difference Data summary means 0.2875 0.06600 means 0.2215 0.03970 0.03970 Mean diff, A1 B1 0.1690 0.2740 Mean diff, A2 B2 (A1-B1) (A2-B2) 95% CI of difference. (B1A1) (B2A2) 95% CI of difference. MS Number of columns (Gender) Number of rows (Ager) Number of values. 16 F (DFn, DFd) 0.01103 F (1, 12) 1.749 0.0002890 0.1962 F (1, 12)- 31.13 0.006305 1 No No Yes 0.1350 to 0.3080 -0.008500 -0.09500 to 0.07800 -0.1050 -0.2780 to 0.06801 0.1050…

45. Table 1.2 shows the mean annual compensation of construction workers. bab Abor Cesaione TABLE 1.2 Construction Workers' Average Annual Compensation Annual Total Compensation (dollars) Year 1999 42,598 2000 44,764 2001 47,822 2002 48,966 Source: U.S. Bureau of the Census, Statistical Abstract of the United States, 2004-2005. (a) Find the linear regression equation for the data. (b) Find the slope of the regression line. What does the slope represent? (c) Superimpose the graph of the linear regression equation on a scatter plot of the data. (d) Use the regression equation to predict the construction workers' average annual compensation in the year 2008. Tabl

The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood Pressure SE 20986 339.28 Age Blood sugar 209.2 Blood pressure 207.2 S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5% Coef Constant Analysis of Variance Source DF Regression 3 Residual Error. 4 Total 7 SS Coef 2912 71.95 179.3 225.4 23863180 3703570 27566750 T 7.21 4.72 * 0.92 MS 7954393 925892 F *** P 0.002 0.009 0.308 ** P 0.032 a) What is dependent and independent variables? b) Fully write out the regression equation. c) Fill in the missing values **, ****, and *****. d) Hence test whether & is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis.

I need help with this question   1. Are the main effects significant? Explain

Define Residuals or errors in Alternative Regression Models?

1) What is the line of best fit for this data? a. y =-1.11x+ 11.83; r=-0.9760964904 Average Speed (mi/h) Time (hours) b. y 11.83x- 1. 11; r=0.9527643586 8.5 2.5 c. y= 11.83x –- 1. 11; r=-0.9760964904 7.5 3.75 d. y =-1.11x+ 11.83; r= 0.9527643586. 6.5 4.5 6.0 5.0 5.5 5.5 5.0 6.25 4.0 6.75 3.5 8.75 r notes MacBook Pro * 2$ & 4 5 6 7 8 9. %3D deletc { [ Y ] G H J K L > ? C V N M + || .. .. B

Why this type of plot is used in analysing regression models

5) Can i get help with this question please