Model 1 ## ## Call: ## 1m (formula = mpg wt, data = mtcars) ## ## Residuals: ## Min 1Q Median 3Q 1.4096 Max 6.8727 ## -4.5432 -2.3647 -0.1252 ## ## Coefficients: ## #3#23 (Intercept) ## wt ## Estimate Std. Error t value Pr(>|t|) ##23 Signif. codes: ## 37.2851 -5.3445 1.8776 19.858 < 2e-16 *** 0.5591 -9.559 1.29e-10 *** *** 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## Residual standard error: 3.046 on 30 degrees of freedom ## Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446 ## F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10 Model 2 ## ## Call: ## 1m(formula = mpg ## ## Residuals: ## Min ~ qsec, data = mtcars) 1Q Median 3Q Max 2.2774 11.6491 ## -9.8760 -3.4539 -0.7203 ## ## Coefficients: ## #3#23 (Intercept) ## qsec ## Estimate Std. Error t value Pr(>|t|) -5.1140 1.4121 10.0295 -0.510 0.6139 0.5592 2.525 0.0171 * ##23 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ## ## Residual standard error: 5.564 on 30 degrees of freedom ## Multiple R-squared: 0.1753, Adjusted R-squared: 0.1478 ## F-statistic: 6.377 on 1 and 30 DF, p-value: 0.01708 Model 3 ## ## Call: ## 1m (formula = ## ## Residuals: ## Min ~ mpg hp, data = mtcars) 1Q Median 30 Max ## -5.7121 -2.1122 -0.8854 1.5819 8.2360 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ##23 (Intercept) 30.09886. 1.63392 18.421 2e-16 *** 1 ## hp -0.06823 0.01012 -6.742 1.79e-07 *** ## ##23 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 1 ## ## Residual standard error: 3.863 on 30 degrees of freedom ## Multiple R-squared: 0.6024, Adjusted R-squared: 0.5892 ## F-statistic: 45.46 on 1 and 30 DF, p-value: 1.788e-07 Model 4 ## ## Call: ## 1m (formula ## ## Residuals: ## Min = mpg ~ qsecwt + hp, data = mtcars) 1Q Median 3Q Max 5.6092 ##23 -3.8591 -1.6418 -0.4636 1.1940 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 27.61053 8.41993 3.279 0.00278 ** ## qsec ## wt ## hp ## 0.51083 0.43922 1.163 0.25463 -4.35880 -0.01782 0.75270 0.01498 -5.791 3.22e-06 *** -1.190 0.24418 ##23 Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1'' 1 ## ## Residual standard error: 2.578 on 28 degrees of freedom ## Multiple R-squared: 0.8348, Adjusted R-squared: 0.8171 ## F-statistic: 47.15 on 3 and 28 DF, p-value: 4.506e-11

I am completely lost please help

For a regression line y^=mx+b, what does the Sum of Squares Due to Error measure? Select the correct answer below: the total variation in x that cannot be explained by variation in y the total variation in y that cannot be explained by the error, or residuals, for x the total variation in y that cannot be explained by the variation in x the total variation in x that cannot be explained by the error, or residuals, for y

Write the linear model to test the hypothesis that there is no treatment effect. Clearly describe each term in the model, and the range of the subscripts. Write the null hypothesis that you are testing.   Call: lm(formula = score ~ list, data = hearing)   Residuals:      Min       1Q   Median       3Q      Max -14.7500  -5.5833  -0.2083   6.3333  16.4167   Coefficients:             Estimate Std. Error t value Pr(>|t|)    (Intercept)   32.750      1.612  20.315  < 2e-16 *** listList2     -3.083      2.280  -1.352  0.17955    listList3     -7.500      2.280  -3.290  0.00142 ** listList4     -7.167      2.280  -3.144  0.00225 ** --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1   Residual standard error: 7.898 on 92 degrees of freedom Multiple R-squared:  0.1382,  Adjusted R-squared:  0.1101 F-statistic: 4.919 on 3 and 92 DF,  p-value: 0.00325

The line of best fit through a set of data isy=21.974+1.021xy=21.974+1.021xAccording to this equation, what is the predicted value of the dependent variable when the independent variable has value 140?   Y=

its not 35,500.

How to do a multi regression model using excel