1.
Sample slope : -0.014.
p-value : 0.245
A p-value of 0.245, which is more than the significance level of 0.05 indicates the acceptance of null hypothesis. This shows that the model is effective.
Yes, X appears to be the effective predictor of the response variable Y.
2.
slope : -4.31
Intercept : 810
Computer output for fitting a simple linear model is given below.State the value of the sample...
Computer output for fitting a simple linear model is given below. State the value of the sample slope for this model and give the null and alternative hypotheses for testing if the slope in the population is different from zero. Identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model. The regression equation is Y=82.0-0.0116X. Predictor Coef SECoef T P. Constant 81.98 11.76 6.97 0.000 X -0.01161...
Chapter 9, Section 1, Exercise 002 Use the computer output to estimate the intercept BO and the slope B1. The regression equation is Y=806-2.5X . Predictor Coef SE Coef T P Constant 806.277 87.64 9.20 0.000 -2.504 0.821 -3.05 0.006 Intercept BO: Slope B1: Don't show me this message again for the assignment Click if you would like to Show Work for this question:Open Show Work
Use the computer output to estimate the intercept ßo and the slope B, . The regression equation is Y = 807 – 2.89X. Predictor Coef SE Coef I P Constant 806.724 87.69 9.20 0.000 -2.889 0.947 -3.05 0.006 Intercept ßo : ETHEL Slope B
Question 1 View Policies Current Attempt in Progress Use the computer output to estimate the intercept B, and the slope B1- The regression equation is Y = 25.5 +3.09X. Predictor Coef SE Coef T P Constant 25.455 5.498 4.63 0.000 3.089 0.4652 6.64 0.000 Intercept Bo : Slope Bi : Attempts: 0 of 4 used Submit Arowe
Computer output for fitting a simple linear model is given below. State the value of the sample slope for the given model. In testing if the slope in the population is different from zero, identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model. Coefficients: Estimate Std.Error t value Pr(>|t|) (Intercept) 821.91 88.38 9.30 0.000 A -3.804 1.247 -3.05 0.006 Sample slope: Enter your answer; sample slope p-value:...
Chapter 9, Section 1, Exercise 001 Use the computer output to estimate the intercept beta Subscript 0 and the slope beta Subscript 1. The regression equation is Upper Y equals 24.0 plus 4.89 Upper X. Predictor Coef SE Coef T P Constant 24.038 5.192 4.63 0.000 Upper X 4.886 0.7358 6.64 0.000 Intercept beta Subscript 0 Baseline colon Slope beta Subscript 1 Baseline colon
Chapter 9, Section 1, Exercise 001 XIncorrect. Use the computer output to estimate the intercept β0 and the slope The regression equation is Y 26.5 +3.19X Predictor Coef SE Coef T Constant 26.5445.733 4.63 0.000 3.189 0.4803 6.64 0.000 24.145 Intercept βο Slope 1 Click if you would like to Show Work for this question: Open Show Work
Chapter 9, Section 1, Exercise 008 Computer output for fitting a simple linear model is given below. State the value of the sample slope for the given model. In testing if the slope in the population is different from zero, identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model. Coefficients: Estimate Std.Error t value Pr(>Itl) Intercept) 820.15 88.19 9.30 0.000 -3.616 .186 -3.05 0.006 Sample slope p-value...
Chapter 9, Section 1, Exercise 002 Use the computer output to estimate the intercept β0 and the slope β- The regression equation is Y 823 - 3.67X. Predictor Coef SE Coef Constant 822.899 89.45 9.20 0.000 -3.674 1.205-3.05 0.006 intercept β0 : Slope 1 We were unable to transcribe this imageChapter 9, Section 1, Exercise 006 tem ute autput a rting a simpla inir medal gi en elo Th2 regressor cquation Y = 81.8-0.0151x. State :he walua af the Sandle...
Consider the following partial computer output from a simple linear regression analysis. Predictor Coef SE Coef T P 4.8615 9.35 0.5201 0.000 Constant -0.34655 0.05866 Independent Var S = .4862R-Sq| Analysis of Variance SS MS Source DF F Regression 1 34.90 Residual Error 13 Total 14 11.3240 Calculate the MSE Consider the following partial computer output from a simple linear regression analysis. Predictor Coef SE Coef T P 4.8615 9.35 0.5201 0.000 Constant -0.34655 0.05866 Independent Var S = .4862R-Sq|...