Homework Answers
f)
TS = (0.96683 - 2)/0.18292
= -5.648206
critical values are -1.96 and 1.96
|TS| > critical value
hence we reject the null hypothesis
g)
Anova table is already printed
anova(ls)
Add Answer to:
> summaryCls) Call: Lm(formula y X) Residuals: -0.20283 -0.146910.02255 0.06655 0.44541 Coefficients: (Intercept) 0.36510 0.09904 3.686...
Using R output provided 1). Perform hypothesis testing for B(beta)1=2 using A(alpha)=0.05 > summary(ls) Call: Residuals:...
Using R output provided
1). Perform hypothesis testing for B(beta)1=2 using
A(alpha)=0.05
> summary(ls) Call: Residuals: Min 1Q Median 3Q Max 0.20283 -0.14691 -0.02255 0.06655 0.44541 Coefficients: (Intercept) 0.365100.099043.686 0.003586 ** Signif. codes: 0 '***' 0.001 '0.01 '*'0.05 '.' 0.1''1 Estimate Std. Error t value Pr>Itl) 0.96683 0.18292 5.286 0.000258** Residual standard error: 0.1932 on 11 degrees of freedom Multiple R-squared 0.7175, Adjusted R-squared: 0.6918 F-statistic: 27.94 on 1 and 11 DF, p-value: 0.0002581 anovaCLs) Analysis of Variance Table Response:...
Call: lm(formula = launch_speed ~ launch_angle, data = muncy) Residuals: Min 1Q Median 3Q Max...
Call: lm(formula = launch_speed ~ launch_angle, data = muncy) Residuals: Min 1Q Median 3Q Max -64.802 -9.009 2.401 10.821 20.709 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 86.95164 0.78064 111.385 < 2e-16 *** launch_angle 0.20804 0.02865 7.261 1.77e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13.74 on 438 degrees of freedom Multiple R-squared: 0.1074, Adjusted R-squared: 0.1054 F-statistic: 52.72 on 1 and 438 DF, p-value:...
2.-Interpret the following regression model Call: lm(formula = Sale.Price ~ Lot.Size + Square.Feet + Num.Baths + API.2011 + dis_coast + I(dis_fwy * dis_down * dis_coast) + Pool, data = Train...
2.-Interpret the following regression model Call: lm(formula = Sale.Price ~ Lot.Size + Square.Feet + Num.Baths + API.2011 + dis_coast + I(dis_fwy * dis_down * dis_coast) + Pool, data = Training) Residuals: Min 1Q Median 3Q Max -920838 -84637 -19943 68311 745239 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.375e+05 7.138e+04 -10.332 < 2e-16 *** Lot.Size -5.217e-01 1.139e-01 -4.581 5.34e-06 *** Square.Feet 1.124e+02 1.086e+01 10.349 < 2e-16 *** Num.Baths 3.063e+04 9.635e+03 3.179 0.00153 ** API.2011 1.246e+03 8.650e+01 14.405 < 2e-16...
It has been established for a long time that height has a positive correlation with weight....
It has been established for a long time that height has a positive correlation with weight. As people gets taller their weight increases. In a research study, a linear regression model was proposed to predict weight based on height. R output below provides the analysis. Interpret it, list any strengths and limitations of the result. Call: lm(formula = Weight ~ Height) Residuals: Min 1Q Median 3Q Max -6.7104 -2.9217 0.4276 2.3973 7.8586 Coefficients: Estimate Std. Error t value...
How do I interpret the p-values in terms of rejecting or failing to reject H0 at...
How do I interpret the p-values in terms of rejecting or failing
to reject H0 at a 95% confidence level? What does the intercept
column mean in terms of p-value? How does the p-value of the F test
compare and what does it mean? In the simple linear regression I'd
conclude age isn't related to pulmonary disease (what does
intercept p-value mean) but for the multiple regression I'd say age
and height aren't related to pulmonary disease but smoking is...
please show your explanation thanks! ## ## Call: ## Im(formula = mpg ~ disp + hp...
please show your explanation thanks!
## ## Call: ## Im(formula = mpg ~ disp + hp + wt + osec, data = mtcars.train.df) ## ## Residuals: Min 1Q Median ## -4.3442 -1.1687 -0.4033 3Q Max 1.0519 5.9623 ## ## Coefficients: Estimate Std. Error t value Pr>t) ## (Intercept) 31.204891 10.909916 2.860 0.00967 ** ## disp 0.009432 0.012308 0.766 0.45245 ## hp -0.032908 0.025528 -1.289 0.21208 ## wt -4.978374 1.434757 -3.470 0.00242 ** ## qsec 0.434043 0.576267 0.753 0.46011 ## ---...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept)...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept) -5.128e+03 1.103e+02 46.49 2e-16** Estimate std. Brror t value Pr(lt|) TEMP PERT TEM: FERT 1.45se-01 9.692e-03 -15.01 1.06e-12 3.110e+01 1.344e+00 23.13 2e-16* 1.397e+02 3.140e+00 44.51 < 2e-16** TEMPSQ FERTSO -1.334e-01 6.853e-03 19.46 6.46e-15 -1.144e+00 2.741e-02 41.74 <2e-16 signif. codes: 00.001 0.01 0.05 011 Residual standard error: 1.679 on 21 degrees of freedom Multiple R-squared: 0.993, F-statistic: 596.3 on 5 and 21 DF, p-value: 2.2e-16...
Regression Analysis Question: is there any relationship between the economic growth rate(GDP) and unemployment rate(Unemp_Rates), Poverty...
Regression Analysis Question: is there any relationship between the economic growth rate(GDP) and unemployment rate(Unemp_Rates), Poverty (Poverty_Rates), Technological and science workforce(ech_Scien_Wforce), high school graduate (HS_Grad_Rates) , and housing cost( Housing_Cost) This is a regression analysis result : Call: lm(formula = GDP_Rate ~ Unemp_Rates + Poverty_Rates + Tech_Scien_Wforce + HS_Grad_Rates + Housing_Cost) Residuals: Min 1Q Median 3Q Max -1.5562 -0.3988 -0.1126 0.4971 1.6748 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 15.822486 5.424619 2.917 0.00555 ** Unemp_Rates -0.053450 0.136427 -0.392...
The R code will help to answer the question. 8. DeGroot&Shervish (2002) consider an experiment to...
The R code will help to answer
the question.
8. DeGroot&Shervish (2002) consider an experiment to study the combined effects of taking a stimulant and a tranquilizer. In this experiment three types of stimulant and four types of tranquilizer are administered to a group of rabbits. Each rabbit received one of the stimulants, then 20 minutes later, one of the tranquilizers. One hour later their response time (in microseconds) to a stimulus was measured. The results were: Tranquilizer Stimulant 1...
Interpreting regression results 2. This is the result of a regression where goals is the dependent...
Interpreting regression results 2. This is the result of a regression where goals is the dependent variable and minutes played is the explanatory variable. a. Write out the simple linear regression equation that predicts goals based on time played using the output displayed here. If the average soccer player played one additional game (90 minutes), how many additional goals would you predict them to have scored? b. Call: 1m(formula goalstimeplayed, data -data) Residuals: Min 1Q Median 3Q Max 5.0572-1.6294 -0.3651...
Using R output provided
1). Perform hypothesis testing for B(beta)1=2 using
A(alpha)=0.05
> summary(ls) Call: Residuals: Min 1Q Median 3Q Max 0.20283 -0.14691 -0.02255 0.06655 0.44541 Coefficients: (Intercept) 0.365100.099043.686 0.003586 ** Signif. codes: 0 '***' 0.001 '0.01 '*'0.05 '.' 0.1''1 Estimate Std. Error t value Pr>Itl) 0.96683 0.18292 5.286 0.000258** Residual standard error: 0.1932 on 11 degrees of freedom Multiple R-squared 0.7175, Adjusted R-squared: 0.6918 F-statistic: 27.94 on 1 and 11 DF, p-value: 0.0002581 anovaCLs) Analysis of Variance Table Response:...
How do I interpret the p-values in terms of rejecting or failing
to reject H0 at a 95% confidence level? What does the intercept
column mean in terms of p-value? How does the p-value of the F test
compare and what does it mean? In the simple linear regression I'd
conclude age isn't related to pulmonary disease (what does
intercept p-value mean) but for the multiple regression I'd say age
and height aren't related to pulmonary disease but smoking is...
please show your explanation thanks!
## ## Call: ## Im(formula = mpg ~ disp + hp + wt + osec, data = mtcars.train.df) ## ## Residuals: Min 1Q Median ## -4.3442 -1.1687 -0.4033 3Q Max 1.0519 5.9623 ## ## Coefficients: Estimate Std. Error t value Pr>t) ## (Intercept) 31.204891 10.909916 2.860 0.00967 ** ## disp 0.009432 0.012308 0.766 0.45245 ## hp -0.032908 0.025528 -1.289 0.21208 ## wt -4.978374 1.434757 -3.470 0.00242 ** ## qsec 0.434043 0.576267 0.753 0.46011 ## ---...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept) -5.128e+03 1.103e+02 46.49 2e-16** Estimate std. Brror t value Pr(lt|) TEMP PERT TEM: FERT 1.45se-01 9.692e-03 -15.01 1.06e-12 3.110e+01 1.344e+00 23.13 2e-16* 1.397e+02 3.140e+00 44.51 < 2e-16** TEMPSQ FERTSO -1.334e-01 6.853e-03 19.46 6.46e-15 -1.144e+00 2.741e-02 41.74 <2e-16 signif. codes: 00.001 0.01 0.05 011 Residual standard error: 1.679 on 21 degrees of freedom Multiple R-squared: 0.993, F-statistic: 596.3 on 5 and 21 DF, p-value: 2.2e-16...
The R code will help to answer
the question.
8. DeGroot&Shervish (2002) consider an experiment to study the combined effects of taking a stimulant and a tranquilizer. In this experiment three types of stimulant and four types of tranquilizer are administered to a group of rabbits. Each rabbit received one of the stimulants, then 20 minutes later, one of the tranquilizers. One hour later their response time (in microseconds) to a stimulus was measured. The results were: Tranquilizer Stimulant 1...
Interpreting regression results 2. This is the result of a regression where goals is the dependent variable and minutes played is the explanatory variable. a. Write out the simple linear regression equation that predicts goals based on time played using the output displayed here. If the average soccer player played one additional game (90 minutes), how many additional goals would you predict them to have scored? b. Call: 1m(formula goalstimeplayed, data -data) Residuals: Min 1Q Median 3Q Max 5.0572-1.6294 -0.3651...
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