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:...
What are the implications of predictability results in Part 2 and 3 for investment decisions? Part...
What are the implications of predictability results in Part 2 and 3 for investment decisions? Part 2 use log dividend-price ratio to predict the 5-year stock market excess log returns: lm(formula = lnexret[2:t] ~ dp[1:t - 1]) Residuals: Min 1Q Median 3Q Max -0.54389 -0.07305 0.01977 0.10712 0.34107 Coefficients: Estimate Std. Error t value (Intercept) 0.58469 0.12768 4.579 dp[1:t - 1] 0.13510 0.03771 3.582 Pr(>|t|) (Intercept) 1.58e-05 *** dp[1:t - 1] 0.000567 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’...
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...
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...
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