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 ***
dis_coast -5.164e+00 1.017e+00 -5.078 4.70e-07 ***
I(dis_fwy * dis_down * dis_coast) 1.098e-08 4.236e-09 2.591 0.00973 **
Pool 1.048e+05 2.010e+04 5.211 2.37e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 141400 on 834 degrees of freedom
Multiple R-squared: 0.5527, Adjusted R-squared: 0.549
F-statistic: 147.2 on 7 and 834 DF, p-value: < 2.2e-16Homework Answers
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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...
1.-Interpret the following regression model Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.819e+05 7.468e+04 -10.470 < 2e-16 *** Lot.Si...
1.-Interpret the following regression model Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.819e+05 7.468e+04 -10.470 < 2e-16 *** Lot.Size -5.359e-01 1.163e-01 -4.610 4.67e-06 *** Square.Feet 1.108e+02 1.109e+01 9.986 < 2e-16 *** Num.Baths 2.985e+04 9.650e+03 3.094 0.00204 ** API.2011 1.226e+03 9.034e+01 13.568 < 2e-16 *** dis_coast -7.706e+00 2.550e+00 -3.022 0.00259 ** dis_fwy 1.617e+01 1.232e+01 1.312 0.18995 dis_down 5.364e+00 3.299e+00 1.626 0.10429 I(dis_fwy * dis_down) -4.414e-04 5.143e-04 -0.858 0.39098 Pool 1.044e+05 2.010e+04 5.194 2.59e-07 *** --- Signif. codes: 0 ‘***’ 0.001...
UESTION 7 Fuel efficiency in auto-mobiles can be influences by a number of characteristics. See the...
UESTION 7 Fuel efficiency in auto-mobiles can be influences by a number of characteristics. See the linear regression output below and answer the following questions Results of linear regression analysis are shown below: Call: lm (formula = mpg ~ ., data = auto-mpg) Residuals: Min 1Q Median 3Q Max -8.6927-2.3864 -0.0801 2.0291 14.3607 Coefficients: Estimate Std. Error t value Pr>Itl) (Intercept) -1.454e+01 4.764e+00 -3.051 0.00244* cyl disp hp gvw accel year -3.299e-01 3.321e-01 -0.993 0.32122 7.678e-03 7.358e-03 1.044 0.29733 -3.914e-04...
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...
> ml < lm(grad.rate-Average.loans+SAT.reading.25p+SAT.math.25p+SFR +sector,data-ouryearipeda) sum...
> ml < lm(grad.rate-Average.loans+SAT.reading.25p+SAT.math.25p+SFR +sector,data-ouryearipeda) summary (ml) Call: Im(formulagrad.rateAverage.loansSAT.reading.25p SAT.math.25p SFR +sector, data-fouryearipeda.) Residuals: -54.768 -6.150 0.596 6.601 38.514 Coefficients: Min 1Q Median 3Q Max Estimate Std. Error t value Pr>ltl) -4.331e+01 3.046e+00 -14.221 <2e-16 (Intercept) Average.loans SAT.reading.25p SAT.math.25p SFR sectorPublic, 4-year or above -2.602e+00 7.907e-01 -3.291 0.00103* .573e-03 1.963e-04 8.011 2.77e-15 8.455e-02 1.178e-02 7.177 1.27e-12* 1.086e-01 1.072e-02 10.138 <2e-16 -1.812e-01 9.119e-02 -1.988 0.04710 Residual standard error: 9.855 on 1149 degrees of freedom (1219 observations deleted due toniAnǐngnes Multiple R-squared:...
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:...
UESTION 7 Fuel efficiency in auto-mobiles can be influences by a number of characteristics. See the linear regression output below and answer the following questions Results of linear regression analysis are shown below: Call: lm (formula = mpg ~ ., data = auto-mpg) Residuals: Min 1Q Median 3Q Max -8.6927-2.3864 -0.0801 2.0291 14.3607 Coefficients: Estimate Std. Error t value Pr>Itl) (Intercept) -1.454e+01 4.764e+00 -3.051 0.00244* cyl disp hp gvw accel year -3.299e-01 3.321e-01 -0.993 0.32122 7.678e-03 7.358e-03 1.044 0.29733 -3.914e-04...
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...
> ml < lm(grad.rate-Average.loans+SAT.reading.25p+SAT.math.25p+SFR +sector,data-ouryearipeda) summary (ml) Call: Im(formulagrad.rateAverage.loansSAT.reading.25p SAT.math.25p SFR +sector, data-fouryearipeda.) Residuals: -54.768 -6.150 0.596 6.601 38.514 Coefficients: Min 1Q Median 3Q Max Estimate Std. Error t value Pr>ltl) -4.331e+01 3.046e+00 -14.221 <2e-16 (Intercept) Average.loans SAT.reading.25p SAT.math.25p SFR sectorPublic, 4-year or above -2.602e+00 7.907e-01 -3.291 0.00103* .573e-03 1.963e-04 8.011 2.77e-15 8.455e-02 1.178e-02 7.177 1.27e-12* 1.086e-01 1.072e-02 10.138 <2e-16 -1.812e-01 9.119e-02 -1.988 0.04710 Residual standard error: 9.855 on 1149 degrees of freedom (1219 observations deleted due toniAnǐngnes Multiple R-squared:...
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