Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height +
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables:
shoe size – in inches
age – in years
height – in inches
forearm length – in inches
Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows:
| > summary(lm(shoesize ~ age + height + forearm, data = measures)) | ||||
| Coefficients: | ||||
(Intercept) age height forearm |
Estimate 10.14882 0.06045 -0.02108 -0.06479 |
Std. Error 4.49245 0.06838 0.06350 0.06847 |
t value 2.259 0.884 -0.332 -0.946 |
Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 |
| Residual standard error: 1.719 on 85 degrees of freedom Multiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 |
Using the information from above, fill in the blanks for the least-squares regression equation. Input all values to 5 decimal places.
yˆ= + (age)+ (height)+ (forearm)
Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables:
shoe size – in inches
age – in years
height – in inches
forearm length – in inches
Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows:
| > summary(lm(shoesize ~ age + height + forearm, data = measures)) | ||||
| Coefficients: | ||||
(Intercept) age height forearm |
Estimate 10.14882 0.06045 -0.02108 -0.06479 |
Std. Error 4.49245 0.06838 0.06350 0.06847 |
t value 2.259 0.884 -0.332 -0.946 |
Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 |
| Residual standard error: 1.719 on 85 degrees of freedom Multiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 |
Using the information from above, fill in the blanks for the least-squares regression equation. Input all values to 5 decimal places.
yˆ= + (age)+ (height)+ (forearm)
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