Call: lm(formula = Sales ~ Price + Urban + US) Residuals: Min 1Q Median 3Q Max -6.9206 -1.6220 -0.0564 1.5786 7.0581 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.043469 0.651012 20.036 < 2e-16 * Price -0.054459 0.005242 -10.389 < 2e-16 * UrbanYes -0.021916 0.271650 -0.081 0.936 USYes 1.200573 0.259042 4.635 4.86e-06 * --- Signif. codes: 0 ‘*’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.472 on 396 degrees of freedom Multiple R-squared: 0.2393, Adjusted R-squared: 0.2335 p-value: < 2.2e-16F-statistic: 41.52 on 3 and 396 DF, p-value: < 2.2e-16 Which of the following statement is wrong? Sales = 13.043469 - 0.054459×Price - 0.021916×Urban + 1.200573×USwith Urban = 1 if the store is in an urban location and 0 if not,and US = 1 if the store is in the US and 0 if not. Sales = 13.043469 - 0.054459×Price - 0.021916×Urban + 1.200573×USwith Urban = 0 if the store is in an urban location and 1 if not,and US = 0 if the store is in the US and 1 if not.

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter7: Analytic Trigonometry

Section7.6: The Inverse Trigonometric Functions

Problem 94E

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Question

Call:

lm(formula = Sales ~ Price + Urban + US)

Residuals:

Min 1Q Median 3Q Max

-6.9206 -1.6220 -0.0564 1.5786 7.0581

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 13.043469 0.651012 20.036 < 2e-16 ***

Price -0.054459 0.005242 -10.389 < 2e-16 ***

UrbanYes -0.021916 0.271650 -0.081 0.936

USYes 1.200573 0.259042 4.635 4.86e-06 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.472 on 396 degrees of freedom

Multiple R-squared: 0.2393, Adjusted R-squared: 0.2335

p-value: < 2.2e-16F-statistic: 41.52 on 3 and 396 DF, p-value: < 2.2e-16

Which of the following statement is wrong?

		Sales = 13.043469 - 0.054459×Price - 0.021916×Urban + 1.200573×US with Urban = 1 if the store is in an urban location and 0 if not, and US = 1 if the store is in the US and 0 if not.
		Sales = 13.043469 - 0.054459×Price - 0.021916×Urban + 1.200573×US with Urban = 0 if the store is in an urban location and 1 if not, and US = 0 if the store is in the US and 1 if not.

Definition Definition Middle value of a data set. The median divides a data set into two halves, and it also called the 50th percentile. The median is much less affected by outliers and skewed data than the mean. If the number of elements in a dataset is odd, then the middlemost element of the data arranged in ascending or descending order is the median. If the number of elements in the dataset is even, the average of the two central elements of the arranged data is the median of the set. For example, if a dataset has five items—12, 13, 21, 27, 31—the median is the third item in ascending order, or 21. If a dataset has six items—12, 13, 21, 27, 31, 33—the median is the average of the third (21) and fourth (27) items. It is calculated as follows: (21 + 27) / 2 = 24.

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