Shown below is a portion of a computer output for regression analysis relating Y (demand) and X(unit price). ANOVA Df SS Regression 1 Error 3132.661 Total 47 8181.479 Coefficients Standard Error Intercept 80.390 3.102 X -2.137 0.248 Compute the coefficient of determination and fully interpret its meaning. Be very specific. Find the standard error for b1(Sb1). Perform a t test and determine whether or not demand and unit price are related. Let significance level= 0.05. Perform an F test and determine whether or not demand and unit price are related. Let significance level= 0.05.

Shown below is a portion of a computer output for regression analysis relating Y (demand) and X(unit price). ANOVA Df SS Regression 1 Error 3132.661 Total 47 8181.479 Coefficients Standard Error Intercept 80.390 3.102 X -2.137 0.248 Compute the coefficient of determination and fully interpret its meaning. Be very specific. Find the standard error for b1(Sb1). Perform a t test and determine whether or not demand and unit price are related. Let significance level= 0.05. Perform an F test and determine whether or not demand and unit price are related. Let significance level= 0.05.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 5PPS

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Question

Shown below is a portion of a computer output for regression analysis relating Y (demand) and X(unit price).

ANOVA

Df SS

Regression 1

Error 3132.661

Total 47 8181.479

Coefficients Standard Error

Intercept 80.390 3.102

X -2.137 0.248

Compute the coefficient of determination and fully interpret its meaning. Be very specific.
Find the standard error for b₁(S_b1).
Perform a t test and determine whether or not demand and unit price are related. Let significance level= 0.05.
Perform an F test and determine whether or not demand and unit price are related. Let significance level= 0.05.

Definition Definition Statistical method that estimates the relationship between a dependent variable and one or more independent variables. In regression analysis, dependent variables are called outcome variables and independent variables are called predictors.

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