Question

course NGAGE MINDTAP z and y. z | 10 220 11 24 12 30 21 18 27 equation for the data of the form bo + b z. Comment on the adequacy of this equation for predicting p. Enter negative value as negative number. x (to 2 decimals) R-sq adj (to 1 decimal) (to 1 decimal) Analysis of Variance SOURCE OF MS F-value (to 2 Residual Error Total |%(to 1 decimal) of the variability in y has been explained byz, b. Develop an estimated regression your answer is zero, enter "0' equation for the data of the form İ-h. +biz + br. Comment on the odequacy or this negative value as negative number. 1f x (to 2 decimals) R-sq adj- to 1 a search this course SAGE MINDTAP Using a an esti ated regression equation for the data of the form y bg + bız + bg2 Comment on the adequacy of this equation for predicting y Enter negative value as negative number, if your answer is zero, enter "0' x + x2 (to 2 decimals) R-sq adj- (to 1 decimal) p-value (to 4 decimals) SOURCE DF MS (to 3 decimals) (to 3 decimais) (to 2 decimals) Regression | Residual Error Total % (to 1 decimal) of the variability in y has been explained by z. (to 2 decimals) Check My Work (3 remaining)

Answer 1

a.

Step 1 - Put the data in excel as shown and arrange the variables as shown

Step 2 - Since we need to predict y

The dependent variable is y

The independent variable is x

Step 3 - Select the regression option from the data analysis tab

Step 4 - Input the values as shown below.

Step 5 - The output is generated as follows.

Regession equation ( highlighted in yellow)

y = 5.9379 + 0.833 x

s = 2.3983 ( highlighted in blue)
rsqure = 0.9159 (highlighted in green)
r sq adjusted = 0.8878

anova

Using the 0.05 significance level, the pvalue indicates a high significance , note the 91 % of the variablity in y has been explained by x.

b.

Step 1 - Put the data in excel as shown, also create x2 and arrange the variables as shown

Step 2 - Since we need to predict y

The dependent variable is y

The independent variable is x, x2

Step 3 - Select the regression option from the data analysis tab

Step 4 - Input the values as shown below.

Step 5 - The output is generated as follows.

Regession equation ( highlighted in yellow)

y= 6.7219 +0.7352 x +0.0025 x2

s = 2.9335 ( highlighted in blue)
rsqure = 0.9161 (highlighted in green)
r sq adjusted = 0.8322

anova

Using the 0.05 significance level, the pvalue indicates a no significance , note the 91 % of the variablity in y has been explained by x.

c.

Since the only the first model is signficant, we will use the first regression equation to predict.

y = 5.9379 + 0.833 x

y = 5.9379 + 0.833 (17) = 20.1004