PCBs and Pelicans. Use the data points given on the WeissStats site for shell thickness and concentration of PCBs for 60 Anacapa pelican eggs referred to in Exercise 14.40.

Short Answer

Expert verified

The variables thickness and PCBdo not violate assumption 1-3 for regression conclusions.

Step by step solution

01

Part (a) Step 1: Given information

Given in the question that, PCBs and Pelicans. Use the data points given on the WeissStats site for shell thickness and concentration of PCBs for 60 Anacapa pelican eggs referred to in Exercise 14.40.
We need to decide that whether we can reasonably apply the regression t-lest. If so, then also do part (b).

02

Part (a) Step 2: Explanation

Given:

MINITAB is used to create the residual plot.

Procedure with Minitab:

To begin, select Start > Regression > Regression.

Step 2: Fill in the THICKNESS column in the Response.

Step 3: Select Column PCB in Predictors.

Step 4: In Graphs, under Residuals versus the variables, enter the columns PCB.

Step 5: Click the OK button.

Construct the normal probability plot of residuals by using MINITAB

03

Part (a) Step 3: MINITAB procedure

Step 1: Select Start > Regression > from the Minitab menu. Regression

Step 2: Fill in the THICKNESS column in the Response field.

Step 3: Select Column PCB from Predictors.

Step 4: Choose Normal probability plot of residuals from the Graphs menu.

Step 5: Select OK from the drop-down menu.

output MINITAB:

Regression conclusions are based on the following assumptions:

Regression line of the population:

For each value of Xof predictor variable, the conditional mean of the response variable (Y)is β0+β1X.

The standard deviation for the response variable (Y)and the standard deviation for the explanatory variable (X)are the same. The standard deviation is represented by the symbol σ.

Populations that are typical:

The response variable follows a normal distribution.

Independent Observations: The responses variable observations are unrelated to one another.

Examine whether the regression t-test is appropriate.

  • It's evident from the residual plot that the residuals are in the horizontal band.
  • It is obvious from the normal probability plot of residuals that the residuals follow a linear trend.

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Most popular questions from this chapter

Suppose that xand yare two variables of a population with xa predictor variable and ya response variable.

a. The distribution of all possible values of the response variable ycorresponding to a particular value of the predictor variable xis called a distribution of the response variable.

b. State the four assumptions for regression inferences.

In Exercises 14.48, we repeat the information from Exercises 14.12.

a. Decide, at the 10%significance level, whether the data provide sufficient evidence to conclude that xis useful for predicting y:

b. Find a 90%confidence interval for the slope of the population regression line.

x243y357 role="math" localid="1652276835214" y^=2+x

Gas Guzzelrs. Use the data on the WeissStats site for gas mileage and engine displacement for 121vehicles referred to in Exercise 14.41

a. Decide whether you can reasonably apply the regression t-test. If so, then also do part (b).

b. Decide, at the 5%significance level, whether the data provide sufficient evidence to conclude that the predictor variable is useful for predicting the response variable.

Gas Guzzlers. The data from Exercise 14.41 for gas mileage and engine displacement of 121 vehicles are on the WeissStats site. Specified value of the predictor variable: 3.0L.

a. Decide whether you can reasonably apply the conditional mean and predicted value t-interval procedures to the data. If so, then also do parts (b)-(f).

b. Determine and interpret a point estimate for the conditional mean of the response variable corresponding to the specified value of the predictor variable.

c. Find and interpret a 95%confidence interval for the conditional mean of the response variable corresponding to the specified value of the predictor variable.

d. Determine and interpret the predicted value of the response variable corresponding to the specified value of the predictor variable.

e. Find and interpret a 95%prediction interval for the value of the response variable corresponding to the specified value of the predictor variable.

f. Compare and discuss the differences between the confidence interval that you obtained in part (c) and the prediction interval that you obtained in part (e).

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