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Does how long young children remain at the lunch table help predict how much they eat? Here are data on a random sample of 20toddlers observed over several months. 鈥淭ime鈥 is the average number of minutes a child spent at the table when lunch was served. 鈥淐alories鈥 is the average number of calories the child consumed during lunch, calculated from careful observation of what the child ate each day.


Here is some computer output from a least-squares regression analysis of these data. Do these data provide convincing evidence at the =0.01=0.01level of a linear relationship between time at the table and calories consumed in the population of toddlers?


PredictorCoefSECoefTPConstant560.6529.3719.090.000Time3.07710.84983.620.002S=23.3980R-Sq=42.1%R-Sq(adj)=38.9%

Short Answer

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Step by step solution

01

Given Information

We need to

02

Simplify

Consider:

n=Samplesize=20=Significancelevel=0.01

The estimate of the slope b1is given in the row "Time" and in the column "Coef" of the given computer output:

b1=-3.0771

The estimated standard deviation of the slope role="math" localid="1654164770191" SEb1is given in the row "Time" and in the column "SE Coef" of the given computer output:

SEb1=0.8498

Given claim: Slope is nonzero:

The null hypothesis or the alternative hypothesis states the given claim The null hypothesis states that the slope is zero. If the given claim is the null hypothesis, then the alternative hypothesis states the opposite of the null hypothesis.

H0:1=0H:10

Compute the value of the test statistic:

t=b11SEb1=3.077100.8498-3.6210

The P-value is the probability of obtaining the value of the test statistic, or a value more extreme. The P-value is the number (or interval) in the column title of the Student's T table in the appendix containing the -value in the row df=n2=202=18We can ignore the minus sign in the test statistic:

0.001=2(0.0005)<P<2(0.001)=0.002

If the P-value is less than or equal to the significance level, then the null hypothesis is rejected:
P<0.01RejectH0

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Most popular questions from this chapter

Boyle鈥檚 law Refers to Exercise 34. We took the logarithm (base 10) of the values for both volume and pressure. Here is some computer output from a linear regression analysis of the transformed data.


a. Based on the output, explain why it would be reasonable to use a power model to describe the relationship between pressure and volume.

b. Give the equation of the least-squares regression line. Be sure to define any variables you use.

c. Use the model from part (b) to predict the pressure in the syringe when the volume is 17cubic centimeters.

T12.11 Growth hormones are often used to increase the weight gain of chickens. In an experiment using 15 chickens, 3 chickens were randomly assigned to each of 5 different doses of growth hormone (0, 0.2, 0.4, 0.8, and 1.0 milligrams). The subsequent weight gain (in ounces) was recorded for each chicken. A researcher plots the data and finds that a linear relationship appears to hold. Here is computer output from a least-squares
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ii. The y intercept
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T12.12 Foresters are interested in predicting the amount of usable lumber they can harvest from various tree species. They collect data on the diameter at breast height (DBH) in inches and the yield in board feet of a random sample of 20 Ponderosa pine trees that have been harvested. (Note that a board foot is defined as a piece of lumber 12 inches by 12 inches by 1 inch.) Here is a scatterplot of the data.

a. Here is some computer output and a residual plot from a least-squares regression on these data. Explain why a linear model may not be appropriate in this case.

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b. Use both models to predict the amount of usable lumber from a Ponderosa pine with diameter 30 inches.
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a. A scatterplot of y versus x looks approximately linear.
b. A scatterplot of Iny versus x looks approximately linear.
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d. A scatterplot of Iny versus lnx looks approximately linear.
e. None of these

In a recent poll, randomly selected New York State residents at various fast-food restaurants were asked if they supported or opposed a "fat tax" on sugared soda. Thirtyone percent said that they were in favor of such a tax and 66% were opposed. But when asked if they would support such a tax if the money raised were used to fund health care given the high incidence of obesity in the United States, 48% said that they were in favor and 49% were opposed.
(a) In this situation, explain how bias may have been introduced based on the way the questions were worded and suggest a way that the questions could have been worded differently in order to avoid this bias.
(b) In this situation, explain how bias may have been introduced based on the way the sample was taken and suggest a way that the sample could have been obtained in order to avoid this bias.
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