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Metabolic rate, the rate at which the body consumes energy, is important in studies of weight gain, dieting, and exercise. We have data on the lean body mass and resting metabolic rate for 12 women who are subjects in a study of dieting. Lean body mass, given in kilograms, is a person's weight leaving out all fat. Metabolic rate is measured in calories burned per 24 hours. The researchers believe that lean body mass is an important influence on metabolic rate. $$\begin{array}{lccccccccc}\hline \text { Mass: } & 36.1 & 54.6 & 48.5 & 42.0 & 50.6 & 42.0 & 40.3 & 33.1 & 42.4 & 34.5 & 51.1 & 41.2 \\\\\text { Rate: } & 995 & 1425 & 1396 & 1418 & 1502 & 1256 & 1189 & 913 & 1124 & 1052 & 1347 & 1204 \\\\\hline\end{array}$$ (a) Use your calculator to help sketch a scatterplot to examine the researchers' belief. (b) Describe the direction, form, and strength of the relationship.

Short Answer

Expert verified
There is a strong positive linear relationship between lean body mass and metabolic rate.

Step by step solution

01

Gather the Data

We are provided with data on lean body mass and resting metabolic rate for 12 women. The lean body mass values are [36.1, 54.6, 48.5, 42.0, 50.6, 42.0, 40.3, 33.1, 42.4, 34.5, 51.1, 41.2], and the metabolic rates are [995, 1425, 1396, 1418, 1502, 1256, 1189, 913, 1124, 1052, 1347, 1204].
02

Create the Scatterplot

Use a graphing calculator or software to create a scatterplot with lean body mass on the x-axis and metabolic rate on the y-axis. Plot each pair (mass, rate) as a point on the graph.
03

Analyze the Scatterplot

Examine the scatterplot for patterns. Look for general trends: such as whether points tend to rise, fall, or remain constant as you move left to right. Also, note the spread of the data: Do the points follow a straight or curved line, and how tightly packed are they?
04

Describe the Relationship

Based on the scatterplot, describe: - **Direction**: Determine if the relationship is positive (upward trend: as mass increases, rate increases), negative (downward trend), or no trend. - **Form**: Decide if the relationship appears linear (points lie on a straight line) or nonlinear. - **Strength**: Evaluate how closely the data points adhere to a line – a strong relationship means points are closely packed along a line, while a weak one shows a lot of variation.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Understanding Metabolic Rate
Metabolic rate is a measure of how much energy, in the form of calories, your body expends over a given period, typically 24 hours. This can be thought of as how quickly your body burns calories. Your metabolic rate is influenced by several factors, including your body's composition, notably your lean body mass.

The metabolic rate helps in various health-related applications. For example, understanding your metabolic rate can support weight management strategies. Here are some key influences on metabolic rate:
  • Genetics: Your inherited traits may determine your baseline metabolic rates.
  • Lean Body Mass: More lean muscle mass often results in a higher metabolic rate as muscles require more energy than fat.
  • Age and Gender: Typically, younger individuals and males burn more calories at rest.
  • Physical Activity: Regular exercise can boost your metabolic rate.
  • Diet and Stress: What you eat and your stress levels can affect how your body uses energy.
Understanding your metabolic rate can help tailor personal health plans, optimize diet, and adjust exercise routines for better health outcomes.
Importance of Lean Body Mass
Lean body mass is essentially what remains after subtracting body fat from your total body weight. It includes muscles, bones, organs, and water, which are critical in understanding metabolic processes. Lean body mass is a significant factor affecting your metabolism and overall health.

Why Lean Body Mass Matters:
  • Muscle Mass: Muscles require more energy than fat even at rest, meaning higher muscle mass can contribute to a higher resting metabolic rate.
  • Bone Health: Strong bones contribute to overall body function, while water is essential for all cellular processes.
  • Health Indicator: A higher proportion of lean mass can be an indicator of good health and fitness levels, reducing risks for various lifestyle diseases.
To maintain and build lean body mass, engaging in regular strength training exercises and consuming a balanced diet with enough protein is beneficial. This not only enhances metabolic rate but also supports functional movement and overall vitality.
Exploring Correlation Analysis
Correlation analysis is a statistical technique used to determine the relationship between two variables. In the context of our exercise, it helps us understand how lean body mass and metabolic rate are related among the participants.

Here, a scatterplot serves as a visual tool to illustrate the relationship: - **Direction:** Looks at whether an increase in one variable leads to an increase or decrease in another. If the data points rise to the right in the scatterplot, it indicates a positive correlation — that lean body mass increases as metabolic rate increases. - **Form:** Determines if the relationship is linear—plots that suggest a straight-line pattern—or if they form a curved pattern thereby suggesting a non-linear relationship. - **Strength:** Indicates how closely the data points are grouped around a line. A strong linear relationship will have points tightly clustered around a line, while a weak relationship shows more scatter. Understanding these aspects of correlation analysis can assist in making informed predictions and decisions based on the varying degrees of association between different scientific variables.

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Most popular questions from this chapter

Exercise 6 (page 159 ) examined the relationship between the number of new birds \(y\) and percent of returning birds \(x\) for 13 sparrowhawk colonies. Here are the data once again. $$\begin{array}{lrrrrrrrrrrrrr}\hline \text { Percent return: } & 74 & 66 & 81 & 52 & 73 & 62 & 52 & 45 & 62 & 46 & 60 & 46 & 38 \\\\\text { New adults: } & 5 & 6 & 8 & 11 & 12 & 15 & 16 & 17 & 18 & 18 & 19 & 20 & 20 \\\\\hline \end{array}$$ (a) Use your calculator to help make a scatterplot. (b) Use your calculator's regression function to find the equation of the least-squares regression line. Add this line to your scatterplot from (a). (c) Explain in words what the slope of the regression line tells us. (d) Calculate and interpret the residual for the colony that had \(52 \%\) of the sparrowhawks return and 11 new adults.

The stock market Some people think that the behavior of the stock market in January predicts its behavior for the rest of the year. Take the explanatory variable \(x\) to be the percent change in a stock market index in January and the response variable \(y\) to be the change in the index for the entire year. We expect a positive correlation between \(x\) and \(y\) because the change during January contributes to the full year's change. Calculation from data for an 18 -year period gives $$\begin{array}{c}\bar{x}=1.75 \% \quad s_{x}=5.36 \% \quad \bar{y}=9.07 \% \\\ s_{y}=15.35 \% \quad r=0.596\end{array}$$ (a) Find the equation of the least-squares line for predicting full-year change from January change. Show your work. (b) Suppose that the percent change in a particular January was 2 standard deviations above average. Predict the percent change for the entire year, without using the least-squares line. Show your work.

Measurements on young children in Mumbai, India, found this least-squares line for predicting height \(y\) from \(\operatorname{arm} \operatorname{span} x:\) $$\hat{y}=6.4+0.93 x$$ Measurements are in centimeters \((\mathrm{cm})\). In addition to the regression line, the report on the Mumbai measurements says that \(r^{2}=0.95\). This suggests that (a) although arm span and height are correlated, arm span does not predict height very accurately. (b) height increases by \(\sqrt{0.95}=0.97 \mathrm{~cm}\) for each additional centimeter of arm span. (c) \(95 \%\) of the relationship between height and arm span is accounted for by the regression line. (d) \(95 \%\) of the variation in height is accounted for by the regression line. (e) \(95 \%\) of the height measurements are accounted for by the regression line.

Are hot dogs that are high in calories also high in salt? The figure below is a scatterplot of the calories and salt content (measured as milligrams of sodium) in 17 brands of meat hot dogs. (a) The correlation for these data is \(r=0.87 .\) Explain what this value means. (b) What effect does the hot dog brand with the lowest calorie content have on the correlation? Justify your answer.

In a scatterplot of the average price of a barrel of oil and the average retail price of a gallon of gas, you expect to see (a) very little association. (b) a weak negative association. (c) a strong negative association. (d) a weak positive association. (e) a strong positive association.

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