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91Ó°ÊÓ

Give the correct notation for the quantity described and give its value. Proportion of US adults who own a cell phone. In a survey of 1006 US adults in \(2014,90 \%\) said they had a cell phone. \(^{7}\)

Short Answer

Expert verified
Notation: \( p \) , Value: \( 0.90 \) or \( \frac{90}{100} \)

Step by step solution

01

Understand the problem

The problem is asking about the proportion of U.S. adults who own a cell phone. The data given is from a survey of 1006 U.S. adults in 2014 from which 90% confirmed they had a cell phone.
02

Define the notation

Let's denote the proportion of U.S. adults who own a cell phone as \( p \). This is the standard notation for a proportion in statistical analysis.
03

Calculate the proportion

To calculate \( p \), we take the given percentage and express it as a decimal. Since 90% of the adults surveyed own a cell phone, the proportion \( p \) is \( 0.90 \) or \( \frac{90}{100} \).
04

Interpret the results

The notation for the quantity described as 'proportion of U.S. adults who own a cell phone' is \( p \), and its value is \( 0.90 \) or as a fraction \( \frac{90}{100} \). This means that in this survey, 90% of U.S. adults own a cell phone.

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Key Concepts

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

Statistical Notation
Statistical notation serves as a universal language in the field of mathematics and statistics, enabling clear communication of quantitative information. In this context, proportions are generally denoted by the letter 'p'.

For example, when denoting the proportion of U.S. adults who own a cell phone, we use the notation '\( p \)'. This allows anyone reading a study or data analysis to quickly understand what '\( p \)' represents without confusion. Proper notation supports clarity and precision, which are essential in any statistical analysis.

Another common aspect of statistical notation is representing percentages as decimals in calculations. This is because mathematical operations can be performed more easily with decimals than with percentages. In our example, the 90% cell phone ownership is represented as '\( p = 0.90 \)', which is clearer for computational purposes than writing '90%'. This standard practice helps avoid errors and makes it easier to perform further statistical analyses, such as hypothesis testing or predictive modelling.
Survey Data Analysis
Survey data analysis involves interpreting responses received from a sample of individuals to draw conclusions about the larger population. In the given exercise, a survey of 1006 U.S. adults in 2014 revealed that 90% owned a cell phone.

To analyze survey data effectively, one must first understand the sample's context and representativeness. In this case, it is implied that the sample of 1006 adults is representative of the U.S. adult population. After understanding the context, it is critical to use the correct statistical notation, as we have assigned '\( p \)' to represent our proportion of interest.

Through proper analysis, we can interpret that the data suggests a majority of U.S. adults owned cell phones in 2014. This kind of analysis is crucial for making informed decisions, such as in marketing strategies, policy-making, or tracking technological adoption trends over time.
Percentage to Decimal Conversion
Converting a percentage to a decimal is an essential skill in both mathematical exercises and real-world applications. The process involves dividing the percentage value by 100. This step transforms the percentage, which can be seen as a portion out of 100, into a proportion represented as a decimal.

For instance, in the given exercise, the percentage of U.S. adults who own a cell phone is 90%. To convert this into a decimal, we simply divide by 100, which gives us '\( p = \frac{90}{100} = 0.90 \)'. This conversion is crucial for calculations in statistics and other disciplines because decimals are much more amenable to computational operations.

In practical terms, understanding this conversion process helps in various scenarios such as calculating discounts, determining interest rates, or even interpreting statistics such as those from surveys. Often, it allows for better comprehension of the data, facilitating more accurate analysis and decision-making based on the results.

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Most popular questions from this chapter

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Use data from a study designed to examine the effect of doing synchronized movements (such as marching in step or doing synchronized dance steps) and the effect of exertion on many different variables, such as pain tolerance and attitudes toward others. In the study, 264 high school students in Brazil were randomly assigned to one of four groups reflecting whether or not movements were synchronized (Synch= yes or no) and level of activity (Exertion= high or low). \(^{49}\) Participants rated how close they felt to others in their group both before (CloseBefore) and after (CloseAfter) the activity, using a 7-point scale (1=least close to \(7=\) most close ). Participants also had their pain tolerance measured using pressure from a blood pressure cuff, by indicating when the pressure became too uncomfortable (up to a maximum pressure of \(300 \mathrm{mmHg}\) ). Higher numbers for this Pain Tolerance measure indicate higher pain tolerance. The full dataset is available in SynchronizedMovement. For each of the following problems: (a) Give notation for the quantity we are estimating, and define any relevant parameters. (b) Use StatKey or other technology to find the value of the sample statistic. Give the correct notation with your answer. (c) Use StatKey or other technology to find the standard error for the estimate. (d) Use the standard error to give a \(95 \%\) confidence interval for the quantity we are estimating. (e) Interpret the confidence interval in context. Are Males or Females More Likely to Go to Maximum Pressure? The study recorded whether participants were female or male \((\operatorname{Sex}=\mathrm{F}\) or \(\mathrm{M})\), and we see that 33 of the 165 females and 42 of the 99 males allowed the pressure to reach its maximum level of \(300 \mathrm{mmHg}\) after treatment, without ever saying that the pain was too much. Use this information to estimate the difference in proportion of people who would allow the pressure to reach its maximum level after treatment, between females and males.

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