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The 2011 roster of the Dallas Cowboys professional football team included 7 defensive linemen. Their weights (in pounds) were 321,285,300 , \(285,286,293,\) and \(298 .\) Calculate the mean. Show your work.

Short Answer

Expert verified
The mean weight is approximately 295.4 pounds.

Step by step solution

01

Understand the Problem

We need to find the mean (average) weight of the 7 defensive linemen in the Dallas Cowboys football team from 2011. The weights given are 321, 285, 300, 285, 286, 293, and 298 pounds.
02

Sum the Weights

Add all the weights of the players together. That is, calculate \( 321 + 285 + 300 + 285 + 286 + 293 + 298 \).
03

Calculate the Total Sum

Perform the addition: \( 321 + 285 + 300 + 285 + 286 + 293 + 298 = 2068 \). This is the total weight of all the players combined.
04

Count the Number of Players

There are 7 players, as given in the problem.
05

Compute the Mean

Divide the total weight by the number of players to get the mean weight: \( \frac{2068}{7} \).
06

Find the Mean

Carry out the division: \( 2068 \div 7 = 295.4286 \). We can round it to one decimal place, so the mean is approximately \( 295.4 \) pounds.

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Key Concepts

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

Arithmetic Mean
Understanding the arithmetic mean is fundamental to data analysis. It's the average of a set of numbers, calculated by adding all the numbers and then dividing by the count of the numbers. In the context of our exercise, calculating the arithmetic mean allows us to understand the typical weight of a defensive lineman on the Dallas Cowboys team in 2011.

To calculate, we start by summing up all the given weights: 321, 285, 300, 285, 286, 293, and 298 pounds. This results in a total sum. Next, we count the total number of defensive linemen, which is 7 in this scenario. Finally, we divide the total weight by the number of players:
  • Total sum = 2068 pounds
  • Number of players = 7
  • Mean = Total sum / Number of players = 2068 / 7
This division gives us an approximate average weight of 295.4 pounds. Getting comfortable with this calculation will improve not just your arithmetic skills, but also your ability to analyze data in various contexts.
Data Analysis
Data analysis involves examining, cleaning, and transforming data to highlight useful information, suggest conclusions, and support decision-making. In this exercise, data analysis helps us understand the general trends within the players’ weights.

Our primary goal was to find the mean weight. However, data analysis could be expanded to look for other metrics like the median, mode, or range of the weights:
  • **Median**: The middle value when weights are arranged in order. An important measure, especially when data shows some skewness.
  • **Range**: The difference between the highest (321) and lowest (285) weights, providing insight into weight variation.
  • **Mode**: The most frequently occurring weight, useful for understanding commonality among data points.
Understanding these concepts allows deeper insights into the distribution and variability of the linemen’s weights, beyond the singular figure derived from the mean.
Step-by-Step Solutions
Step-by-step solutions offer clarity and simplicity when solving problems. Breaking complex tasks, like calculating the mean, into smaller, achievable steps makes learning manageable.

Here's how the step-by-step solution was structured in this exercise:
  • **Define the Problem**: Clearly understand what's being asked - calculating the mean weight of the linemen.
  • **Summation**: Add all the individual weights – a critical and straightforward first step.
  • **Division**: Use the total weight and divide by the count of weights to find the mean.
  • **Verification**: Recheck calculations to ensure accuracy, rounding appropriately if necessary.
This structured approach not only supports solving the immediate problem of finding the mean but also nurtures problem-solving skills. It encourages precise thinking, ensures accuracy, and builds confidence in tackling similar problems in future analytical scenarios.

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