2r051 CDC Volulme 3

A sample of a population that is taken in such a manner that each value has an equal chance of being selected is referred to as a random sample. In a random sample, every individual in the population has an equal probability of being chosen, which helps to reduce bias and increase the representativeness of the sample. This allows for generalizations to be made about the entire population based on the characteristics observed in the sample.

The given expression involves multiplication and division. According to the order of operations, multiplication and division should be performed from left to right. Therefore, we first divide 5 by 2, which equals 2.5. Then, we multiply 2.5 by 6, resulting in 15.0.

The given question asks to compute the difference between the signed numbers -17 and -(-10). To find the difference, we can change the subtraction into addition by changing the sign of the second number and then adding them together. So, -(-10) becomes +10. Therefore, -17 + 10 equals -7. Hence, the correct answer is -7.0.

In the equation Y=X+2, the symbol "Y" represents a variable. In algebra, variables are used to represent unknown values or quantities that can vary. In this equation, "Y" is the variable that represents an unknown value, while "X" is another variable representing a different unknown value. The equation states that "Y" is equal to "X" plus 2, indicating that the value of "Y" depends on the value of "X" plus 2.

The mode of a sample is the value that appears most frequently. In this case, the number 6 appears twice, which is more than any other number in the sample. Therefore, the mode of the sample is 6.

The second piece of equipment weighs 3.5 times as much as the first piece, which weighs 125 pounds. To find the weight of the second piece, we multiply 125 by 3.5, which equals 437.5 pounds. Therefore, the second piece of equipment weighs 437.5 pounds.

To find the number that eighty is 40 percent of, we can set up the equation 0.4x = 80, where x represents the unknown number. By dividing both sides of the equation by 0.4, we can solve for x and find that the unknown number is 200.

The best example of a population in statistical language is the total T-38s failures across the Air Force. A population refers to the entire group or set of individuals or objects that the researcher is interested in studying. In this case, the population is all the T-38s failures across the entire Air Force. This example represents the entire group of failures and is not limited to a specific time period or location, making it the best example of a population.

A histogram is the best graphical method to represent the number of individual values in each class because it displays the frequency distribution of a continuous or discrete variable. The x-axis represents the different classes or intervals, while the y-axis represents the frequency or count of values within each class. The height of each bar in the histogram corresponds to the frequency of values in that class, allowing for a clear visualization of the distribution and comparison between different classes. Frequency polygons, overlapping polygons, and overlapping histograms may not provide as clear and concise representation of the number of values in each class.

The mode is the measure of central tendency that represents the most frequently occurring value in a distribution. It is the value that appears the highest number of times, making it the most typical value. Unlike the mean and median, which can be influenced by outliers or extreme values, the mode is not affected by these values. Therefore, it is considered the most representative measure of central tendency in a distribution.

Inferential statistics is the correct answer because it is the statistical method that uses only a sample of data to make inferences or draw conclusions about a larger population. It involves analyzing the sample data and using it to make predictions or generalizations about the entire population. This is different from descriptive statistics, which focuses on summarizing and describing the characteristics of the sample data itself. Data survey and randomization are not specific statistical methods, but rather terms that may be used in the context of statistical analysis.

The use of descriptive statistics is necessary to summarize a large amount of data. Descriptive statistics involve organizing, summarizing, and presenting data in a meaningful way, such as calculating measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation). By summarizing the data, patterns, trends, and characteristics can be identified, allowing for a better understanding of the data set as a whole. This information can then be used to draw conclusions and make informed decisions based on the data.

The class interval that will give you 18 classes is 0.2. This is because the question is asking for the class interval that will result in 18 classes in the noncumulative frequency distribution. The other class intervals listed (0.1, 0.3, 0.4) will not result in 18 classes.

The second step in making a frequency distribution is to determine the class interval size. This is important because it helps in organizing the data into groups or intervals. The class interval size determines the width of each interval and ensures that the data is grouped in a meaningful and manageable way. By determining the class interval size, we can effectively analyze and interpret the data in a frequency distribution.

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Interval measurement scale consists of equal intervals between scale values and an arbitrary zero point. This means that the numerical difference between two values on the scale is meaningful and consistent. However, the zero point is arbitrary and does not indicate the absence of the measured attribute. Examples of interval scales include temperature measured in Celsius or Fahrenheit, where the difference between 10 and 20 degrees is the same as the difference between 80 and 90 degrees.

If the class interval in a frequency distribution is too large, it means that the data is grouped together in broader ranges. This results in a loss of detail because the individual values within each range are not accounted for separately. By having larger class intervals, the distribution fails to capture the variability and specific values of the data, leading to a loss of information and detail.

Measures such as the mean or median that are computed from a sample are called statistics. Statistics refer to the numerical data derived from a subset of a population, which is used to make inferences and draw conclusions about the entire population. In this context, the mean or median calculated from a sample provides information about the central tendency of the sample data and can be used to estimate the corresponding measures in the population.

In a frequency polygon, the frequencies of the various class intervals are plotted against the corresponding midpoints. This is done to visually represent the distribution of data and show the frequency of occurrences within each interval. By plotting the frequencies, we can observe any patterns or trends in the data and understand the distribution more clearly.

The median is a measure of central tendency that is used to find the middle value in a set of data. It is calculated by arranging the data in ascending order and selecting the middle value. However, the median cannot be used with data from a nominal measurement scale. Nominal data is categorical data that does not have any inherent order or numerical value. Since the median relies on the order of the data, it cannot be calculated for nominal data.