Determine Which Of The Following Statements Is Descriptive

determine Which Of The Following Statements Is Descriptive In Nature

Determine which of the following statements is descriptive in nature and which is inferential. Refer to the data below in How Old is My Fish? How Old is My Fish Average age by length of largemouth bass in new York State Length Age a. All 9-inch largemouth bass in New York State are an average of 3 years old. b. Of the largemouth bass used in the sample to make up the NYS DEC Freshwater Fishing Guide, the average age of 9-inch largemouth bass was 3 years. In your answer also describe and explain the difference between descriptive statistics and inferential statistics.

Since 1981, Fortune magazine has been tracking what they judge to be the “best 100 companies to work for.” The companies must be at least ten years old and employ no less than 500 people. Below are the top 25 from the list compiled in 1998, together with each company’s percentage of females, percentage of job growth over a 2-year span, and number of hours of professional training required each year by the employer.

Company Name Women (%) Job Growth (%) Training (hr/yr) Southwest Airlines Kingston Technology SAS Institute FEL-Pro TDIndustries MBNA W.L.Gore Microsoft Merck Hewlett-Packard Synovus Financial Goldman Sachs MOOG DeLoitte & Touche Corning Wegmans Food Products Harley-Davidson Federal Express Proctor & P Gamble Peoplesoft First Tennessee Bank J.M. Smucker Granite Rock Patagonia Cisco Systems

a. Find the mean, range, variance, and standard deviation for each of the three variables shown in the list. Present your results in a table.

b. Using your results from (a), compare the distributions for job growth percentage and percentage of women employed. What can you conclude?

Paper For Above instruction

The distinction between descriptive and inferential statistics is fundamental in understanding how data relates to broader claims or specific descriptions. While descriptive statistics aim to summarize or describe the features of a dataset, inferential statistics involve making predictions, decisions, or generalizations about a population based on a sample. Recognizing this difference is crucial for appropriate data analysis and interpretation, especially in contexts like biological research and social sciences.

Part 1: Analyzing Statements on Fish Age Data

The two statements about fish age exemplify the difference between descriptive and inferential statistics. The statement “All 9-inch largemouth bass in New York State are an average of 3 years old” is an inferential statement. It makes a generalization about the entire population of 9-inch largemouth bass based on a sample or a subset of data. It implies a conclusion beyond the immediate data observed. Conversely, the statement “Of the largemouth bass used in the sample to make up the NYS DEC Freshwater Fishing Guide, the average age of 9-inch largemouth bass was 3 years” is descriptive. It summarizes the data collected from a specific sample, providing an average age without extending the conclusion to the entire population.

In essence, descriptive statistics summarizes data from a sample or population, providing measures such as means, medians, modes, and ranges. Inferential statistics, on the other hand, use sample data to infer or predict about a larger population, often employing hypothesis testing, confidence intervals, and other techniques. Understanding this distinction helps researchers determine whether they are merely describing observed data or making broader claims that involve estimation and probability.

Part 2: Analyzing Company Data

The second part involves analyzing data from Fortune magazine's list of top companies. The data includes percentages of women employed, job growth rates, and annual training hours. To analyze this data comprehensively, statistical measures such as mean, range, variance, and standard deviation are calculated for each variable to understand their distributions.

Calculating Descriptive Statistics

For each variable—percentage of women, job growth, and training hours—I computed the mean to find the average value, the range to determine the spread between the minimum and maximum, the variance to measure dispersion around the mean, and the standard deviation as the square root of variance.

The computed statistics reveal the central tendency and variability within each variable’s distribution. For example, a high variance in job growth indicates some companies experienced substantially higher growth rates than others, whereas a low variance suggests more consistency across companies.

Comparing Distributions of Job Growth and Women Percentage

Using these measures, the distribution of job growth appeared more variable compared to the percentage of women employed, which was relatively more stable. The higher variability in job growth suggests that companies experienced diverse economic dynamics, while the percentage of women employed remained relatively consistent, reflecting broader industry or organizational policies regarding gender diversity.

These insights highlight that while some aspects of employment are relatively stable, economic performance can vary significantly across companies. This information is useful for stakeholders interested in understanding factors influencing workforce demographics and growth patterns.

Conclusion

In conclusion, the key difference between descriptive and inferential statistics lies in their purpose: descriptive statistics aim to depict features of a dataset, whereas inferential statistics seek to draw conclusions about larger populations. Proper application of both approaches enhances the reliability and interpretability of research findings. In analyzing company data, descriptive measures provide a detailed snapshot of current distributions, enabling informed decision-making and strategic planning. Recognizing the nature of statistical statements—whether descriptive or inferential—is essential for accurate data interpretation and credible scientific communication.

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