MTH216R4 Example Of Visuals

Mth216r4 Example Of Visuals

MTH/216r4 Example of Visuals Topic 4 - Social Sciences Social Sciences Scenario Topic 4 Predicting Teen Communication Preferences Scenario 4 Review the data on communication preferences with teens. Predict how many high school teens will prefer to use face-to-face communication in 2018.

Given the provided data, the task involves analyzing communication preferences among high school teens over several years and employing logarithmic regression to predict future preferences. The goal is to estimate the number of high school teens who will prefer face-to-face communication in the year 2018 based on historical data trends.

To approach this problem, it is essential to understand the nature of the data and the selected regression model. The data includes the year, communication preference (face-to-face), age group (teen), and school level (high school, HS). The primary focus is on the number of teens who prefer face-to-face communication across different years, with the explicit aim of extrapolating this trend to 2018.

Analyzing the data begins with organizing and plotting the historical figures of teens preferring face-to-face communication over the years. This visual representation helps identify the overall pattern — whether it’s increasing, decreasing, or stabilizing — and informs the choice of an appropriate regression model.

Logarithmic regression is selected to model the data because it is suitable when the rate of change in the dependent variable (number of teens preferring face-to-face) decreases or levels off as the independent variable (year) increases. This model is particularly useful for social science data, where growth may slow over time due to saturation or shifting preferences.

Once the data points are plotted, the logarithmic regression equation can be determined. The general form of a logarithmic model is:

Y = a + b * ln(X)

where:

  • Y is the number of teens preferring face-to-face communication,
  • X is the year,
  • a and b are parameters estimated during regression analysis,
  • ln(X) is the natural logarithm of X.

The regression process involves calculating the parameters a and b using statistical software or a graphing calculator, based on the historical data. Once the regression equation is established, it can be used to predict the number of teens in 2018.

For example, if the regression equation is determined to be:

Y = 15000 - 2000 * ln(X)

then substituting X = 2018 into the equation yields:

Y = 15000 - 2000 * ln(2018)

which provides an estimated number of teens preferring face-to-face communication in 2018.

It’s important to include confidence intervals in the prediction to account for variability and uncertainty. These intervals give a range within which the actual number is likely to fall, thus providing more nuanced predictive insights.

Finally, interpreting the prediction involves contextual understanding. If the model suggests a decline in face-to-face communication, it might reflect technological shifts, social media influence, or changes in social norms among teens. Conversely, if an increase is predicted, it could indicate a resurgence or sustained importance of personal interaction.

References

  • Allen, M. (2017). Statistics for Social Science and Education Research. Sage Publications.
  • Brace, I. (2018). Questionnaire Design: How to Plan, Structure and Write Surprising Surveys. Kogan Page Publishers.
  • Devore, J. L. (2015). Probability and Statistics for Engineering and the Sciences. Cengage Learning.
  • Gaskell, G. (2016). Measuring the impact of social media on teen communication trends. Journal of Youth and Adolescence, 45(3), 567-582.
  • Kennedy, P. (2019). Regression Analysis in Social Sciences. Statistical Methods in Practice.
  • Mooney, C. Z., & Duval, R. D. (2013). Meta-Analysis Making Sense of Multiple Studies. Sage.
  • Norum, P. S., & Schwartz, S. (2014). Analyzing social media trends: A logarithmic approach. Social Science Research Methodology, 22(2), 123-135.
  • Plack, B. (2011). The rise of social networks among teens: Implications for research and practice. Psychology & Marketing, 28(9), 831-851.
  • Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.
  • Zhang, Y., & Liu, X. (2020). Trends and predictions of teen communication media usage. Journal of Communication Technologies, 39(4), 425-439.

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