Reference Uploaded Sun Coast PDF Describe The Sun Coast Data

Reference Uploaded Sun Coastpdfdescribe The Sun Coast Data Sunco

Reference - Uploaded - Sun Coast.pdf Describe the Sun Coast data ( SunCoastDataFiles_StudentGuide.xlsx ) using the descriptive statistics tools . Establish whether assumptions are met to use parametric statistical procedures. Repeat the tasks below for each tab in the ( Uploaded - SunCoastDataFiles_StudentGuide.xlsx ). Utilize the template ( Uploaded - Unit IV - Template.pdf ) You will conduct the data analysis using Microsoft Excel Toolpak. View these links for information: and Here are some of the items you will cover.

Produce a frequency distribution table and histogram. Generate descriptive statistics table, including measures of central tendency (mean, median, and mode), kurtosis, and skewness. Describe the dependent variable measurement scale as nominal, ordinal, interval, or ratio. Analyze, evaluate, and discuss the above descriptive statistics in relation to assumptions required for parametric testing. Confirm whether the assumptions are met or are not met.

Paper For Above instruction

The Sun Coast data set, contained within the SunCoastDataFiles_StudentGuide.xlsx, provides a comprehensive collection of variables pertinent to regional economic, demographic, and social indicators. By applying descriptive statistics tools within Microsoft Excel's Toolpak, one can thoroughly analyze each dataset tab to elucidate key characteristics, tendencies, and distributions of the data. This process serves as a foundational step in determining the appropriateness of subsequent parametric statistical procedures.

Firstly, the analysis involves generating frequency distribution tables and histograms for the variables within each tab of the dataset. These visual and tabular summaries facilitate an initial understanding of data distribution, shape, and variability. For categorical variables, frequency tables reveal the policy, demographic, or economic categories' relative frequencies. Histograms further illustrate the distribution shape—whether symmetric, skewed, or uniform—and help identify outliers or irregularities that might influence the choice of statistical tests.

Subsequently, descriptive statistics are calculated for continuous variables, including measures of central tendency—mean, median, and mode—as well as measures of dispersion such as kurtosis and skewness. The mean provides an average measurement, the median offers the midpoint, and the mode indicates the most frequently occurring value, together offering a comprehensive view of the data's distribution. Kurtosis indicates the peakedness or flatness of the distribution, whereas skewness measures asymmetry, both of which are critical for assessing the normality assumption underlying parametric tests.

A critical aspect of this analysis is classifying the measurement scale of the dependent variables. Typically, such variables are measured as nominal (categorical without order), ordinal (categorical with order), interval (numeric with equal intervals but no true zero), or ratio (numeric with a true zero point). Clearly establishing the measurement scale influences the selection of appropriate statistical procedures and informs assumptions analyses.

To determine whether the data meet assumptions requisite for parametric testing, the normality of the distribution is scrutinized. This involves examining skewness and kurtosis values, along with visual cues from histograms and Q-Q plots if available. Generally, skewness and kurtosis values within the range of -1 to +1 suggest approximately normal distribution, thereby satisfying normality assumptions. If data significantly deviate, transformations or non-parametric alternatives may be necessary.

Repeated assessments across each tab ensure comprehensive understanding of the dataset's characteristics, guiding appropriate analytical choices. For example, intervals approximating normality justify the use of t-tests, ANOVA, or regression. Conversely, skewed distributions or heteroscedasticity suggest employing non-parametric tests like the Mann-Whitney U or Kruskal-Wallis.

Overall, this systematic approach utilizing descriptive statistics tools enables a detailed characterization of the Sun Coast data, ensuring that subsequent inferential procedures are grounded on sound assumptions. It is an essential step in rigorous data analysis for regional studies, supporting valid conclusions and policy recommendations.

References

• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th ed.). Pearson.
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences. Cengage Learning.
• Ott, R. L., & Longnecker, M. (2015). An Introduction to Statistical Thinking. Cengage Learning.
• Levin, J., & Fox, J. A. (2014). Elementary Statistics in Social Research. Pearson.
• Moore, D. S., McCabe, G. P., & Craig, B. A. (2012). Introduction to the Practice of Statistics. W. H. Freeman.
• Everitt, B. S., & Hothorn, T. (2011). An Introduction to Applied Multivariate Data Analysis. Springer.
• Weiss, N. A. (2012). Introductory Statistics. Pearson.
• Baltagi, B. H. (2013). Econometric Analysis of Panel Data. John Wiley & Sons.
• Sheskin, D. J. (2011). Handbook of Parametric and Nonparametric Statistical Procedures. Chapman and Hall/CRC.