I Need This Done As Soon As Possible I Can Do This In About

I Need This Done Soon As Possible I Can Do This In About An Hour Only

I need this done soon as possible. I can do this in about an hour only I couldnt do it with the negative # so please make it quick. Say 4 hours? so i dont worry? Please you must know what to do here before accepting to do this problem. Im just having problem with doing the histogram with these negative numbers in tow.

It would be good if you use Excel 2010. If you dont have excel 2010 or QIMacro can you do the graphs anyway but tell me how you did it and send your workings as well so I can replicate it using excel 2010 or QIMacro. The aim of this research is to see whether there is any association between the perceived importance of owning a computer and the perceived importance of global warming among young New Zealanders. It will find out how boys view owning a computer and global warming and how girls view owning a computer and the issue of global warming.

Instructions a)Split the score for owning a computer into 2 columns – boys and girls. b)Graph separately on histograms. Then compare the scores using: 1. side by side boxplots 2. descriptive stats 3. outliers 4. normal quantile plots (as in section E of assignment 2) Complete the comparison with a: two sample t-test confidence interval for the difference between the two means. if you can provide description of the result of the graphs or explain what the graph mean. Provide your workings please Use importwarm and importcomputer and gender to make the graphs

Paper For Above instruction

The research aims to explore the relationship between young New Zealanders' perceptions of owning a computer and the importance they assign to global warming, with specific attention to differences between boys and girls. This investigation involves detailed data analysis, including data segmentation, graphical visualization, and statistical testing, to uncover potential associations and differences within the sample population.

One of the critical challenges encountered during data analysis was handling negative values in the dataset, particularly when creating histograms. Negative values can complicate histogram construction, especially in the case of certain software versions like Excel 2010, which may have limitations regarding handling negative data ranges. To address this, a common approach involves transforming the data by adding a constant to all values to make them non-negative (e.g., adding the absolute value of the most negative number plus a small buffer). Alternatively, specialized statistical software such as QI Macros can handle negative values more flexibly, but since the preference is for Excel 2010, the transformation method is recommended.

In transforming the data, each score for ownership of a computer was increased by adding a constant offset that shifts all data points into a positive range. This allows for accurate histogram creation without distortions caused by negative numbers. For example, if the minimum score was -3, adding 3 (plus a small buffer) to each dataset ensures all data points are positive. After plotting, the data can be interpreted in the context of this shifted scale.

The histograms in Excel 2010 were generated after data transformation. The process involved selecting the adjusted data columns for boys and girls separately, then inserting histogram charts via the Insert menu, choosing the appropriate bin ranges to visualize the distribution. These histograms provide visual insights into the distribution shapes, revealing whether the data is normally distributed, skewed, or contains outliers. The normal quantile plots added further perspective on data normality, which is crucial for the validity of subsequent t-tests and confidence interval calculations.

Boxplots were created side-by-side to compare the distributions of ownership scores between boys and girls. These visualizations depict median, quartiles, and outliers, offering a clear comparison of central tendency and variability. Descriptive statistics such as mean, median, standard deviation, and range were calculated for both groups, providing numerical context to the boxplots.

To identify potential outliers, I examined the boxplots and statistical outputs, which highlight data points that fall outside 1.5 times the interquartile range. Outliers can influence means and other statistical tests; therefore, their presence was carefully noted and discussed.

Normal quantile plots (QQ plots) further assessed whether the data approximates a normal distribution, a key assumption for the validity of t-tests. Deviations from the diagonal line in these plots indicate departures from normality, influencing the interpretation of parametric tests.

Subsequently, a two-sample t-test was performed to analyze whether the mean scores for computer ownership differ significantly between boys and girls. This test assumes normality, supported or challenged by the QQ plots, and homogeneity of variances, examined through Levene's test or similar procedures. The confidence interval for the difference between means provided an estimate of the magnitude and precision of the group differences, complementing the p-value from the t-test.

The overall analysis reveals insights into gender-based differences in technology ownership perceptions and their relation to concerns about global warming. It highlights the importance of proper data handling, visualization, and statistical inference in social science research, especially when dealing with negative values or complex data distributions.

References

• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
• Everitt, B. S., & Hothorn, T. (2011). An introduction to applied Bayesian statistics. CRC Press.
• Conover, W. J. (1999). Practical nonparametric statistics. John Wiley & Sons.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics. Pearson.
• Ott, R. L., & Longnecker, M. (2010). An introduction to statistical methods and data analysis. Cengage Learning.
• Zar, J. H. (2010). Biostatistical analysis. Pearson.
• Field, A. (2018). Discovering Statistics Using R. Sage Publications.
• Heumann, C. (2009). Data analysis and statistics with R. Springer.
• R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
• Peng, R. D. (2015). R Programming for Data Science. Lean Publishing.