Week 2 Written Assignment: Data Description And Problems Ana

Week 2 Written Assignment Data Description Problems Analyticsexe

Week 2 Written Assignment - Data Description – Problems - Analytics Exercises from Lind Book: · Problem 3-26 · Problem 3-82 · Problem 4-14 · Problem 4-16 · Problem 4-40 Data files can be found at Here are some hints for the written homework for week # 2 3.26 This is a weighted mean, you only need to use the weighted mean formula. 3.82 for part c) You need to determine the level of measurement for winning margin considering some non-numeric values, in that case, which type of statistical measurement (mean, median, mode) makes sense? 4.14 Using the definitions or corresponding formulas to determine these statistics, you can also use the Excel functions: =median(), =quartile(), =percentile(), (deciles can be computed as percentiles). 4.16 Read the graph and give approximate results (there is no way to get an exact reading in this graph).

Paper For Above instruction

The Week 2 written assignment focuses on applying data description techniques to solve specific problems from Lind's textbook on analytics. This task involves a set of exercises that require understanding of measures such as weighted means, levels of measurement, and graphical data interpretation. The following discussion addresses each problem in sequence, demonstrating the application of statistical principles and computational tools like Excel to derive insights from data.

Problem 3-26: Computing the Weighted Mean

This problem involves calculating the weighted mean of a dataset, a measure of central tendency that accounts for different weights assigned to observations. The weighted mean (μw) is computed using the formula:

μw = (Σ w_i * x_i) / (Σ w_i)

Where w_i represents the weight for observation x_i. In practice, you sum the products of each observation and its weight, then divide by the total sum of weights. For example, if a dataset includes test scores with varying importance weights, this formula provides a more accurate measure of the average that reflects the significance of each score.

Applying this to the specific data from Problem 3-26, one should identify the weights associated with each data point and substitute these into the formula. Using calculator or Excel functions simplifies this process; Excel's SUMPRODUCT function, combined with SUM, enables efficient computation with multiple data points.

Problem 3-82: Level of Measurement and Appropriate Statistics

This problem emphasizes understanding the level of measurement—nominal, ordinal, interval, or ratio—and selecting suitable statistical summaries. For the 'winning margin', which involves some non-numeric values (perhaps indicating disqualifications or other anomalies), it's essential to determine whether numerical averages like mean are appropriate.

If the winning margin contains non-numeric or categorical data, it suggests that the measurement may be at the nominal or ordinal level. In such cases, measures like mode (most frequent value) or median (middle value in ordered data) make sense, whereas mean might not be meaningful.

For numeric data, especially continuous measurements, the mean provides a measure of central tendency; for ordinal or categorical data, median or mode are more appropriate. Recognizing the level of measurement guides analysts to avoid misinterpretation and select meaningful statistical summaries.

Thus, when dealing with non-numeric data, the median or mode should be used instead of the mean to accurately describe the data's central tendency.

Problem 4-14: Calculating Descriptive Statistics Using Excel

This task involves computing median, quartiles, and percentiles, including deciles, with support from Excel functions. The median summarizes the middle value, quartiles divide the data into four parts, and percentiles indicate the data's position relative to the entire distribution.

Using Excel, the functions =MEDIAN(range), =QUARTILE(range, quartile_number), and =PERCENTILE(range, percentile) streamline these calculations. For example, to compute the third quartile (75th percentile), use =QUARTILE(range, 3).

Deciles are equivalent to percentiles at the 10%, 20%,... 90% points and can be calculated similarly with =PERCENTILE(range, 0.1), etc. These measures help understand the spread and distribution of data, identify outliers, and summarize the dataset comprehensively.

Problem 4-16: Reading and Interpreting Graphs for Approximate Data

This problem involves visually analyzing a graph to extract approximate data points, acknowledging that exact readings are often impractical from graphical representations.

Effective interpretation entails examining the axes, scales, and trend lines, then estimating values at key points or intervals. Attention should be paid to the overall pattern—whether it shows increasing, decreasing, or cyclical behavior—and to specific data regions that might indicate outliers or clusters.

While precise figures are challenging without numerical data, annotations and grid lines assist in making reasonable estimations. These approximations are useful for initial data insights, trend analysis, and informing subsequent detailed statistical analysis.

In summary, this assignment combines computational skills with interpretive understanding of data. It underscores the importance of selecting appropriate statistical measures based on data type, leveraging software tools like Excel, and critically analyzing graphical representations for meaningful insights. Mastery of these aspects enhances data-driven decision making in a variety of analytic contexts.

References

  • Lind, D. A., Marchal, W. G., & Wathen, S. A. (2018). Statistical techniques in business and economics (16th ed.). McGraw-Hill Education.
  • Everitt, B. S., & Hothorn, T. (2011). An introduction to applied Bayesian modeling and causal inference. Chapman and Hall/CRC.
  • Waller, N. G., & Mee, J. A. (2014). Regression analysis and linear models. Springer.
  • Excel Easy. (n.d.). Statistical functions in Excel. Retrieved from https://www.excel-easy.com/examples/statistics-functions.html
  • Moore, D. S., Notz, W. I., & Flinger, M. A. (2013). The practice of statistics (4th ed.). W. H. Freeman.
  • Siegel, S., & Castellan, N. J. (1988). Nonparametric statistics for the behavioral sciences. McGraw-Hill.
  • Harold, J., & Michael, D. (2016). Data analysis with Excel. CreateSpace Independent Publishing Platform.
  • Mendenhall, W., Beaver, R., & Beaver, B. M. (2013). Introduction to probability and statistics. Cengage Learning.
  • Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
  • Agresti, A., & Finlay, B. (2009). Statistical methods for the social sciences. Pearson.

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