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# W1 Discussion Topics Essay

1715 words - 7 pages

This week there are four discussion topics. Complete by the due date and you will have a second chance to fix mistakes and resubmit for a second review and increased points.

Name:

DT1 DESCRIPTIVE STATISTICS

Topics: Central Tendency, Variability (Dispersion), Quartiles, normal data and skewed distribution of a single data group. You will want to know what measure to use when data distribution is normal (symmetrical) and when the data distribution is significantly skewed. We will work with six measures, mean, median, mode, standard deviation, interquartile range (IQR), and range. You may use a web search or the textbook.

Textbook: ...view middle of the document...

Explain the strengths and weaknesses for each measure of variability: a) Standard Deviation, b) Range, c) Interquartile Range

Standard Deviation
Advantages – Shows better picture of results than from the mean alone
Disadvantages – Doesn’t show full range of data, and can be effected by outliers

Range
Disadvantages – Doesn’t consider clusters of results. Effected by outliers more than any other variability.

Interquartile Range
Advantages – Doesn’t show extreme values. Can also measure variability if extreme values are not recorded exactly
Disadvantages – No open to manipulation

D. Choose the best measure to use when data is normal? Choose the best measure to use when data is significantly skewed?

The range would be a good method to use for normal data. However standard deviation would be best because of its usefulness of the scatter of observations. If observations follow normal distribution, a range covered by one SD above the mean and one below included 68% of the observations. A range of two and two would be about 95%, and a range of three and three would be about 99% of the observations.

Normal data has outliers. Range would pick the min and max points, thus would not be a good choice. Best: Standard deviation.

Skewed data. The standard deviation uses the mean in the calculation. Best: interquartile range.

Shape (Skewness)

E. Skewness may be caused by data favoring one side of the mean, from outliers, or the data is not normal.

1. Give an example when data favors one side of the mean.

During a sports players’ career, as he/she is younger, their statistics would most likely be better during their first few years of playing, while the last half of their career would most likely see a decline. A positive skew.
[pic]

2. Give an example of data that has extreme outliers.

When a ball player plays a period of time without using performance enhancing drugs he/she will be most likely consistent year after year. However, if that same player uses these PED’s and has a astonishing season of play, those numbers (in baseball most likely home runs) would show as an extreme outlier.
DT2 IDENTIFYING LEVELS OF MEASUREMENT

Topics: Four types of levels of measurement (scales) of data classification. Learn that data levels are either numeric or non-numeric. Learn about the two sub-categories for each level. You may use a web search or the textbook.

Numeric Interval, Ratio
Attribute: Nominal, Ordinal

Textbook:
McClave Ch. 2 p. 2, Types of Data
Cooper & Schindler Ch. 11, pp. 273-278 Measurement Scales

Instructions:
Consider each Part as a single question on a survey questionnaire.

a. Select whether your answer would produce attribute or numeric data, then decide on nominal, ordinal, interval, ratio if you can.

b. Why did you make that your choice? This helps me to understand...

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