PURCHASE A DIGITAL COPY
PURCHASE A HARD COPY
|Lesson 1||Introduction to Statistical Research Methods|
|Lesson 2||Visualizing Data|
|Lesson 3||Central Tendency|
|Lesson 6||Normal Distribution|
|Lesson 7||Sampling Distributions|
|Lesson 9||Hypothesis Testing|
|Lesson 10||t-Tests for Dependent Samples|
|Lesson 11||t-Tests for Independent Samples|
|Lesson 12||Intro to One-Way ANOVA|
|Lesson 13||One-Way ANOVA: Test significance of differences|
|Lesson 15||Linear Regression|
|Lesson 16||Chi-Squared Tests|
Keep the standard deviation in the back of your head for the time being, and let’s move on to a different but related question. If you know a particular value, how can you describe how this value compares to others in the dataset?
For example, Katie plays competitive chess, and her United States Chess Federation rating is 1800. We know that the higher the number, the better the rating. But just how good is a rating of 1800? We could say that Katie’s rating is lower than 8110 other chess players, but we don’t know how many chess players exist in total.
A more descriptive way to compare a rating of 1800 to other ratings is to look at the distribution of ratings of other US players.
In this case, we get 88% by adding all the absolute frequencies for each bin up to a rating of 1800, and then dividing by the total number
of chess players. It’s easier to analyze proportions and percentages using relative, rather than absolute, frequencies.