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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|
When there is an identifiable trend in the data (i.e., at least a moderately strong correlation between x and y), we often want to model this relationship so that we can interpolate (estimate the value of y for any given value of x within the range of data we have) and extrapolate (predict the value of y for any given value of x beyond our range of data).
To model relationships, we can use a line or curve. The type of curve that best fits the data below is logistic.
There are many possibilities for which functions you could use to model the data, but the simplest is with a line. Therefore, this is called linear regression.
As you saw in Lesson 14, each x-value is and each y-value is . The line used to model the trend between the ’s and ’s is called the regression line or line of best fit.