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Lesson 1  Introduction to Statistical Research Methods 
Lesson 2  Visualizing Data 
Lesson 3  Central Tendency 
Lesson 4  Variability 
Lesson 5  Standardizing 
Lesson 6  Normal Distribution 
Lesson 7  Sampling Distributions 
Lesson 8  Estimation 
Lesson 9  Hypothesis Testing 
Lesson 10  tTests for Dependent Samples 
Lesson 11  tTests for Independent Samples 
Lesson 12  Intro to OneWay ANOVA 
Lesson 13  OneWay ANOVA: Test significance of differences 
Lesson 14  Correlation 
Lesson 15  Linear Regression 
Lesson 16  ChiSquared Tests 
Afterward  
Index 
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 xvalue is and each yvalue is . The line used to model the trend between the ’s and ’s is called the regression line or line of best fit.
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