Street-Smart Stats cover
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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 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 14 Correlation
Lesson 15 Linear Regression
Lesson 16 Chi-Squared 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.

1

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.

2

 

As you saw in Lesson 14, each x-value is xi and each y-value is yi. The line used to model the trend between the xi’s and yi’s is called the regression line or line of best fit.

To continue learning, purchase Street-Smart Stats: A Friendly Introduction to Statistical Research Methods.
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