Week 11 correlation linear regression
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Week 11 Correlation & Linear Regression. Administrative Tasks. Turn in your HW Sample Research Paper Buckle your seatbelts we have a lot to cover. Scatterplots. Y is the vertical axis X is the horizontal axis Dots are observations The intersection of the IV & DV for each unit of analysis

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Week 11 Correlation & Linear Regression

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Week 11 correlation linear regression

Week 11 Correlation & Linear Regression


Administrative tasks

Administrative Tasks

  • Turn in your HW

  • Sample Research Paper

  • Buckle your seatbelts we have a lot to cover


Scatterplots

Scatterplots

  • Y is the vertical axis

  • X is the horizontal axis

  • Dots are observations

    • The intersection of the IV & DV for each unit of analysis

  • What can you infer from about the IV/DV relationship from the scatterplot on the left?

  • Presentation can be deceiving


Three factors to consider when analyzing scatterplots

Three factors to consider when analyzing scatterplots

  • Directionality

    • Do the dots appear to flow in a particular direction?

    • Or, does the scatterplot look more like white noise (like a TV when it doesn’t have a signal)?

    • The more it looks like white noise the less the two variables are related

  • Clustering

    • Are the majority of the dots in a small area of the graph?

    • How would this impact our confidence in predictions outside of this area?

  • Outliers

    • Are there cases that differ markedly from the overall pattern of the dots? i.e. not near the cluster or contrary to the directionality

    • Which observations are these? How influential are these cases?


Pearsons s correlation coefficient quantifying scatter

Pearsons’s correlation coefficient: quantifying scatter

  • To estimate the strength of the relationship between two interval level variables we can calculate Pearson’s correlation coefficient (r)

    • Values range from -1 to +1

      • -1 = perfectly negative association

      • +1 = perfectly positive association

      • 0 = no association

  • The perks of Pearson:

    • Direction

    • Magnitude (of predictive power)

    • Impervious to the units in which the variables are measured


The downside correlation does not equal causation

The downside - correlation does not equal causation


Scatterplots and r values

Scatterplots and r values


Calculating r

Calculating r

  • “That’s scarier than anything I saw on Halloween!”

  • 1. Subtract each observations x value from x’s mean and multiply it by the difference between its y value and the mean of y

  • 2. Do that for each observation and sum them all together

  • 3. Divide that sum by n-1 times the s.d. of x & the s.d. of y


The real good news you don t have to actually do that

The real good news? You don’t have to actually do that

  • Excel is your best buddy & will do all the hard work for you

  • Not only that, but Excel will also allow you to create a scatterplot and show you a line depicting this relationship

  • Let’s check it out:


Tell me more about this line

“Tell me more about this line!”

  • Slope:

    • Change in Y divided by change in X

  • Intercept

    • Value of Y when X = O

  • Error

    • Distance between the line’s Y value and a data point’s Y value

  • The line minimizes the sum of all the squared errors

  • “I love line!”


Recipe for creating the line

Recipe for creating the line

  • The line rarely, if ever, passes through every point

  • There is an error component

  • Thus, the actual values of Y can be explained by the formula:

  • Y=α+βX+ε

    • α - Alpha – an intercept component to the model that represents the models value for Y when X=0

    • β - Beta – a coefficient that loosely denotes the nature of the relationship between Y and X and more specifically denotes the slope of the linear equation that specifies the model

    • ε - Epsilon – a term that represents the errors associated with the model


This is ordinary least squares ols or linear regression

This is ordinary least squares (OLS) or linear regression

  • The Goal:

    • Minimize the sum of the squared errors

  • Consider the impact of outliers

  • How many ways can a line be created?


Not gonna do it wouldn t be prudent

“Not gonna do it. Wouldn’t be prudent.”

  • You know the trick:

    • It’s not as hard as it looks

  • You are really comparing Y’s deviations from it’s mean alongside X’s deviation from it’s mean

    • See the formula at the bottom of pg. 331

  • Ideally the Xi’s move in sync with the Yi’s divergence from the mean

    • This is covariation


The really good news excel does it all for you

The Really Good News? Excel does it all for you!

  • Enter the data into Excel

  • Click the “Data” tab at the top

  • In the Data tab look all the way to the right and click on “Data Analysis”

  • In the Analysis Tools menu click on Regression and hit Ok

  • Highlight the appropriate columns in the “Input Y Range” & Input X Range” fields

  • Check the labels option & hit Ok

  • Instant regression results!


What to look for when examining regression output

What to look for when examining regression output

  • Beta coefficient:

    • Directionality

    • Size of the coefficient

    • Standard Error

    • Statistical Significance

  • Constant

    • Far less important than Beta

    • When X = O what would we expect Y to be?

      • Is X ever O?

  • Goodness of fit

    • How much of the variation in Y is actually explained by X?

    • How “good” does your model “fit” the actual values of Y?

    • R-squared (the coefficient of determination) provides an estimate


R strikes back

r Strikes Back!

  • Recall that r, Pearson’s correlation coefficient, measures the degree to which two variables co-vary

  • With OLS the:

    • Constant tells us where the line starts

    • Beta tells us how the line slopes

    • R-squared tells us the % of the variation in Y our model predicts

      • Range 0-1

        • O = Predicts none of the variation

        • 1 = Predicts all of the variation


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