# Computing a bivariate regression

## Video: Computing a bivariate regression

In the last movie, we use correlations to look at the strength of association between two variables. However, correlations are standardized measures. That is, they don't involve a unit of measurement. It's not a correlation of 0.78 meters or anything. It's just a correlation of 0.78. And what that can be really handy, because it makes it easier to compare associations across different kinds of variables, it can also be really nice to put the association back into the original metric. To do that we'll look at another procedure that's very closely related to correlation and that has many of its advantages, but that also uses the original units of measurement.

## Computing a bivariate regression

In the last movie, we use correlations to look at the strength of association between two variables. However, correlations are standardized measures. That is, they don't involve a unit of measurement. It's not a correlation of 0.78 meters or anything. It's just a correlation of 0.78. And what that can be really handy, because it makes it easier to compare associations across different kinds of variables, it can also be really nice to put the association back into the original metric. To do that we'll look at another procedure that's very closely related to correlation and that has many of its advantages, but that also uses the original units of measurement.

That is bivariate linear regression. As a note SPSS has a wonderful new procedure called Automatic Linear Modeling that also performs linear regression which we'll cover a little bit later. For now though, it makes more sense to stick to the standard linear regression, because we're only using one predictor variable and automatic linear modeling seems to a little like overkill for that. And second, automatic linear modeling does an awful lot of work behind the curtains and it's kind of nice to keep things visible for right now. As that in mind here's how to do a bivariate linear regression in SPSS.

For this example, we'll be using the Google Search data again, Searches.sav, where we will be using the percentage of people in a state with bachelo'rs degrees or higher as a way of predicting the relative level of interest in Facebook as a Google Search topic. To do this we go first to Analyze and then we come down to Regression and we go to the second one down, Linear. We need to take our outcome variable, that is the thing we're trying to predict, and put it in the Dependent box.

This means dependent variable or the variable that depends on other variables. In this case, that's going to be Facebook, that is Facebook as a relative interest in Google searches. Independent is the variables that we're going to use as predictors, in this particular case I'm going to be using the Percent of Population with a bachelor's degree or higher. Now the linear regression command is actually tremendously sophisticated and gives tons of options. None of which I'm going to use at this particular moment. I'm doing the simplest possible version here of simply using the Percent of Population with bachelors degree or higher to predict Facebook interest on Google Searches.

And I'm going to do nothing else at this moment. All I'm going to do now is press OK. And I get a table that tells me the percent of population with a bachelor's degree or higher and that is using Facebook interests as a dependent variable. The next table down gives me an indication of the association. We have a correlation here of 0.644. That's the R. Now to capital R here, because that actually stands for multiple correlation which means you can use several variables to correlate with a single outcome.

Although in this case we only have two variables so it's still bivariate. And then you have another one here that's called R Square and that is that the 0.415 is the square of the number next to it, the 0.644. And the reason you do this is because you can't really compare correlation coefficients. They are not linear. A correlation of 0.4 is not twice as strong as a correlation of 0.2, even though the number is twice as big. Instead, if you square them then you get numbers that are directly comparable and a correlation of 0.4 squared becomes 0.16 and a correlation of 0.2 squared becomes 0.04.

And so the other correlation is actually four times as strong. You also have something called Adjusted R Squared. Sometimes people report R Squared, sometimes they report Adjusted R Squared. An Adjusted R Squared changes the number according to the ratio of observations to predictors. We also have the Standard Error of the Estimate that goes into the probability values. And the next table is the ANOVA or ANOVA table. That's short for analysis of variance and it's an indication of the statistical significance of the model as a whole.

If we had more than one predictor then this would be an important thing, but because we have only one predictor and we know it's statistically significant it doesn't really tell us anything extra right now. The next one down from that is coefficients, and what we see here is the slope and the intercept that we are familiar with from charting relationships. The Unstandardized Coefficients are the slope in the intercept in original units. And so what we see is if we're trying to predict the level of interest in Facebook on a state-by-state basis we have an intercept here of 3.240.

That says give everybody an interest of three standard deviations above the mean, but then for every percentage of the population that has a bachelors degree or higher, subtract a tenth of a point from that. That's the -0.119. And that means it's a downhill. The higher the level of education, the lower the interest in Facebook as a Google search term. This will become clearer if I quickly make a scatterplot of the association between the two variables. I've already shown how to make scatterplot, so I'm going to go through this a little bit quickly.

I come to Graphs to Chart Builder to Scatter, where I'm going to put level of education here in the X, and I'm going to put Facebook here in the Y and I'll just click OK. And it's clear. It's a very strong negative association. The higher the percentage of the population with a bachelors degree, the lower the relative interest in Facebook as a search term. So the similarities between bivariate correlation and bivariate regression, which we just did, are pretty easy to see in this example.

They both give the same standardized effects and the same P values. The difference is that the regression model also gives the intercept and slope for the model which is a nice piece of information. Also in a later section we'll see how this procedure can be very easily adapted to having several predictor variables, in which case it's called Multiple Regression. And while it's possible to use categorical predictors in linear regression, the basic approach doesn't work well when the outcome variable is categorical. Instead, it's more common to use cross tabulations, which we'll turn to next.

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#### This video is part of

SPSS Statistics Essential Training (2011)

52 video lessons · 20100 viewers

Author

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1. ### Introduction

2m 58s
1. Welcome
1m 5s
2. Using the exercise files
40s
3. Using a different version of the software
1m 13s
2. ### 1. Getting Started

19m 0s
1. Taking a first look at the interface
11m 49s
7m 11s
3. ### 2. Charts for One Variable

21m 54s
1. Creating bar charts for categorical variables
7m 18s
2. Creating pie charts for categorical variables
2m 54s
3. Creating histograms for quantitative variables
5m 45s
4. Creating box plots for quantitative variables
5m 57s
4. ### 3. Modifying Data

33m 10s
1. Recoding variables
5m 33s
2. Recoding with visual binning
5m 33s
3. Recoding by ranking cases
5m 26s
4. Computing new variables
5m 37s
5. Combining or excluding outliers
5m 21s
6. Transforming outliers
5m 40s
5. ### 4. Working with the Data File

28m 12s
1. Selecting cases
6m 44s
2. Using the Split File command
5m 12s
3. Merging files
5m 33s
4. Using the Multiple Response command
10m 43s
6. ### 5. Descriptive Statistics for One Variable

22m 14s
1. Calculating frequencies
8m 43s
2. Calculating descriptives
5m 31s
3. Using the Explore command
8m 0s
7. ### 6. Inferential Statistics for One Variable

16m 3s
1. Calculating inferential statistics for a single proportion
6m 6s
2. Calculating inferential statistics for a single mean
5m 39s
3. Calculating inferential statistics for a single categorical variable
4m 18s
8. ### 7. Charts for Two Variables

30m 43s
1. Creating clustered bar charts
7m 10s
2. Creating scatterplots
5m 8s
3. Creating time series
3m 24s
4. Creating simple bar charts of group means
4m 17s
5. Creating population pyramids
3m 0s
6. Creating simple boxplots for groups
3m 3s
7. Creating side-by-side boxplots
4m 41s
9. ### 8. Descriptive and Inferential Statistics for Two Variables

45m 28s
1. Calculating correlations
8m 17s
2. Computing a bivariate regression
6m 27s
3. Creating crosstabs for categorical variables
6m 34s
4. Comparing means with the Means procedure
6m 33s
5. Comparing means with the t-test
6m 4s
6. Comparing means with a one-way ANOVA
6m 30s
7. Comparing paired means
5m 3s
10. ### 9. Charts for Three or More Variables

24m 30s
1. Creating clustered bar charts for frequencies
6m 34s
2. Creating clustered bar charts for means
3m 45s
3. Creating scatterplots by group
4m 13s
4. Creating 3-D scatterplots
4m 25s
5. Creating scatterplot matrices
5m 33s
11. ### 10. Descriptive Statistics for Three or More Variables

30m 57s
1. Using Automatic Linear Models
11m 52s
2. Calculating multiple regression
9m 3s
3. Comparing means with a two-factor ANOVA
10m 2s
12. ### 11. Formatting and Exporting Tables and Charts

29m 29s
1. Formatting descriptive statistics
6m 1s
2. Formatting correlations
7m 49s
3. Formatting regression
10m 19s
4. Exporting charts and tables
5m 20s
13. ### Conclusion

51s
1. What's next
51s

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