Course Outline

list High School / Advanced Statistics and Data Science I (ABC)

Book
  • High School / Advanced Statistics and Data Science I (ABC)
  • High School / Statistics and Data Science I (AB)
  • High School / Statistics and Data Science II (XCD)
  • High School / Algebra + Data Science (G)
  • College / Introductory Statistics with R (ABC)
  • College / Advanced Statistics with R (ABCD)
  • College / Accelerated Statistics with R (XCD)
  • CKHub: Jupyter made easy

13.2 What You have Learned about Modeling Variation

At first, this was all we could do. You could spot a relationship in a graph, but you weren’t able to quantify it. But now you can do so much more! You can actually specify and fit models to the data, and figure out how strong the relationship is!

Below we’ve re-drawn the graphs to include the best-fitting regression line. (You totally could have done this yourself; feel free to go back and try it if you want.)

A scatter plot of the distribution of avg_hatecrimes_per_100k_fbi by share_unemp_seas in hate_crimes overlaid with a best-fitting regression line on the left. A scatter plot of the distribution of avg_hatecrimes_per_100k_fbi by median_house_inc in hate_crimes overlaid with a best-fitting regression line on the right. The slope of the right distribution is steeper than the slope of the left distribution.

Use the code window below to find the best-fitting estimates of these two models.

require(coursekata) # find and print the best-fitting estimates for the unemployment model # find and print the best-fitting estimates for the income model # find and print the best-fitting estimates for the unemployment model lm(avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes) # find and print the best-fitting estimates for the income model lm(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) not_called = "Did you use the lm() function to find and print the best-fitting estimates?" ex() %>% { check_function(., "lm", index = 1, not_called_msg = not_called) %>% check_result() %>% check_equal(incorrect_msg = "Did you model avg_hatecrimes_per_100k_fbi as a function of share_unemp_seas in the hate_crimes data frame?") check_function(., "lm", index = 2, not_called_msg = not_called) %>% check_result() %>% check_equal(incorrect_msg = "Did you model avg_hatecrimes_per_100k_fbi as a function of median_house_inc in the hate_crimes data frame?" ) }
Call:
lm(formula = avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes)

Coefficients:
     (Intercept)  share_unemp_seas
            1.77             11.99

Call:
lm(formula = avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes)

Coefficients:
     (Intercept)  median_house_inc
      -9.564e-01         6.054e-05

Just from our visualizations, we got the impression that the median household income would explain more of the variation in hate crimes than would unemployment. But median household income has a very small slope: .00006, compared to 11.99 for unemployment.

You know by now that to get these statistics, you will need to examine the ANOVA tables for the two models. How did you ever get along without the supernova() function before? Use the code window below (where we have fit two models for you: unemp_model and income_model) to get the ANOVA tables for the two models.

require(coursekata) # this code fits the models unemp_model <- lm(avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes) income_model <- lm(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # print the supernova table for unemp_model # print the supernova table for income_model # this code fits the models unemp_model <- lm(avg_hatecrimes_per_100k_fbi ~ share_unemp_seas, data = hate_crimes) income_model <- lm(avg_hatecrimes_per_100k_fbi ~ median_house_inc, data = hate_crimes) # print the supernova table for unemp_model supernova(unemp_model) # print the supernova table for income_model supernova(income_model) msg = "Did you call supernova() on both unemp_model and income_model?" ex() %>% { check_output_expr(., "supernova(unemp_model)") check_output_expr(., "supernova(income_model)") }
Analysis of Variance Table (Type III SS)
Model: avg_hatecrimes_per_100k_fbi ~ share_unemp_seas

                             SS df    MS     F    PRE     p
----- --------------- | ------- -- ----- ----- ------ -----
Model (error reduced) |   0.787  1 0.787 0.264 0.0055 .6099
Error (from model)    | 143.206 48 2.983
----- --------------- | ------- -- ----- ----- ------ -----
Total (empty model)   | 143.993 49 2.939
Analysis of Variance Table (Type III SS)
Model: avg_hatecrimes_per_100k_fbi ~ median_house_inc

                             SS df     MS     F    PRE     p
----- --------------- | ------- -- ------ ----- ------ -----
Model (error reduced) |  14.584  1 14.584 5.409 0.1013 .0243
Error (from model)    | 129.409 48  2.696
----- --------------- | ------- -- ------ ----- ------ -----
Total (empty model)   | 143.993 49  2.939

From the two models we fit here, we would say that the income model explains more variation in hate crimes than does the unemployment model. States with higher household incomes seem to report more hate crimes to the FBI. This relationship isn’t perfectly predictive, but it does explain 10% of the total error around the empty model.

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