Course Outline

segmentGetting Started (Don't Skip This Part)

segmentStatistics and Data Science: A Modeling Approach

segmentPART I: EXPLORING VARIATION

segmentChapter 1  Welcome to Statistics: A Modeling Approach

segmentChapter 2  Understanding Data

segmentChapter 3  Examining Distributions

segmentChapter 4  Explaining Variation

segmentPART II: MODELING VARIATION

segmentChapter 5  A Simple Model

segmentChapter 6  Quantifying Error

segmentChapter 7  Adding an Explanatory Variable to the Model

segmentChapter 8  Models with a Quantitative Explanatory Variable

segmentPART III: EVALUATING MODELS

segmentChapter 9  The Logic of Inference

segmentChapter 10  Model Comparison with F

10.5 Using F to Test a Regression Model

segmentChapter 11  Parameter Estimation and Confidence Intervals

segmentPART IV: MULTIVARIATE MODELS

segmentChapter 12  Introduction to Multivariate Models

segmentChapter 13  Multivariate Model Comparisons

segmentFinishing Up (Don't Skip This Part!)

segmentResources
list College / Advanced Statistics and Data Science (ABCD)
10.5 Using F to Test a Regression Model
We can use sampling distributions of \(b_1\) or \(F\) to compare the empty model versus a twogroup model. It turns out we can do the same for regression models. In chapter 9, we used a sampling distribution of \(b_1\) to test the \(b_1\) in a regression model. Now let’s use the Fdistribution to see how the F ratio from a regression model compares to the Fs generated by the empty model of the DGP.
Recap: Using Check to Predict Tips
In Chapter 9 we created a regression model that used the total check for each table (Check
) to predict the outcome Tip
. We were interested in exploring the model: Tip = Check + Other Stuff. To remind us of this example, here is a scatterplot of the data as well as the best fitting regression model depicted as a blue line.
gf_point(Tip ~ Check, data = TipExperiment) %>%
gf_lm(color = "blue")
Using lm()
, we fit the regression model and found the best fitting parameter estimates:
lm(Tip ~ Check, data = TipExperiment)
Call:
lm(formula = Tip ~ Check, data = TipExperiment)
Coefficients:
(Intercept) Check
18.74805 0.05074
The bestfitting \(b_1\) estimate was 0.05, meaning that for every one dollar increase in the total check, the Tip
increased, on average, by 5 cents. This is the slope of the regression line. But while this is the bestfitting estimate of the slope based on the data, is it possible that this \(b_1\) could have been generated by the empty model, one in which the true slope in the DGP is 0?
In Chapter 9 we asked this question using the sampling distribution of \(b_1\). But we can ask the same question using the sampling distribution of F. Let’s start by finding the F for the Check
model. You can do this in the code window below using the fVal()
function.
require(coursekata)
# Simulate Check
set.seed(22)
x < round(rnorm(1000, mean=15, sd=10), digits=1)
y < x[x > 5 & x < 30]
TipPct < sample(y, 44)
TipExperiment$Check < (TipExperiment$Tip / TipPct) * 100
# calculate the sample F for the Check model
fVal()
# calculate the sample F for the Check model
fVal(Tip ~ Check, data = TipExperiment)
ex() %>% {
check_function(., "fVal") %>%
check_result() %>%
check_equal()
}
18.8653561562528
The F ratio for the Check
model is 18.87, which suggests that the variance explained by the model is 18.87 times the variance left unexplained by the model. Clearly, the Check
model explains more variation than the empty model in the data.
But is it possible that the sample F could have been generated by a DGP in which there is no effect of Check
on Tip
? To answer this question we will need the sampling distribution of F generated under the empty model in which the true slope of the regression line in the DGP is 0.
Creating a Sampling Distribution of F
Following the same approach as we used for group models, we can use shuffle()
to simulate many samples from a DGP in which there is no effect of Check
on Tip
, fit the Check
model to each shuffled sample, and then calculate the corresponding value of F.
Write code to create a randomized sampling distribution of 1000 Fs (called sdoF
) under the assumption that Check
has no relationship with Tip
. Print the first six lines of sdoF
.
require(coursekata)
# Simulate Check
set.seed(22)
x < round(rnorm(1000, mean=15, sd=10), digits=1)
y < x[x > 5 & x < 30]
TipPct < sample(y, 44)
TipExperiment$Check < (TipExperiment$Tip / TipPct) * 100
# create a sampling distribution of 1000 Fs generated from the empty model
# print the first 6 rows
# create a sampling distribution of 1000 Fs generated from the empty model
sdoF < do(1000) * fVal(shuffle(Tip) ~ Check, data = TipExperiment)
# print the first 6 rows
head(sdoF)
ex() %>% {
check_object(., "sdoF") %>%
check_equal()
check_function(., "head") %>%
check_arg("x") %>%
check_equal()
}
fVal
1 0.350193090
2 1.819454478
3 0.162724620
4 0.002413171
5 0.496788060
6 0.121889251
Use the code block below to make a histogram of the randomized F ratios.
require(coursekata)
# Simulate Check
set.seed(22)
x < round(rnorm(1000, mean=15, sd=10), digits=1)
y < x[x > 5 & x < 30]
TipPct < sample(y, 44)
TipExperiment$Check < (TipExperiment$Tip / TipPct) * 100
# this creates a sampling distribution of 1000 Fs generated from the empty model
sdoF < do(1000) * fVal(shuffle(Tip) ~ Check, data = TipExperiment)
# make a histogram depicting the sdoF
# this creates a sampling distribution of 1000 Fs generated from the empty model
sdoF < do(1000) * fVal(shuffle(Tip) ~ Check, data = TipExperiment)
# make a histogram depicting the sdoF
gf_histogram(~ fVal, data = sdoF)
ex() %>% check_or(
check_function(., "gf_histogram") %>% {
check_arg(., "object") %>% check_equal()
check_arg(., "data") %>% check_equal()
},
override_solution_code(., '{
sdoF < do(1000) * fVal(shuffle(Tip) ~ Check, data = TipExperiment)
gf_histogram(sdoF, ~ fVal)
}') %>%
check_function(., "gf_histogram") %>% {
check_arg(., "object") %>% check_equal()
check_arg(., "gformula") %>% check_equal()
},
override_solution_code(., '{
sdoF < do(1000) * fVal(shuffle(Tip) ~ Check, data = TipExperiment)
gf_histogram(~sdoF$fVal)
}') %>%
check_function(., "gf_histogram") %>% {
check_arg(., "object") %>% check_equal()
}
)
As you can see, the sampling distribution of F created based on the assumption that the empty model is true is still shaped like the F distribution, even though the model is a regression model and not a group model.
Use the code window and the tally()
function to find out how many (or what proportion) of the 1000 Fs were greater than the sample F of 18.87.
require(coursekata)
# Simulate Check
set.seed(22)
x < round(rnorm(1000, mean=15, sd=10), digits=1)
y < x[x > 5 & x < 30]
TipPct < sample(y, 44)
TipExperiment$Check < (TipExperiment$Tip / TipPct) * 100
# we’ve saved the value 18.87 for you as sample_F
sample_F < fVal(Tip ~ Check, data = TipExperiment)
# we have also generated 1000 Fs from the empty model of the DGP
sdoF < do(1000) * fVal(shuffle(Tip) ~ Check, data = TipExperiment)
# how many fVals are larger than sample_F?
tally()
# we’ve saved the value 18.87 for you as sample_F
sample_F < fVal(Tip ~ Check, data = TipExperiment)
# we have also generated 1000 Fs from the empty model of the DGP
sdoF < do(1000) * fVal(shuffle(Tip) ~ Check, data = TipExperiment)
# how many fVals are larger than sample_F?
tally(~ fVal > sample_F, data = sdoF)
ex() %>% check_or(
check_function(., "tally") %>% {
check_arg(., "x") %>% check_equal()
check_arg(., "data") %>% check_equal()
},
override_solution_code(., '{tally(~(fVal > sample_F), data= sdoF)}') %>%
check_function(., "tally") %>% {
check_arg(., "x") %>% check_equal()
check_arg(., "data") %>% check_equal()
}
)
fVal > sample_F
TRUE FALSE
0 1000
Although it is not impossible, not one of the 1000 randomized Fs (in our shuffled distribution; yours might be a little different) were as large as our sample F of 18.87. Based on this, we would estimate the pvalue to be very small. And indeed, it is when we check our ANOVA table for the regression model (reproduced below), which reports a very small pvalue of .0001 based on the mathematical F distribution.
supernova(lm(Tip ~ Check, data = TipExperiment))
Analysis of Variance Table (Type III SS)
Model: Tip ~ Check
SS df MS F PRE p
        
Model (error reduced)  1708.140 1 1708.140 18.865 0.3100 .0001
Error (from model)  3802.837 42 90.544
        
Total (empty model)  5510.977 43 128.162
Notice that the pvalue is very small, .0001. If you think about what this number means, it means that 1 out of 10000 Fs (1/10000 = .0001) would be greater than our sample F. If we simulated 10000 Fs, one of them might be greater than our sample F. But it would be very rare. It is unlikely to get an F that large if the empty model is true. Based on this we would reject the empty model (null hypothesis).
Does Check
Cause an Increase in Tip
?
There appears to be a strong relationship between the size of the check and the amount that a table tips. And the fact that there is a very low pvalue of .0001 indicates that a relationship this strong would be highly unlikely to have appeared in the data if the empty model were true. But should the low pvalue give us confidence that size of the check actually caused the increase in Tip
?
Causality cannot be inferred purely based on the results of statistical analysis; we must also take into account the research design.
When we were investigating the effect of smiley face on Tip
we had the advantage of random assignment: each table at the restaurant was randomly assigned to Condition
, to either get a smiley face or not. Because of random assignment, any variation in tips across conditions could be assumed to be either the result of smiley face (the one thing the researchers varied across conditions) or the result of pure randomness. A low pvalue in this case would have supported the inference that smiley face caused higher tips because it gave us a basis for ruling out pure randomness as an explanation.
The analysis of the relationship between Check
and Tip
is a different story, however, because tables were not randomly assigned to check amounts. Because of this correlational design, we must consider the possibility that other factors, not measured by the researchers, might have actually caused the apparent relationship between Check
and Tip
. For example, if the tables with large checks were all celebrating something and those with smaller checks were not, we would have to consider that it might be the celebratory mood of the table, and not the check amount, that was causing the variation in tips.
Even in a correlational design, a low pvalue can still help us rule out pure randomness as the only cause of variation in tip. But it cannot tell us that the variable Check
is therefore a cause of variation in Tip
. The variation could be due to check size, but also could be due to other confounding variables that we haven’t measured or even considered. In a correlational research design, pvalue does not necessarily indicate a causal relationship.