Lab 7 Introduction to linear regression

Batter up

The movie Moneyball focuses on the “quest for the secret of success in baseball”. It follows a low-budget team, the Oakland Athletics, who believed that underused statistics, such as a player’s ability to get on base, betterpredict the ability to score runs than typical statistics like home runs, RBIs (runs batted in), and batting average. Obtaining players who excelled in these underused statistics turned out to be much more affordable for the team.

In this lab we’ll be looking at data from all 30 Major League Baseball teams and examining the linear relationship between runs scored in a season and a number of other player statistics. Our aim will be to summarize these relationships both graphically and numerically in order to find which variable, if any, helps us best predict a team’s runs scored in a season.

The data

Let’s load up the data for the 2011 season.

load("more/mlb11.RData")
summary(mlb11)

##                    team         runs          at_bats          hits     
##  Arizona Diamondbacks: 1   Min.   :556.0   Min.   :5417   Min.   :1263  
##  Atlanta Braves      : 1   1st Qu.:629.0   1st Qu.:5448   1st Qu.:1348  
##  Baltimore Orioles   : 1   Median :705.5   Median :5516   Median :1394  
##  Boston Red Sox      : 1   Mean   :693.6   Mean   :5524   Mean   :1409  
##  Chicago Cubs        : 1   3rd Qu.:734.0   3rd Qu.:5575   3rd Qu.:1441  
##  Chicago White Sox   : 1   Max.   :875.0   Max.   :5710   Max.   :1600  
##  (Other)             :24                                                
##     homeruns        bat_avg         strikeouts    stolen_bases   
##  Min.   : 91.0   Min.   :0.2330   Min.   : 930   Min.   : 49.00  
##  1st Qu.:118.0   1st Qu.:0.2447   1st Qu.:1085   1st Qu.: 89.75  
##  Median :154.0   Median :0.2530   Median :1140   Median :107.00  
##  Mean   :151.7   Mean   :0.2549   Mean   :1150   Mean   :109.30  
##  3rd Qu.:172.8   3rd Qu.:0.2602   3rd Qu.:1248   3rd Qu.:130.75  
##  Max.   :222.0   Max.   :0.2830   Max.   :1323   Max.   :170.00  
##                                                                  
##       wins          new_onbase        new_slug         new_obs      
##  Min.   : 56.00   Min.   :0.2920   Min.   :0.3480   Min.   :0.6400  
##  1st Qu.: 72.00   1st Qu.:0.3110   1st Qu.:0.3770   1st Qu.:0.6920  
##  Median : 80.00   Median :0.3185   Median :0.3985   Median :0.7160  
##  Mean   : 80.97   Mean   :0.3205   Mean   :0.3988   Mean   :0.7191  
##  3rd Qu.: 90.00   3rd Qu.:0.3282   3rd Qu.:0.4130   3rd Qu.:0.7382  
##  Max.   :102.00   Max.   :0.3490   Max.   :0.4610   Max.   :0.8100  
##

In addition to runs scored, there are seven traditionally used variables in the data set: at-bats, hits, home runs, batting average, strikeouts, stolen bases, and wins. There are also three newer variables: on-base percentage, slugging percentage, and on-base plus slugging. For the first portion of the analysis we’ll consider the seven traditional variables. At the end of the lab, you’ll work with the newer variables on your own.

What type of plot would you use to display the relationship between runs and one of the other numerical variables? Plot this relationship using the variable at_bats as the predictor. Does the relationship look linear? If you knew a team’s at_bats, would you be comfortable using a linear model to predict the number of runs?

I would use the scatterplot to display the relationship between runs and one of the other numerical variables.The relationships looks linear, however i wouldn’t comfortable use a linear model due to the data spread.

plot(mlb11$runs ~ mlb11$at_bats, main = " Runs vs atBats", xlab = "At Bats", ylab = "Runs")

If the relationship looks linear, we can quantify the strength of the relationship with the correlation coefficient.

cor(mlb11$runs, mlb11$at_bats)

## [1] 0.610627

Sum of squared residuals

Think back to the way that we described the distribution of a single variable. Recall that we discussed characteristics such as center, spread, and shape. It’s also useful to be able to describe the relationship of two numerical variables, such as runs and at_bats above.

Looking at your plot from the previous exercise, describe the relationship between these two variables. Make sure to discuss the form, direction, and strength of the relationship as well as any unusual observations.

The realtionship is linear and positive: once At Bats is increasing, Runs is increasing as well.The relationship doesn’t look strong, thare are also some outliers.

Just as we used the mean and standard deviation to summarize a single variable, we can summarize the relationship between these two variables by finding the line that best follows their association. Use the following interactive function to select the line that you think does the best job of going through the cloud of points.

plot_ss(x = mlb11$at_bats, y = mlb11$runs)

## Click two points to make a line.
                                
## Call:
## lm(formula = y ~ x, data = pts)
## 
## Coefficients:
## (Intercept)            x  
##  -2789.2429       0.6305  
## 
## Sum of Squares:  123721.9

After running this command, you’ll be prompted to click two points on the plot to define a line. Once you’ve done that, the line you specified will be shown in black and the residuals in blue. Note that there are 30 residuals, one for each of the 30 observations. Recall that the residuals are the difference between the observed values and the values predicted by the line:

\[ e_i = y_i - \hat{y}_i \]

The most common way to do linear regression is to select the line that minimizes the sum of squared residuals. To visualize the squared residuals, you can rerun the plot command and add the argument showSquares = TRUE.

plot_ss(x = mlb11$at_bats, y = mlb11$runs, showSquares = TRUE)

## Click two points to make a line.
                                
## Call:
## lm(formula = y ~ x, data = pts)
## 
## Coefficients:
## (Intercept)            x  
##  -2789.2429       0.6305  
## 
## Sum of Squares:  123721.9

Note that the output from the plot_ss function provides you with the slope and intercept of your line as well as the sum of squares.

Using plot_ss, choose a line that does a good job of minimizing the sum of squares. Run the function several times. What was the smallest sum of squares that you got? How does it compare to your neighbors?

The smallest sum of square is 139523.2

The linear model

It is rather cumbersome to try to get the correct least squares line, i.e. the line that minimizes the sum of squared residuals, through trial and error. Instead we can use the lm function in R to fit the linear model (a.k.a. regression line).

m1 <- lm(runs ~ at_bats, data = mlb11)

The first argument in the function lm is a formula that takes the form y ~ x. Here it can be read that we want to make a linear model of runs as a function of at_bats. The second argument specifies that R should look in the mlb11 data frame to find the runs and at_bats variables.

The output of lm is an object that contains all of the information we need about the linear model that was just fit. We can access this information using the summary function.

summary(m1)

## 
## Call:
## lm(formula = runs ~ at_bats, data = mlb11)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -125.58  -47.05  -16.59   54.40  176.87 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2789.2429   853.6957  -3.267 0.002871 ** 
## at_bats         0.6305     0.1545   4.080 0.000339 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66.47 on 28 degrees of freedom
## Multiple R-squared:  0.3729, Adjusted R-squared:  0.3505 
## F-statistic: 16.65 on 1 and 28 DF,  p-value: 0.0003388

Let’s consider this output piece by piece. First, the formula used to describe the model is shown at the top. After the formula you find the five-number summary of the residuals. The “Coefficients” table shown next is key; its first column displays the linear model’s y-intercept and the coefficient of at_bats. With this table, we can write down the least squares regression line for the linear model:

\[ \hat{y} = -2789.2429 + 0.6305 * atbats \]

One last piece of information we will discuss from the summary output is the Multiple R-squared, or more simply, \(R^2\). The \(R^2\) value represents the proportion of variability in the response variable that is explained by the explanatory variable. For this model, 37.3% of the variability in runs is explained by at-bats.

Fit a new model that uses homeruns to predict runs. Using the estimates from the R output, write the equation of the regression line. What does the slope tell us in the context of the relationship between success of a team and its home runs?

plot(mlb11$runs ~ mlb11$homeruns, main = " Runs vs Home runs", xlab = "Home Runs", ylab = "Runs")

cor(mlb11$runs, mlb11$homeruns)

## [1] 0.7915577

The relationship between runs and home runs is linear positive and relatively strong as the correlation coefficient 0.7916, which is closer to +1

m2<- lm(runs ~ homeruns, data = mlb11)
summary(m2)

## 
## Call:
## lm(formula = runs ~ homeruns, data = mlb11)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -91.615 -33.410   3.231  24.292 104.631 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 415.2389    41.6779   9.963 1.04e-10 ***
## homeruns      1.8345     0.2677   6.854 1.90e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 51.29 on 28 degrees of freedom
## Multiple R-squared:  0.6266, Adjusted R-squared:  0.6132 
## F-statistic: 46.98 on 1 and 28 DF,  p-value: 1.9e-07

Equation of the regression line:

y= 415.2389 + 1.8345 * HomeRuns

Prediction and prediction errors

Let’s create a scatterplot with the least squares line laid on top.

plot(mlb11$runs ~ mlb11$at_bats)
abline(m1)

The function abline plots a line based on its slope and intercept. Here, we used a shortcut by providing the model m1, which contains both parameter estimates. This line can be used to predict \(y\) at any value of \(x\). When predictions are made for values of \(x\) that are beyond the range of the observed data, it is referred to as extrapolation and is not usually recommended. However, predictions made within the range of the data are more reliable. They’re also used to compute the residuals.

If a team manager saw the least squares regression line and not the actual data, how many runs would he or she predict for a team with 5,578 at-bats? Is this an overestimate or an underestimate, and by how much? In other words, what is the residual for this prediction?

Least Square Regression line for runs vs at_bats

y^ = -2789.2429 + 0.6305 * atbats

##If atbats is 5,578
atbats= 5578
y<- -2789.2429 + 0.6305*atbats
y

## [1] 727.6861

The team manager would predict 728 runs for a team with 5,578 at-bats. We can conclude it is an overestimate residual, because Phillies have an actual data point, which is 5579 at-bats and only 713 runs.

Model diagnostics

To assess whether the linear model is reliable, we need to check for (1) linearity, (2) nearly normal residuals, and (3) constant variability.

Linearity: You already checked if the relationship between runs and at-bats is linear using a scatterplot. We should also verify this condition with a plot of the residuals vs. at-bats. Recall that any code following a # is intended to be a comment that helps understand the code but is ignored by R.

plot(m1$residuals ~ mlb11$at_bats)
abline(h = 0, lty = 3)  # adds a horizontal dashed line at y = 0

Is there any apparent pattern in the residuals plot? What does this indicate about the linearity of the relationship between runs and at-bats?

There is no apparent pattern, which indicates a linear relationship between runs and at-bats.

Nearly normal residuals: To check this condition, we can look at a histogram

hist(m1$residuals)

or a normal probability plot of the residuals.

qqnorm(m1$residuals)
qqline(m1$residuals)  # adds diagonal line to the normal prob plot

Based on the histogram and the normal probability plot, does the nearly normal residuals condition appear to be met?

Yes, the nearly normal condition appear to be met.

Constant variability:

Based on the plot in (1), does the constant variability condition appear to be met?

Yes, the constant variability condition appear to be met, however there are a few outliers.

On Your Own

Choose another traditional variable from mlb11 that you think might be a good predictor of runs. Produce a scatterplot of the two variables and fit a linear model. At a glance, does there seem to be a linear relationship?

Let’s assume that the ‘Wins’ will be a good predictor of ‘runs’.

plot(mlb11$runs ~ mlb11$wins, main = " Runs vs Wins", xlab = "Win", ylab = "Runs")

m3 <- lm(runs ~ wins, data = mlb11)
summary(m3)

## 
## Call:
## lm(formula = runs ~ wins, data = mlb11)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -145.450  -47.506   -7.482   47.346  142.186 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  342.121     89.223   3.834 0.000654 ***
## wins           4.341      1.092   3.977 0.000447 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 67.1 on 28 degrees of freedom
## Multiple R-squared:  0.361,  Adjusted R-squared:  0.3381 
## F-statistic: 15.82 on 1 and 28 DF,  p-value: 0.0004469

abline(m3)

plot(mlb11$wins, resid(m3))
abline(h = 0, lty = 3)

Correlation Coefficient

cor(mlb11$runs, mlb11$wins)

## [1] 0.6008088

Equation of the regression line for the relationship between Run and Wins

y^ = 342.121 + 4.341 * wins

By looking at the plot we can say that the relationship between runs and Wins is linear positive and relatively strong as the correlation coefficient 0.6008088 is closer to +1.

How does this relationship compare to the relationship between runs and at_bats? Use the R\(^2\) values from the two model summaries to compare. Does your variable seem to predict runs better than at_bats? How can you tell?

plot_ss(x = mlb11$wins, y = mlb11$runs, showSquares = TRUE)

## Click two points to make a line.
                                
## Call:
## lm(formula = y ~ x, data = pts)
## 
## Coefficients:
## (Intercept)            x  
##     342.121        4.341  
## 
## Sum of Squares:  126068.4

The R2 value for model using at_bats is 0.3729 while for model using ‘Wins’ is 0.361. Based on this we can conclude that variable ‘at_bats’ is better predictor of runs.

Now that you can summarize the linear relationship between two variables, investigate the relationships between runs and each of the other five traditional variables. Which variable best predicts runs? Support your conclusion using the graphical and numerical methods we’ve discussed (for the sake of conciseness, only include output for the best variable, not all five).

I investigated the relationship between ‘runs’ with: bat_avg, hits, strikeouts, stolen_bases and wins. It appears that the best variable that predics ‘run’ based on R2 value is ‘bat_avg’ .

plot(mlb11$runs ~ mlb11$bat_avg, main = "Relationship between Runs and Batting Avg", xlab = "Batting Avg", ylab = "Runs")
m4 <- lm(runs ~ bat_avg, data = mlb11)
abline(m4)

summary (m4)

## 
## Call:
## lm(formula = runs ~ bat_avg, data = mlb11)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -94.676 -26.303  -5.496  28.482 131.113 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -642.8      183.1  -3.511  0.00153 ** 
## bat_avg       5242.2      717.3   7.308 5.88e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49.23 on 28 degrees of freedom
## Multiple R-squared:  0.6561, Adjusted R-squared:  0.6438 
## F-statistic: 53.41 on 1 and 28 DF,  p-value: 5.877e-08

cor(mlb11$runs, mlb11$bat_avg)

## [1] 0.8099859

hist(m4$residuals)

qqnorm(m4$residuals)
qqline(m4$residuals)

Now examine the three newer variables. These are the statistics used by the author of Moneyball to predict a teams success. In general, are they more or less effective at predicting runs that the old variables? Explain using appropriate graphical and numerical evidence. Of all ten variables we’ve analyzed, which seems to be the best predictor of runs? Using the limited (or not so limited) information you know about these baseball statistics, does your result make sense?

The three newer variables: new_onbase, new_slug and new_obs

cor(mlb11$runs, mlb11$new_onbase)

## [1] 0.9214691

cor(mlb11$runs, mlb11$new_slug)

## [1] 0.9470324

cor(mlb11$runs, mlb11$new_obs)

## [1] 0.9669163

Based on the correlation coefficient, these three variables are more effective than the old variables.

The best variable that predicts ‘runs’ is On-base plus slugging variable. And yes, it does make sense, due to the variable new_obs represents a sum of new_onbase and new_slug.

plot(mlb11$runs ~ mlb11$new_obs, main = "Relationship between Runs and On-base plus slugging Avg", xlab = "New_obs", ylab = "Runs")
m5 <- lm(runs ~ new_obs, data = mlb11)
abline(m5)

Check the model diagnostics for the regression model with the variable you decided was the best predictor for runs.

plot_ss(x = mlb11$new_obs, y = mlb11$runs, showSquares = TRUE)

## Click two points to make a line.
                                
## Call:
## lm(formula = y ~ x, data = pts)
## 
## Coefficients:
## (Intercept)            x  
##      -686.6       1919.4  
## 
## Sum of Squares:  12837.66

plot(mlb11$new_obs, resid(m5))
abline(h = 0, lty = 3)

hist(m5$residuals)

qqnorm(m5$residuals)
qqline(m5$residuals)

This is a product of OpenIntro that is released under a Creative Commons Attribution-ShareAlike 3.0 Unported. This lab was adapted for OpenIntro by Andrew Bray and Mine Çetinkaya-Rundel from a lab written by the faculty and TAs of UCLA Statistics.