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")

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?

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

I would use sattter plot to display the relationship between 'runs' and one of other numerical variables.

yx.lm <- lm(mlb11$runs ~ mlb11$at_bats)
yx.lm

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$at_bats)
## 
## Coefficients:
##   (Intercept)  mlb11$at_bats  
##    -2789.2429         0.6305

plot( mlb11$at_bats, mlb11$runs, pch = 19, cex = 1.1, col = "blue", main="Oakland Athletics")+
abline(yx.lm, col = "red")

## numeric(0)

yx.res <- resid(yx.lm)

par(mfrow = c(1,2))
hist(yx.res, xlab="Residuals", breaks = 10)

plot(mlb11$at_bats, yx.res, ylab="Residuals", xlab="at_bats", main="Oakland Athletics") 

abline(0, 0)

The relationship look linear. To eveluate whether linear model could be applied to 'run's over 'at_bats' pair data, I will check the following conditions:

Linearity:I would say the data is linear as shown in the plot.
Nearly normal residuals:The distribution of residuals is bimodal.It splits into two groups at 'at-bats' = 0. 
Constant variabilities:The variance around the line is constant.
Independent observation:Each player's performance is indenpendent to each other.

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.

According to the satter plot and the linear regression line, there is strong positive correlation between 'runs' and 'at_bats'.

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?

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

I ran the function several times. The sum of squares is always 123721.9. The sum of squares will change when the coefficients of the line change.

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?

m2 <- lm(mlb11$runs ~ mlb11$homeruns)
summary(m2)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$homeruns)
## 
## 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 ***
## mlb11$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

the equation of the regression line:

\[ \hat{y} = 415.2389 + 1.8345 * homeruns \]

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)

## numeric(0)

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?

runs <- (-2789.2429) + 0.6305 * 5578
runs

## [1] 727.6861

# when at-bats = 5579, runs = 713

There is no way to tell what is the actual observation of runs when at_bats equals to 5578 from the data set. If the actural 'runs' is higher than 727.69, then the predicted runs is an underestimate. If the actural 'runs' is lower than 727.69, the predicted runs is an overestimate.

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

## numeric(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?

The vairance around the line is nearly constant. most of the variance is between 0 -100 and there are 3 has variance higher than 100.

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?

The residuals histogram is not normal but the qqplot for the residual show normal distribution. The nearly normal residuals condition appear to be met.

Constant variability:

Based on the plot in (1), does the constant variability condition appear to be met? Based on the plot in (1), the variance around the line is nearly constant and the variability condition appears to be met.

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?

plot(mlb11$new_onbase, mlb11$runs, col = "blue", main="Oakland Athletics") + 
  abline(lm(mlb11$runs ~ mlb11$new_onbase), col = "red")

## numeric(0)

There seems to be a linear relationship between 'runs' and 'bat_avg'.

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?

m3 <- lm(mlb11$runs ~ mlb11$new_onbase)
summary(m3)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$new_onbase)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -58.270 -18.335   3.249  19.520  69.002 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1118.4      144.5  -7.741 1.97e-08 ***
## mlb11$new_onbase   5654.3      450.5  12.552 5.12e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.61 on 28 degrees of freedom
## Multiple R-squared:  0.8491, Adjusted R-squared:  0.8437 
## F-statistic: 157.6 on 1 and 28 DF,  p-value: 5.116e-13

R-squared is 0.6561, which is larger than 0.70, suggesting there is a strong positive correlation between 'runs' and 'new_onbase'. This R-squared is larger than the one from 'at_bats' linear model so this variant seems to predict `runs` better than `at_bats`. You can tell from the satter plot in which the points fit the linear regrassion line better than 'at_bats' linear model.

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).

at_bats<- lm(mlb11$runs ~ mlb11$at_bats)
summary(at_bats)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$at_bats)
## 
## 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 ** 
## mlb11$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

hits <- lm(mlb11$runs ~ mlb11$hits)
summary(hits)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$hits)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -103.718  -27.179   -5.233   19.322  140.693 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -375.5600   151.1806  -2.484   0.0192 *  
## mlb11$hits     0.7589     0.1071   7.085 1.04e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 50.23 on 28 degrees of freedom
## Multiple R-squared:  0.6419, Adjusted R-squared:  0.6292 
## F-statistic:  50.2 on 1 and 28 DF,  p-value: 1.043e-07

homeruns <- lm(mlb11$runs ~ mlb11$homeruns)
summary(homeruns)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$homeruns)
## 
## 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 ***
## mlb11$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

bat_avg <- lm(mlb11$runs ~ mlb11$bat_avg)
summary(bat_avg)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$bat_avg)
## 
## 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 ** 
## mlb11$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

wins <- lm(mlb11$runs ~ mlb11$wins)
summary(wins)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$wins)
## 
## 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 ***
## mlb11$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

plot(mlb11$bat_avg, mlb11$runs, col = "blue")+
abline(lm(mlb11$runs ~ mlb11$bat_avg), col = "red")

## numeric(0)

The linear regression modal shows that R-squared of 'bat_avg' and `runs`, 0.6561, is the best R-squared among the five old variants tested. As shown in the scatter plot for `runs` vs. 'bat_avg', the points moderately fit the least squares line. So among the traditional variants 'bat_avg' is  that best predicts `runs`.

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?

new_onbase<- lm(mlb11$runs ~ mlb11$new_onbase)
summary(new_onbase)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$new_onbase)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -58.270 -18.335   3.249  19.520  69.002 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -1118.4      144.5  -7.741 1.97e-08 ***
## mlb11$new_onbase   5654.3      450.5  12.552 5.12e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32.61 on 28 degrees of freedom
## Multiple R-squared:  0.8491, Adjusted R-squared:  0.8437 
## F-statistic: 157.6 on 1 and 28 DF,  p-value: 5.116e-13

new_slug <- lm(mlb11$runs ~ mlb11$new_slug)
summary(new_slug)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$new_slug)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -45.41 -18.66  -0.91  16.29  52.29 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     -375.80      68.71   -5.47 7.70e-06 ***
## mlb11$new_slug  2681.33     171.83   15.61 2.42e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26.96 on 28 degrees of freedom
## Multiple R-squared:  0.8969, Adjusted R-squared:  0.8932 
## F-statistic: 243.5 on 1 and 28 DF,  p-value: 2.42e-15

new_obs <- lm(mlb11$runs ~ mlb11$new_obs)
summary(new_obs)

## 
## Call:
## lm(formula = mlb11$runs ~ mlb11$new_obs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -43.456 -13.690   1.165  13.935  41.156 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -686.61      68.93  -9.962 1.05e-10 ***
## mlb11$new_obs  1919.36      95.70  20.057  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.41 on 28 degrees of freedom
## Multiple R-squared:  0.9349, Adjusted R-squared:  0.9326 
## F-statistic: 402.3 on 1 and 28 DF,  p-value: < 2.2e-16

plot(mlb11$new_obs, mlb11$runs, col = "blue")+
abline(lm(mlb11$runs ~ mlb11$new_obs), col = "red")

## numeric(0)

The three newer variables, new_onbase, new_slug, and new_obs used by the author of *Moneyball* to predict a teams success have R-squared as 0.8491, 0.8969 and 0.9349 respectively. In general they are more effective at predicting runs than that the old variable. Of all ten variables we've analyzed, new_obs seems to be the best predictor of `runs` since it has the highest R-squared. As shown in the scatter plot and linear regression model, the points fit the lease squares line much tighter than the points do in the other variants' plots. The graph show a strong positive linear relationship.

These results make sense. As you can find out the obs or On-base plus slugging (OPS)  is an innovated sabermetric baseball statistic for sorting baseball players now a days which ouperformed the tranditional evaluation system. I cited the background knowledge from the following website:   

http://www.fangraphs.com/library/offense/ops/

"In general, OPS is better than something like batting average or RBI because it captures a player's ability to get on base and their ability to hit for extra bases. For the most part, those two factors capture more of what hitters are trying to do. Generally speaking, if you sort hitters by OPS, you are sorting them based on their production to date with some minor exceptions."

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

# check the variance of residuals
par(mfrow = c(1,2))
plot(new_obs$residuals ~ mlb11$new_obs)+
abline(h = 0, lty = 3)

## numeric(0)

# check the distribution of residuals
hist(new_obs$residuals)

qqnorm(new_obs$residuals)
qqline(new_obs$residuals)

To eveluate whether linear model could be applied to 'run's over 'new_obs' pair data, I will check the following conditions:

Linearity:From the scatter plot, we can see the relationship between 'runs' and 'new_obs' look linear. 

Nearly normal residuals:From the histogram, The distribution of residuals is nearly normal. 

Constant variabilities:From the residuals scatter plot, The variance around the line is constant.

Independent observation:Each player's performance is indenpendent to each other.

So we can fit a least squares line to these pair of vairants.

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.