Crime data set

The data are measurements from the 67 counties of Florida.
Our focus is on
- The response \(y\): Crime which is the crime rate
- The predictor \(x_1\): Education which is proportion of the population with high school exam
- The predictor \(x_2\): Urbanisation which is proportion of the population living in urban areas

FL <- read.delim("https://asta.math.aau.dk/datasets?file=fl-crime.txt")
head(FL, n = 3)

##   Crime Education Urbanisation
## 1   104      82.7         73.2
## 2    20      64.1         21.5
## 3    64      74.7         85.0

Multiple regression model for crime data

Both Education (\(x_1\)) and Urbanisation (\(x_2\)) were correlated with Crime (\(y\)).
We consider the model \[Y=\alpha + \beta_1x_1 + \beta_2x_2 + \epsilon\]
The errors \(\epsilon\) are random terms with a \(\texttt{norm}(0,\sigma)\) distribution.
The graph for the mean response is a 2-dimensional plane in a 3-dimensional space.

model <- lm(Crime ~ Education + Urbanisation, data = FL)
summary(model)

## 
## Call:
## lm(formula = Crime ~ Education + Urbanisation, data = FL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.693 -15.742  -6.226  15.812  50.678 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   59.1181    28.3653   2.084   0.0411 *  
## Education     -0.5834     0.4725  -1.235   0.2214    
## Urbanisation   0.6825     0.1232   5.539 6.11e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.82 on 64 degrees of freedom
## Multiple R-squared:  0.4714, Adjusted R-squared:  0.4549 
## F-statistic: 28.54 on 2 and 64 DF,  p-value: 1.379e-09

From the output we find the prediction equation \[ \widehat y = 59 - 0.58x_1 + 0.68x_2. \]

Regression model

In a multiple regression we have
- a response variable \(Y\).
- \(k\) predictor variables \(x_1,x_2,\ldots,x_k\).
Multiple regression model: \[Y=\alpha + \beta_1x_1 + \beta_2x_2 + \dotsm + \beta_kx_k + \epsilon.\]
The systematic part of the model says that the mean response is a linear function of the predictors: \[ E(Y|x_1,x_2,\ldots,x_k)=\alpha + \beta_1x_1 + \beta_2x_2 + \dotsm + \beta_kx_k. \]
- Here \(E(Y|x_1,x_2,\ldots,x_k)\) is used to denote the mean value of the response when we know the value of the predictors \(x_1,\ldots,x_k\).
The error \(\epsilon\) is a random variable having a distribution with
- a normal distribution
- mean 0
- standard deviation \(\sigma\)

Interpretation of parameters

In the multiple linear regression model \[E(y|x_1,x_2,\ldots,x_k) = \alpha + \beta_1x_1 + \beta_2x_2 + \dotsm + \beta_kx_k\]
- The parameter \(\alpha\) is the Intercept, corresponding to the mean response, when all predictors are equal to zero.
- The parameters \((\beta_1,\beta_2,\dotsm,\beta_k)\) are called partial regression coefficients.
Imagine that all predictors but \(x_1\) are held fixed. Then \[E(y|x_1,x_2,\ldots,x_k) = \tilde{\alpha} + \beta_1x_1, \] where \[ \tilde\alpha = \alpha + \beta_2 x_2 + \dotsm + \beta_k x_k. \]
So the mean response depends linearly on \(x_1\) when all other variables are kept fixed.
- The line has slope \(\beta_1\), which describes the change in the mean response, when \(x_1\) is changed one unit.
- The rate of change \(\beta_1\) does not depend on the value of the remaining predictors. In this case we say that there is no interaction between the effects of the predictors on the response.
The above holds similarly for the other partial regression coefficients.

Estimation of model parameters

Suppose we have a sample of size \(n\).
Based on the sample, we estimate \((\alpha,\beta_1,\beta_2,\ldots,\beta_k)\) by the values \((a,b_1,b_2,\ldots,b_k)\).
Based on this estimate we obtain the prediction equation (or regression equation) as \[ \widehat y=a + b_1x_1 + b_2x_2 + \dotsm + b_kx_k \]
The difference between our observations and the predictions made by the prediction equation are called the residuals \(e_i=y_i-\widehat{y_i}\).
The estimates \((a,b_1,b_2,\ldots,b_k)\) are chosen by the least squares method, which seeks to minimize the sum of squared residuals \[\sum_{i=1}^n(y_i-\widehat{y}_i)^2.\]

Estimation of error variance

Recall that \(\sigma^2\) is the variance of the error terms in the model. Using the residuals as approximations of the errors in our sample, we estimate \(\sigma^2\) by \[s^2 = \frac{\sum_{i=1}^n(y_i-\widehat{y}_i)^2}{n-k-1}.\]
Rather than \(n\) we divide by the degrees of freedom \(df=n-k-1\). Theory shows, that this is reasonable.
- The degrees of freedom \(df\) are determined by the sample size \(n\) minus the number of parameters in the regression equation.
- Currently we have \(k+1\) parameters: \(1\) intercept and \(k\) slopes.

Multiple \(R^2\)

We can compare two models to predict the response \(y\).
Model 1: We do not use the predictors, and use \(\bar y\) to predict any \(y\)-measurement. The corresponding sum of squared prediction errors is
- \(TSS = \sum_{i=1}^n(y_i - \bar y)^2\) and is called the Total Sum of Squares.
Model 2: We use the multiple prediction equation with predictors \(x_1,\ldots,x_k\) to predict \(y\). The sum of squared prediction errors is now
- \(SSE=\sum_{i=1}^n(y_i - \widehat y_i)^2\) and is called Sum of Squared Errors.
We then define the multiple coefficient of determination \[R^2 = \frac{TSS - SSE}{TSS}.\]
Thus, \(R^2\) is the relative reduction in squared prediction errors, when we use \(x_1,x_2,\ldots,x_k\) as explanatory variables.
- We say that \(R^2\) is the proportion of the total variation in the data that can be explained by \(x_1,\ldots,x_k\).
Properties:
- \(0\leq R^2 \leq 1\)
- \(R^2=0\) means \(TSS=SSE\), so the model does not improve when we include \(x_1,\ldots,x_k\) in the model.
- \(R^2=1\) means \(SSE=0\), so the observations are predicted perfectly by \(x_1,\ldots,x_k\).

Example

summary(model)

## 
## Call:
## lm(formula = Crime ~ Education + Urbanisation, data = FL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.693 -15.742  -6.226  15.812  50.678 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   59.1181    28.3653   2.084   0.0411 *  
## Education     -0.5834     0.4725  -1.235   0.2214    
## Urbanisation   0.6825     0.1232   5.539 6.11e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.82 on 64 degrees of freedom
## Multiple R-squared:  0.4714, Adjusted R-squared:  0.4549 
## F-statistic: 28.54 on 2 and 64 DF,  p-value: 1.379e-09

The prediction equation is \(\widehat y=59 - 0.58x_1 + 0.68x_2\)
The estimate for \(\sigma\) is \(s = 20.82\) (Residual standard error in R) with \(df = 67-3 = 64\) degrees of freedom.
Multiple \(R^2 = 0.4714\), i.e. 47% of the variation in the response is explained by including the predictors in the model.
The output provides a test of the hypothesis \(H_0: \beta_1 = 0\) corresponding to no effect of the predictor \(x_1\).
- The estimate \(b_1 = -0.5834\) has standard error (Std. Error) \(se = 0.4725\) with corresponding observed \(t\)-value (t value) \(t_\text{obs} = \frac{-0.5834}{0.4725} = -1.235\).
- This means that \(b_1\) isn’t significantly different from zero, since the \(p\)-value (Pr(>|t|)) is 22%. That means that we can exclude Education as a predictor.

Example

Our final model is then a simple linear regression:

model2 <- lm(Crime ~ Urbanisation, data = FL)
summary(model2)

## 
## Call:
## lm(formula = Crime ~ Urbanisation, data = FL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.766 -16.541  -4.741  16.521  49.632 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  24.54125    4.53930   5.406 9.85e-07 ***
## Urbanisation  0.56220    0.07573   7.424 3.08e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.9 on 65 degrees of freedom
## Multiple R-squared:  0.4588, Adjusted R-squared:  0.4505 
## F-statistic: 55.11 on 1 and 65 DF,  p-value: 3.084e-10

The coefficient of determination always decreases, when the model is simpler. Now we have \(R^2 = 46\%\), where before we had \(47\%\). But the decrease is not significant as we will see next.

F-test for comparing two models

We can compare two models, where one is obtained from the other by setting \(m\) parameters to zero, by an \(F\)-test.
We can compare \(R^2\) for the two models via the following \(F\)-statistic: \[ F_{obs} = \frac{(R_2^2-R_1^2)/\text{df}_1}{(1-R_2^2)/\text{df}_2} \]
- \(R_2^2\) corresponds to the larger model and \(R_1^2\) corresponds to the smaller model.
- Large values of \(F_{obs}\) means that \(R_2^2\) is large compared to \(R_1^2\), pointing towards the alternative hypothesis.
- \(\text{df}_1=m\) is the number of parameters set to zero in the null-hypothesis.
- \(\text{df}_2=n-k-1\) where \(n\) is sample size and \(k+1\) is the number of unknown parameters in the larger model.
In R the calculations are done using anova:

anova(model2, model)

## Analysis of Variance Table
## 
## Model 1: Crime ~ Urbanisation
## Model 2: Crime ~ Education + Urbanisation
##   Res.Df   RSS Df Sum of Sq      F Pr(>F)
## 1     65 28391                           
## 2     64 27730  1    660.61 1.5247 0.2214

Overall F-test for effect of predictors

F-test

We consider the hypothesis \[H_0: \beta_1=\beta_2=\ldots=\beta_k=0\] against the alternative that at least one \(\beta_i\) are non-zero.
- This is the hypothesis that there is no effect of any of the predictors.
As test statistic we use \[F_{obs}=\frac{R^2/k}{(1-R^2)/(n-k-1)}\]
Large values of \(R^2\) implies large values of \(F_{obs}\), which points to the alternative hypothesis.
Thus, the p-value is the probability of observing something larger than the computed \(F_{obs}\).
The distribution of \(F_{obs}\) under the null-hypothesis is an F-distribution with degrees of freedom
- \(df_1=k\) (the number of parameters set to zero in the null-hypothesis).
- \(df_2=n-k-1\) (number of observations minus number of unknown parameters in the model).

Example

We return to Crime and the prediction equation \(\widehat y = 59 - 0.58x_1 + 0.68x_2\), where \(n=67\) and \(R^2=0.4714\).
We test the hypothesis \(H_0:\beta_1 = \beta_2 =0\). We have
- \(df_1=k=2\) since 2 parameters are set to zero under \(H_0\).
- \(df_2=n-k-1=67-2-1=64\).
- Then we can calculate \(F_{obs} = \frac{R^2/k}{(1-R^2)/(n-k-1)} = 28.54\)
To evaluate the value \(28.54\) in the relevant F-distribution:

1 - pdist("f", 28.54, df1=2, df2=64)

## [1] 1.378612e-09

So \(p\)-value=\(1.38\times 10^{-9}\) (notice we don’t multiply by 2 since this is a one-sided test; only large values point more towards the alternative than the null hypothesis).
All this can be found in the summary output we already have:

summary(model)

## 
## Call:
## lm(formula = Crime ~ Education + Urbanisation, data = FL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -34.693 -15.742  -6.226  15.812  50.678 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   59.1181    28.3653   2.084   0.0411 *  
## Education     -0.5834     0.4725  -1.235   0.2214    
## Urbanisation   0.6825     0.1232   5.539 6.11e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.82 on 64 degrees of freedom
## Multiple R-squared:  0.4714, Adjusted R-squared:  0.4549 
## F-statistic: 28.54 on 2 and 64 DF,  p-value: 1.379e-09

Interaction between effects of predictors

Could it be possible that a combination of Education and Urbanisation is good for prediction? We investigate this using the interaction model \[E(y|x_1,x_2) = \alpha + \beta_1x_1 + \beta_2x_2 + \beta_3x_1x_2,\] where we have extended with a possible effect of the product \(x_1x_2\).
If we fix \(x_2\) in this model, the mean response is linear in \(x_1\) with intercept \(\alpha + \beta_2x_2\) and slope \(\beta_1 + \beta_3 x_2\), since \[E(y|x_1,x_2) = (\alpha+ \beta_2x_2 ) + (\beta_1+ \beta_3x_2)x_1.\]
- The slope for \(x_1\) now depends on the value of \(x_2\)!
- Interaction means that the effect of \(x_1\) on the response depends on the value of \(x_2\).
- Interaction does not mean that \(x_1\) and \(x_2\) affect each other.

Example - interaction model

We fit the model for the Crime data set:

model3 <- lm(Crime ~ Education * Urbanisation, data = FL)
summary(model3)

## 
## Call:
## lm(formula = Crime ~ Education * Urbanisation, data = FL)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.181 -15.207  -6.457  14.559  49.889 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            19.31754   49.95871   0.387    0.700  
## Education               0.03396    0.79381   0.043    0.966  
## Urbanisation            1.51431    0.86809   1.744    0.086 .
## Education:Urbanisation -0.01205    0.01245  -0.968    0.337  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.83 on 63 degrees of freedom
## Multiple R-squared:  0.4792, Adjusted R-squared:  0.4544 
## F-statistic: 19.32 on 3 and 63 DF,  p-value: 5.371e-09

When we look at the \(p\)-values in the table nothing is significant at the \(5\%\) level!
But the F-statistic tells us that the predictors collectively have a significant prediction ability.
Why has the highly significant effect of \(x_2\) disappeared? Because the predictors \(x_1\) and \(x_1x_2\) are able to explain the same as \(x_2\).
Previously we only had \(x_1\) as alternative explanation to \(x_2\) - and that wasn’t enough.
The phenomenon is called multicollinearity. It happens because the predictors are highly correlated.
It also illustrates that we can have different models with equally good predictive properties.
- In the case of an interaction model we always choose the model without interaction because it is simpler.
However, in general it can be difficult to choose between models. For example, if both height and weight are good predictors of some response, but one of them can be left out, which one do we choose?

Multiple linear regression with categorical predictors

Dummy variables

Suppose we want to predict the response variable using a categorical predictor variable \(x\) with \(k\) categories.
We choose one group, say group k, as the reference category.
For the remaining groups \(1,\ldots,k-1\), we define dummy variables \[z_i=\begin{cases}0,& \text{if } x\neq i,\\1,& \text{if } x = i.\end{cases}\] for \(i=1,\ldots, k-1.\)
- The dummy variable \(z_i\) is 1 if an observation is in group \(i\) and 0 otherwise.
- When all dummy variables \(z_i=0\), \(i=1,\ldots,k-1\), it means that the observation belongs to the reference group \(k\).
We can use the variables \(z_1,\ldots,z_{k-1}\) in a multiple regression along with other predictor variables.

Example

Consider the dataset mtcars. We are interested in how engine type vs (categorical) and weight of the car wt (quantitative, \(x_1\)) are associated with fuel consumption mpg.
The variable vs is already coded as a dummy variable \(z\) in R, taking the value 1 if the engine is v-shaped and 0 otherwise.
The multiple regression model becomes \[E(Y|x_1,z)= \alpha + \beta_1x_1 + \beta_2z.\]
- For \(z=0\): \[E(Y|x_1,z)= \alpha + \beta_1x_1.\]
- For \(z=1\): \[E(Y|x_1,z)= \alpha +\beta_2 + \beta_1x_1.\]
So we get two different regression lines for the two groups.
- The lines have a common slope \(\beta_1\) (parallel lines).
- The lines have different intercepts. The difference in intercepts is \(\beta_2\).

Example

We always start with some graphics (remember the function gf_point for plotting points and gf_lm for adding a regression line).

library(mosaic)
gf_point(mpg ~ wt, color = ~factor(vs), group=~factor(vs), data = mtcars) %>% gf_lm()

## Warning: Using the `size` aesthetic with geom_line was deprecated in ggplot2 3.4.0.
## ℹ Please use the `linewidth` aesthetic instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

An unclear picture, but a tendency to decreasing number of miles per gallon with increasing weight for both engine types.
The slope of the lines for the two engine types look different. But is the difference significant? Or can the difference be explained by sampling variation?

Example

We fit a multiple regression model without interaction in R:

model1 <- lm(mpg ~ wt + factor(vs) , data = mtcars)
summary(model1)

## 
## Call:
## lm(formula = mpg ~ wt + factor(vs), data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7071 -2.4415 -0.3129  1.4319  6.0156 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  33.0042     2.3554  14.012 1.92e-14 ***
## wt           -4.4428     0.6134  -7.243 5.63e-08 ***
## factor(vs)1   3.1544     1.1907   2.649   0.0129 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.78 on 29 degrees of freedom
## Multiple R-squared:  0.801,  Adjusted R-squared:  0.7873 
## F-statistic: 58.36 on 2 and 29 DF,  p-value: 6.818e-11

The common slope to wt is estimated to be \(\widehat\beta_1 = -4.44\), with corresponding p-value \(5.63\cdot 10^{-8}\), so the effect of wt is significantly different from zero.
- The estimate is negative, so increasing weight decreases the number of miles per gallon.
The estimate for intercept in the reference group (“not v-shaped”) is \(\widehat\alpha=33.0\), which is significantly different from zero if we test at level 5% (this test is not really of interest).
The difference between intercepts for the two engine types is \(\widehat\beta_1=3.15\), which is significant with p-value=1%.
- This suggests that the regression lines are not the same for the two engine types.
- The value 3.15 is the vertical distance between the two regression lines.

plotModel(model1)

Example: Prediction equations

summary(model1)

## 
## Call:
## lm(formula = mpg ~ wt + factor(vs), data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7071 -2.4415 -0.3129  1.4319  6.0156 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  33.0042     2.3554  14.012 1.92e-14 ***
## wt           -4.4428     0.6134  -7.243 5.63e-08 ***
## factor(vs)1   3.1544     1.1907   2.649   0.0129 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.78 on 29 degrees of freedom
## Multiple R-squared:  0.801,  Adjusted R-squared:  0.7873 
## F-statistic: 58.36 on 2 and 29 DF,  p-value: 6.818e-11

Reference/baseline group (not v-shaped): \[\widehat y = 33.0 -4.44 x\]
V-shaped: \[\widehat y = 33.0 + 3.15 -4.44 x = 36.15 -4.44 x. \]

Interaction model

We can expand the regression model by including an interaction between \(x\) and \(z\): \[E(y|x,z) = \alpha + \beta_1 x + \beta_2 z + \beta_3 z\cdot x.\]
This yields a regression line for engine type:
Not v-shaped (\(z=0\)): \(E(y|x,z) = \alpha + \beta_1 x\)
V-shaped (\(z=1\)): \(E(y|x,z) = \alpha + \beta_2 + (\beta_1 + \beta_3) x\).
\(\beta_2\) is the difference in Intercept between the two groups, while \(\beta_3\) is the difference in slope between the two groups.

Example: Prediction equations

When we use * in the model formula we include interaction between vs and wt:

model2 <- lm(mpg ~ wt * factor(vs), data = mtcars)
summary(model2)

## 
## Call:
## lm(formula = mpg ~ wt * factor(vs), data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9950 -1.7881 -0.3423  1.2935  5.2061 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     29.5314     2.6221  11.263 6.55e-12 ***
## wt              -3.5013     0.6915  -5.063 2.33e-05 ***
## factor(vs)1     11.7667     3.7638   3.126   0.0041 ** 
## wt:factor(vs)1  -2.9097     1.2157  -2.393   0.0236 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.578 on 28 degrees of freedom
## Multiple R-squared:  0.8348, Adjusted R-squared:  0.8171 
## F-statistic: 47.16 on 3 and 28 DF,  p-value: 4.497e-11

We use the output to write the prediction equations:
- Reference/baseline group (not v-shaped): \[\widehat y = 29.5 -3.5 x\]
- V-shaped: \[\widehat y = (29.5+ 11.8) +(-3.5 -2.9)x = 41.3 -6.4 x. \]

Example: Individual tests

summary(model2)

## 
## Call:
## lm(formula = mpg ~ wt * factor(vs), data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9950 -1.7881 -0.3423  1.2935  5.2061 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     29.5314     2.6221  11.263 6.55e-12 ***
## wt              -3.5013     0.6915  -5.063 2.33e-05 ***
## factor(vs)1     11.7667     3.7638   3.126   0.0041 ** 
## wt:factor(vs)1  -2.9097     1.2157  -2.393   0.0236 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.578 on 28 degrees of freedom
## Multiple R-squared:  0.8348, Adjusted R-squared:  0.8171 
## F-statistic: 47.16 on 3 and 28 DF,  p-value: 4.497e-11

The difference in slope between the two engine types is estimated to \(\widehat\beta_3=-2.9\) which is significant with p-value=0.0236, so the slopes are significantly different.

plotModel(model2)

Hierarchy of models

Always test for no interaction (\(\beta_3=0\)) before making tests for main effects (\(\beta_1=0\) or \(\beta_2=0\)).

F-test

We can compare the two models in the mtcars example, namely the model with and without interaction via

anova(model1, model2)

## Analysis of Variance Table
## 
## Model 1: mpg ~ wt + factor(vs)
## Model 2: mpg ~ wt * factor(vs)
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1     29 224.09                              
## 2     28 186.03  1    38.062 5.7287 0.02363 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Multiple linear regression

Example from last lecture

Crime data set

Multiple regression model for crime data

The general model

Regression model

Interpretation of parameters

Estimation

Estimation of model parameters

Estimation of error variance

Multiple R-squared

Multiple \(R^2\)

Example

Example

F-test for comparing two models

Overall F-test for effect of predictors

F-test

Example

Interaction model

Interaction between effects of predictors

Example - interaction model

Multiple linear regression with categorical predictors

Dummy variables

Example

Example

Example

Example: Prediction equations

Interaction model

Example: Prediction equations

Example: Individual tests

Hierarchy of models

F-test