lm2: Simple Linear Regression (Cloze with Theory and Application)

Exercise template with both theory and applied questions about simple linear regression based on a randomly-generated CSV file.

Name:
lm2
Type:
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Theory: Consider a linear regression of y on x. It is usually estimated with which estimation technique (three-letter abbreviation)?

This estimator yields the best linear unbiased estimator (BLUE) under the assumptions of the Gauss-Markov theorem. Which of the following properties are required for the errors of the linear regression model under these assumptions?

Application: Using the data provided in linreg.csv estimate a linear regression of y on x. What are the estimated parameters?

Intercept:

Slope:

In terms of significance at 5% level:

Theory: Linear regression models are typically estimated by ordinary least squares (OLS). The Gauss-Markov theorem establishes certain optimality properties: Namely, if the errors have expectation zero, constant variance (homoscedastic), no autocorrelation and the regressors are exogenous and not linearly dependent, the OLS estimator is the best linear unbiased estimator (BLUE).

Application: The estimated coefficients along with their significances are reported in the summary of the fitted regression model, showing that y increases significantly with x (at 5% level).


Call:
lm(formula = y ~ x, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.50503 -0.17149 -0.01047  0.13726  0.69840 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.005094   0.023993  -0.212    0.832
x            0.558063   0.044927  12.421   <2e-16

Residual standard error: 0.2399 on 98 degrees of freedom
Multiple R-squared:  0.6116,    Adjusted R-squared:  0.6076 
F-statistic: 154.3 on 1 and 98 DF,  p-value: < 2.2e-16

Code: The analysis can be replicated in R using the following code.

## data
d <- read.csv("linreg.csv")
## regression
m <- lm(y ~ x, data = d)
summary(m)
## visualization
plot(y ~ x, data = d)
abline(m)

Theory: Consider a linear regression of y on x. It is usually estimated with which estimation technique (three-letter abbreviation)?

This estimator yields the best linear unbiased estimator (BLUE) under the assumptions of the Gauss-Markov theorem. Which of the following properties are required for the errors of the linear regression model under these assumptions?

Application: Using the data provided in linreg.csv estimate a linear regression of y on x. What are the estimated parameters?

Intercept:

Slope:

In terms of significance at 5% level:

Theory: Linear regression models are typically estimated by ordinary least squares (OLS). The Gauss-Markov theorem establishes certain optimality properties: Namely, if the errors have expectation zero, constant variance (homoscedastic), no autocorrelation and the regressors are exogenous and not linearly dependent, the OLS estimator is the best linear unbiased estimator (BLUE).

Application: The estimated coefficients along with their significances are reported in the summary of the fitted regression model, showing that y decreases significantly with x (at 5% level).


Call:
lm(formula = y ~ x, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.73798 -0.12940 -0.00055  0.17485  0.71948 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.02682    0.02520   1.064   0.2898
x           -0.08318    0.04181  -1.990   0.0494

Residual standard error: 0.2515 on 98 degrees of freedom
Multiple R-squared:  0.03883,   Adjusted R-squared:  0.02902 
F-statistic: 3.959 on 1 and 98 DF,  p-value: 0.04941

Code: The analysis can be replicated in R using the following code.

## data
d <- read.csv("linreg.csv")
## regression
m <- lm(y ~ x, data = d)
summary(m)
## visualization
plot(y ~ x, data = d)
abline(m)

Theory: Consider a linear regression of y on x. It is usually estimated with which estimation technique (three-letter abbreviation)?

This estimator yields the best linear unbiased estimator (BLUE) under the assumptions of the Gauss-Markov theorem. Which of the following properties are required for the errors of the linear regression model under these assumptions?

Application: Using the data provided in linreg.csv estimate a linear regression of y on x. What are the estimated parameters?

Intercept:

Slope:

In terms of significance at 5% level:

Theory: Linear regression models are typically estimated by ordinary least squares (OLS). The Gauss-Markov theorem establishes certain optimality properties: Namely, if the errors have expectation zero, constant variance (homoscedastic), no autocorrelation and the regressors are exogenous and not linearly dependent, the OLS estimator is the best linear unbiased estimator (BLUE).

Application: The estimated coefficients along with their significances are reported in the summary of the fitted regression model, showing that x and y are not significantly correlated (at 5% level).


Call:
lm(formula = y ~ x, data = d)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.60377 -0.15149  0.00999  0.15205  0.59555 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.02871    0.02338  -1.228    0.222
x            0.03627    0.03927   0.924    0.358

Residual standard error: 0.2329 on 98 degrees of freedom
Multiple R-squared:  0.008632,  Adjusted R-squared:  -0.001484 
F-statistic: 0.8533 on 1 and 98 DF,  p-value: 0.3579

Code: The analysis can be replicated in R using the following code.

## data
d <- read.csv("linreg.csv")
## regression
m <- lm(y ~ x, data = d)
summary(m)
## visualization
plot(y ~ x, data = d)
abline(m)
Description:
Cloze with theory and applied questions about linear regression. The theory part uses knowledge questions in "string" and "mchoice" format. The applied part is based on bivariate numeric data for download in a CSV file (comma-separated values) and uses two "num" and one "schoice" item.
Solution feedback:
Yes
Randomization:
Random numbers and data file
Mathematical notation:
No
Verbatim R input/output:
Yes
Images:
No
Other supplements:
linreg.csv
Template:
Raw: (1 random version)
PDF:
lm2-Rmd-pdf
lm2-Rnw-pdf
HTML:
lm2-Rmd-html
lm2-Rnw-html

Demo code:

library("exams")

set.seed(403)
exams2html("lm2.Rmd")
set.seed(403)
exams2pdf("lm2.Rmd")

set.seed(403)
exams2html("lm2.Rnw")
set.seed(403)
exams2pdf("lm2.Rnw")