Assignment1

Published

December 4, 2024

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Assignment 1

1. Anscombe’s examples

       x1             x2             x3             x4           y1        
 Min.   : 4.0   Min.   : 4.0   Min.   : 4.0   Min.   : 8   Min.   : 4.260  
 1st Qu.: 6.5   1st Qu.: 6.5   1st Qu.: 6.5   1st Qu.: 8   1st Qu.: 6.315  
 Median : 9.0   Median : 9.0   Median : 9.0   Median : 8   Median : 7.580  
 Mean   : 9.0   Mean   : 9.0   Mean   : 9.0   Mean   : 9   Mean   : 7.501  
 3rd Qu.:11.5   3rd Qu.:11.5   3rd Qu.:11.5   3rd Qu.: 8   3rd Qu.: 8.570  
 Max.   :14.0   Max.   :14.0   Max.   :14.0   Max.   :19   Max.   :10.840  
       y2              y3              y4        
 Min.   :3.100   Min.   : 5.39   Min.   : 5.250  
 1st Qu.:6.695   1st Qu.: 6.25   1st Qu.: 6.170  
 Median :8.140   Median : 7.11   Median : 7.040  
 Mean   :7.501   Mean   : 7.50   Mean   : 7.501  
 3rd Qu.:8.950   3rd Qu.: 7.98   3rd Qu.: 8.190  
 Max.   :9.260   Max.   :12.74   Max.   :12.500  
       x1             x2             x3             x4           y1        
 Min.   : 4.0   Min.   : 4.0   Min.   : 4.0   Min.   : 8   Min.   : 4.260  
 1st Qu.: 6.5   1st Qu.: 6.5   1st Qu.: 6.5   1st Qu.: 8   1st Qu.: 6.315  
 Median : 9.0   Median : 9.0   Median : 9.0   Median : 8   Median : 7.580  
 Mean   : 9.0   Mean   : 9.0   Mean   : 9.0   Mean   : 9   Mean   : 7.501  
 3rd Qu.:11.5   3rd Qu.:11.5   3rd Qu.:11.5   3rd Qu.: 8   3rd Qu.: 8.570  
 Max.   :14.0   Max.   :14.0   Max.   :14.0   Max.   :19   Max.   :10.840  
       y2              y3              y4        
 Min.   :3.100   Min.   : 5.39   Min.   : 5.250  
 1st Qu.:6.695   1st Qu.: 6.25   1st Qu.: 6.170  
 Median :8.140   Median : 7.11   Median : 7.040  
 Mean   :7.501   Mean   : 7.50   Mean   : 7.501  
 3rd Qu.:8.950   3rd Qu.: 7.98   3rd Qu.: 8.190  
 Max.   :9.260   Max.   :12.74   Max.   :12.500  

Call:
lm(formula = y1 ~ x1, data = anscombe)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.92127 -0.45577 -0.04136  0.70941  1.83882 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   3.0001     1.1247   2.667  0.02573 * 
x1            0.5001     0.1179   4.241  0.00217 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.237 on 9 degrees of freedom
Multiple R-squared:  0.6665,    Adjusted R-squared:  0.6295 
F-statistic: 17.99 on 1 and 9 DF,  p-value: 0.00217

Call:
lm(formula = y2 ~ x2, data = anscombe)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9009 -0.7609  0.1291  0.9491  1.2691 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)    3.001      1.125   2.667  0.02576 * 
x2             0.500      0.118   4.239  0.00218 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.237 on 9 degrees of freedom
Multiple R-squared:  0.6662,    Adjusted R-squared:  0.6292 
F-statistic: 17.97 on 1 and 9 DF,  p-value: 0.002179

Call:
lm(formula = y3 ~ x3, data = anscombe)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.1586 -0.6146 -0.2303  0.1540  3.2411 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   3.0025     1.1245   2.670  0.02562 * 
x3            0.4997     0.1179   4.239  0.00218 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.236 on 9 degrees of freedom
Multiple R-squared:  0.6663,    Adjusted R-squared:  0.6292 
F-statistic: 17.97 on 1 and 9 DF,  p-value: 0.002176

Call:
lm(formula = y4 ~ x4, data = anscombe)

Residuals:
   Min     1Q Median     3Q    Max 
-1.751 -0.831  0.000  0.809  1.839 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   3.0017     1.1239   2.671  0.02559 * 
x4            0.4999     0.1178   4.243  0.00216 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.236 on 9 degrees of freedom
Multiple R-squared:  0.6667,    Adjusted R-squared:  0.6297 
F-statistic:    18 on 1 and 9 DF,  p-value: 0.002165

Analysis of Variance Table

Response: y1
          Df Sum Sq Mean Sq F value  Pr(>F)   
x1         1 27.510 27.5100   17.99 0.00217 **
Residuals  9 13.763  1.5292                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Variance Table

Response: y2
          Df Sum Sq Mean Sq F value   Pr(>F)   
x2         1 27.500 27.5000  17.966 0.002179 **
Residuals  9 13.776  1.5307                    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Variance Table

Response: y3
          Df Sum Sq Mean Sq F value   Pr(>F)   
x3         1 27.470 27.4700  17.972 0.002176 **
Residuals  9 13.756  1.5285                    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Variance Table

Response: y4
          Df Sum Sq Mean Sq F value   Pr(>F)   
x4         1 27.490 27.4900  18.003 0.002165 **
Residuals  9 13.742  1.5269                    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                  lm1      lm2       lm3       lm4
(Intercept) 3.0000909 3.000909 3.0024545 3.0017273
x1          0.5000909 0.500000 0.4997273 0.4999091
$lm1
             Estimate Std. Error  t value    Pr(>|t|)
(Intercept) 3.0000909  1.1247468 2.667348 0.025734051
x1          0.5000909  0.1179055 4.241455 0.002169629

$lm2
            Estimate Std. Error  t value    Pr(>|t|)
(Intercept) 3.000909  1.1253024 2.666758 0.025758941
x2          0.500000  0.1179637 4.238590 0.002178816

$lm3
             Estimate Std. Error  t value    Pr(>|t|)
(Intercept) 3.0024545  1.1244812 2.670080 0.025619109
x3          0.4997273  0.1178777 4.239372 0.002176305

$lm4
             Estimate Std. Error  t value    Pr(>|t|)
(Intercept) 3.0017273  1.1239211 2.670763 0.025590425
x4          0.4999091  0.1178189 4.243028 0.002164602

2. Generative art

Delcan, Pablo. 2024. “I’m Just a Human Sitting in Front of a Stack of Blank Paper, Sketching as Fast as I Can” The New York Times, September 5. https://www.nytimes.com/2024/09/05/opinion/ai-art-model-prompt-brush.html.

Kent, Charlotte. 2020. “The Game of Life - Emergence in Generative Art” The Brooklyn Rail, September 8. https://brooklynrail.org/2020/09/artseen/The-Game-of-Life-Emergence-in-Generative-Art/.

Nayyar, Rhea. 2024. “Real Photographer Beats Out Robots in AI Art Competition” Hyperallergic, June 16. https://hyperallergic.com/925633/real-photographer-beats-out-robots-in-ai-art-competition/.

Pearson, Matt. 2011.”generative art a practical guide using processing” Shelter Island, NY: Manning Publishing Co.

3. Fall export

Color: chocolate3

Fall

4. Critique of a chart

Texas

This article uses stacked area charts to show the percent of electricity produced by type for each state. All share similar issues, but I selected Texas as the example to look at here. For the axes, their main problem is that the text is quite small especially for the percentages and in comparison to the labels of the energy types. An additional issue with the percentages is that while both sides run from 0-100%, the labels are not consistent between the left and the right and as they lack line markings on the sides the labels are difficult to interpret. For the chart itself, the issue is that the shapes are unclear and it is rather more difficult to distinguish different years. The decision to do this likely came from the animation of the different charts at the beginning of the article. However, this choice hampers the charts when they are used outside of the animation. The charts could also use a grid to help distinguish shapes and make the vertical axes cleaner.

Popovich, Nadja. 2024. “How Does Your State Make Electricity?” New York Times, August 2. https://www.nytimes.com/interactive/2024/08/02/climate/electricity-generation-us-states.html.