Analysis of Grade Inflation at Georgia State University
Did AI or the pandemic permanently alter how university grades are distributed? I recently analyzed historical university grade data, and the results revealed a massive, statistically significant structural break.
AI has dramatically impacted grades. While the research linked looked at grades from a large university in Texas, I wanted to see how graded changed at my alma-mater, Georgia State University.
Using a database of publically accessible grades , I’ve created a website to explore grades. In this notebook I perform a Exploratory Data Analysis to evaluate grade inflation at GSU.
Measuring Grade Inflation by Proportions of A and DWF grades
One way to measure a course’s difficulty (or ease) is by analyzing the proportion of grades. To analyze a courses’ ease, it is sensible to look at the proportion of A grades. To analyze the difficulty, it is sensible to look at the proportion of DWF (D through withdraw-fail) grades.
There’s a couple factors we must consider:
Grade Inflation: With or without AI, grade inflation has been a noted trend
COVID’s Impact: It has been noted that COVID has inflated grades as well
This gives us a couple problems to deal with, but also ways to deal with it.
We group semesters into three periods:
Pre-AI, Pre-COVID (2005 through 2019)
COVID (2020 through 2022)
Post-AI, Post-COVID (2023 through 2025)
We visually inspect our periods. What intuitions and information can we extract from graphs?
We perform a test of proportions, seeing how the periods’ proportions of grades differ.
We perform tests on linear model slopes to see how significant grade inflation changes are periods.
We inspect and evaluate changes to average GPA over time.
We deal with our first problem (Grade Inflation) in our second subsection, for now let’s focus on proportions and get a general idea of grade distributions and grades over time.
Imports and data loading
library (DBI)library (RPostgres)library (dotenv)library (tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.2.1 ✔ readr 2.2.0
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.3 ✔ tibble 3.3.1
✔ lubridate 1.9.5 ✔ tidyr 1.3.2
✔ purrr 1.2.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library (broom)library (knitr)load_dot_env () # reads .env from project root
<- DBI:: dbConnect (:: Postgres (),host = Sys.getenv ("SUPABASE_HOST" ),dbname = Sys.getenv ("SUPABASE_DB" ),user = Sys.getenv ("SUPABASE_USER" ),password = Sys.getenv ("SUPABASE_PASSWORD" ),port = Sys.getenv ("SUPABASE_PORT" ),sslmode = "disable" # required for Supabase <- dbReadTable (con, "grade_letter_totals" )
id crn term_code grade_a grade_b grade_c
1 efd5d62f-2a36-4ff2-9056-340a16e0d38f 80001 200508 16 28 19
2 e3e5f3a3-2cc0-4e12-8998-078978a7a0e3 80002 200508 8 24 25
3 ac842608-1dfb-4c31-a83a-2afb21e0ea2e 80003 200508 14 29 19
4 568c2866-4efa-4613-8e4e-e1c72ec8f47b 80004 200508 19 40 9
5 d970fa44-eebe-461c-a15a-aa847b2b223a 80005 200508 5 23 6
6 5c1335a9-554b-49d1-946f-262099861973 80007 200508 5 36 0
grade_d grade_f wf w other gpa_avg dwf_pct total created_at
1 6 2 0 3 0 2.70 14.9 NA 2026-05-09 21:59:31
2 6 6 0 1 0 2.32 18.6 NA 2026-05-09 21:59:31
3 4 3 0 4 0 2.68 15.1 NA 2026-05-09 21:59:31
4 0 0 1 5 0 3.10 8.1 NA 2026-05-09 21:59:31
5 2 3 0 20 0 2.64 42.4 NA 2026-05-09 21:59:31
6 0 6 0 14 1 2.72 32.3 NA 2026-05-09 21:59:31
section_id
1 8f0131ed-e968-4e14-925e-660edf44f405
2 60b4e609-9187-4fbe-8d3c-e906da6219e2
3 b168177b-58ea-4412-abbb-378593ff3f5b
4 ab2a7e0a-1909-4b41-b486-6a37af3f1f91
5 4e25fe05-ce97-4678-a1d2-f8bbf5a6ef03
6 5927c06e-91f5-49cf-a4ec-163627824af5
Let’s convert our dataframe in long format for easy usage.
<- df |> select (term_code, grade_a: w) |> pivot_longer (cols = grade_a: w,names_to = "grade_type" ,values_to = "count" |> group_by (term_code, grade_type) |> summarize (sum (count))
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by term_code and grade_type.
ℹ Output is grouped by term_code.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(term_code, grade_type))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.
# A tibble: 434 × 3
# Groups: term_code [62]
term_code grade_type `sum(count)`
<chr> <chr> <int>
1 200508 grade_a 33462
2 200508 grade_b 28710
3 200508 grade_c 13191
4 200508 grade_d 3294
5 200508 grade_f 3165
6 200508 w 7294
7 200508 wf 349
8 200601 grade_a 31856
9 200601 grade_b 27557
10 200601 grade_c 12059
# ℹ 424 more rows
<- df_long |> mutate (semester = case_match (str_sub (term_code, 5 , 6 ),"08" ~ "Fall " ,"01" ~ "Spring " ,"05" ~ "Summer " ),grade = case_match (grade_type,"grade_a" ~ "A" ,"grade_b" ~ "B" ,"grade_c" ~ "C" ,"grade_d" ~ "D" ,"grade_f" ~ "F" ,"w" ~ "W" ,"wf" ~ "WF" ),year = as.integer (str_sub (term_code, 3 , 4 )),term = str_c (semester, year),grade = factor (grade, levels = c ("WF" , "W" , "F" , "D" , "C" , "B" , "A" ))|> ungroup () |> rename (count = ` sum(count) ` ) |> select (, - grade_type) |> arrange (term_code)
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `semester = case_match(...)`.
ℹ In group 1: `term_code = "200508"`.
Caused by warning:
! `case_match()` was deprecated in dplyr 1.2.0.
ℹ Please use `recode_values()` instead.
# A tibble: 434 × 6
term_code count semester grade year term
<chr> <int> <chr> <fct> <int> <chr>
1 200508 33462 "Fall " A 5 Fall 5
2 200508 28710 "Fall " B 5 Fall 5
3 200508 13191 "Fall " C 5 Fall 5
4 200508 3294 "Fall " D 5 Fall 5
5 200508 3165 "Fall " F 5 Fall 5
6 200508 7294 "Fall " W 5 Fall 5
7 200508 349 "Fall " WF 5 Fall 5
8 200601 31856 "Spring " A 6 Spring 6
9 200601 27557 "Spring " B 6 Spring 6
10 200601 12059 "Spring " C 6 Spring 6
# ℹ 424 more rows
Graphing grade distributions
ggplot (data = df_long_cleaned, aes (x = term_code, y = count, fill= grade)) + geom_col (position = "fill" , width = 0.7 ) + scale_x_discrete (breaks = unique (df_long_cleaned$ term_code)[seq (1 , length (unique (df_long_cleaned$ term_code)), by = 10 )]+ labs (title = "Proportion of grades by semester" ,x = " " ,y = "Proportion" ,fill = "Grade" + scale_fill_brewer (palette = "Blues" ) + theme_minimal ()
How has the proportion of A grades changed over time at Georgia State University?
To measure a courses’ ease, let’s investigate the proportion A grades.
<- df_long_cleaned |> mutate (grade = case_match (grade,"A" ~ "A" ,.default = "Other" ),grade = factor (grade, levels = c ("Other" , "A" ))ggplot (data = df_A, aes (x = term_code, y = count, fill= grade)) + geom_col (position = "fill" , width = 0.7 ) + scale_x_discrete (breaks = unique (df_A$ term_code)[seq (1 , length (unique (df_A$ term_code)), by = 10 )]+ labs (title = "Proportion of A grades by semester" ,x = "Semester " ,y = "Proportion of As" ,fill = "Grade" + scale_fill_brewer (palette = 1 ) + theme_minimal ()
Graphing A distribution in our subgroups
<- df_A |> filter (term_code >= "200508" & term_code <= "201908" ) |> mutate (period = "Pre-COVID" )<- df_A |> filter (term_code >= "202001" & term_code <= "202208" ) |> mutate (period = "COVID" )<- df_A |> filter (term_code >= "202301" & term_code <= "202601" ) |> mutate (period = "Post-COVID" )<- bind_rows (pre_covid_A_df, covid_A_df, post_covid_A_df)<- combined_df %>% # Enforce the chronological order of your periods mutate (period = factor (period, levels = c ("Pre-COVID" , "COVID" , "Post-COVID" ))) %>% # Group by period and grade to sum up the counts group_by (period, grade) %>% summarize (total_count = sum (count, na.rm = TRUE ), .groups = "drop_last" ) %>% # Calculate proportion relative ONLY to that specific time period's total mutate (proportion = total_count / sum (total_count)) %>% ungroup ()ggplot (df_plot, aes (x = grade, y = proportion, fill = period)) + geom_col (position = "dodge" , width = 0.7 ) + scale_y_continuous (labels = scales:: percent) + # Changes 0.25 to 25% on the y-axis labs (title = "Proportion of A Grades Over Different Time Periods" ,subtitle = "Proportions calculated within each respective time period" ,x = "grade" ,y = "Percentage of Total Grades" ,fill = "Time Period" + scale_fill_brewer (palette = 1 ) + theme_minimal ()
It is clear to see that the proportion of A grade proportions seems to be rising. Interestingly, it seems there was an “A grade cull” starting in Fall 2016. Since then, A grade proportions have exceeded where they where before 2016. Additionally, it seems AI has not affected the general inflation of A grades
Another note of surprise is that summer classes seem to give a higher proportion of A grades than Fall and Spring Semesters. Although not relevant to the central inquiry of this notebook, this should be investigated.
Statistical testing of A grade proportions
However it is important to view this statistically and not just visually. Thus let’s perform some statistical testing.
<- combined_df |> group_by (period) |> summarise (a_count = sum (count[grade == 'A' ]),total_count = sum (count)
# A tibble: 3 × 3
period a_count total_count
<chr> <int> <int>
1 COVID 562210 1245967
2 Post-COVID 677032 1398079
3 Pre-COVID 1672514 4027666
# Proportion test <- prop.test (x = df_groups_A$ a_count, n = df_groups_A$ total_count)tidy (group_test_A) |> rename (` COVID ` = estimate1,` Post-COVID ` = estimate2,` Pre-COVID ` = estimate3|> mutate (across (all_of (as.character (df_groups_A$ period)), ~ scales:: label_percent (accuracy = 0.01 )(.)
# A tibble: 1 × 8
COVID `Post-COVID` `Pre-COVID` statistic p.value parameter method alternative
<chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 45.12% 48.43% 41.53% 21453. 0 2 3-sam… two.sided
Indeed, we can statistically confirm the proportion of A grades is different across time periods. Pre-COVID had the lowest proportion of A grades, followed by COVID gaining a significant bump, and Post-COVID an additional bump.
Investigating rate of A grade inflation across different time periods
We now know that A grade inflation at GSU is a real thing. However a natural question arises: How has the rate of A grade inflation changed across time periods?
I.E. if we draw a line which best describes the proportion of A grades, would the slope of that line be statistically different between time periods?
To answer this we’ll develop a linear model describing the proportion of A grades across time periods, then perform a statistical test to determine if the slopes are the same or different.
# A function to calculate months elapsed <- function (term_code) {# Extract the year and semester parts <- as.numeric (substr (term_code, 1 , 4 ))<- substr (term_code, 5 , 6 )# Assign a baseline month number based on the semester digit <- case_when (%in% c ("01" , "1" ) ~ 1 , # January (Spring) %in% c ("05" , "06" ) ~ 6 , # June (Summer) %in% c ("08" , "8" ) ~ 8 , # August (Fall) TRUE ~ NA_real_ # Calculate total absolute months since Year 0 <- (year * 12 ) + month_of_year# Subtract the baseline (August 2005) so 200508 becomes 0 <- (2005 * 12 ) + 8 <- total_months - baseline_monthsreturn (months_elapsed)
# Build pre COVID df <- pre_covid_A_df |> # Aggregate counts per term and grade group_by (term_code, semester, grade) |> summarise (total_count = sum (count), .groups = "drop_last" ) |> # Calculate proportions while 'other' is still present mutate (proportion = total_count / sum (total_count)) |> ungroup () |> # Drop the 'other' rows now that math is done filter (grade == "A" ) |> # Pivot wider (only the 'A' column will be created) pivot_wider (id_cols = c (semester, term_code),names_from = grade,values_from = proportion|> mutate (months_elapsed = calculate_months_elapsed (term_code)) |> mutate (period = "Pre-COVID" )# Build COVID df and model <- covid_A_df |> # Aggregate counts per term and grade group_by (term_code, semester, grade) |> summarise (total_count = sum (count), .groups = "drop_last" ) |> # Calculate proportions while 'other' is still present mutate (proportion = total_count / sum (total_count)) |> ungroup () |> # Drop the 'other' rows now that math is done filter (grade == "A" ) |> # Pivot wider (only the 'A' column will be created) pivot_wider (id_cols = c (semester, term_code),names_from = grade,values_from = proportion|> mutate (months_elapsed = calculate_months_elapsed (term_code)) |> mutate (period = "COVID" )# Build post COVID df <- post_covid_A_df |> # Aggregate counts per term and grade group_by (term_code, semester, grade) |> summarise (total_count = sum (count), .groups = "drop_last" ) |> # Calculate proportions while 'other' is still present mutate (proportion = total_count / sum (total_count)) |> ungroup () |> # Drop the 'other' rows now that math is done filter (grade == "A" ) |> # Pivot wider (only the 'A' column will be created) pivot_wider (id_cols = c (semester, term_code),names_from = grade,values_from = proportion|> mutate (months_elapsed = calculate_months_elapsed (term_code)) |> mutate (period = "Post-COVID" )<- bind_rows (pre_covid_A_proportion_df, covid_A_proportion_df, post_covid_A_proportion_df) |> mutate (period = factor (period, levels = c ("Pre-COVID" , "COVID" , "Post-COVID" )))<- lm (A ~ months_elapsed * period, data = combined_A_proportion_df)summary (combined_model)
Call:
lm(formula = A ~ months_elapsed * period, data = combined_A_proportion_df)
Residuals:
Min 1Q Median 3Q Max
-0.05431 -0.02689 -0.01283 0.02962 0.09510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4218197 0.0135535 31.123 <2e-16 ***
months_elapsed 0.0001567 0.0001377 1.138 0.260
periodCOVID 0.1380445 0.2768707 0.499 0.620
periodPost-COVID -0.2531477 0.2827822 -0.895 0.375
months_elapsed:periodCOVID -0.0006802 0.0014675 -0.463 0.645
months_elapsed:periodPost-COVID 0.0012743 0.0012503 1.019 0.313
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04484 on 56 degrees of freedom
Multiple R-squared: 0.2363, Adjusted R-squared: 0.1681
F-statistic: 3.465 on 5 and 56 DF, p-value: 0.008472
Interpretations :
Our model compared months_elapsed to the proportion of A grades. With an estimate greater than zero and a \(p\) -value greater than \(0.05\) , we cannot say that the proportion of A grades is significantly increasing pre-COVID
Using the pre-COVID slope as a baseline, we we can see that the COVID and Post-COVID period’s rate of grade inflation did not meaningfully change (with \(p\) -values of \(0.645\) and \(0.313\) respectively).
However as we will see below, the summer semester clearly affects the regression line of the Pre-COVID period due to it’s significantly higher proportion of A grades. Let’s build a model to account for this
<- lm (A ~ months_elapsed * period + semester, data = combined_A_proportion_df)summary (combined_model_with_semester)
Call:
lm(formula = A ~ months_elapsed * period + semester, data = combined_A_proportion_df)
Residuals:
Min 1Q Median 3Q Max
-0.03786 -0.01902 0.00171 0.01519 0.04217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.973e-01 7.647e-03 51.961 <2e-16 ***
months_elapsed 1.249e-04 6.753e-05 1.849 0.0699 .
periodCOVID 1.780e-01 1.365e-01 1.304 0.1978
periodPost-COVID -2.964e-01 1.386e-01 -2.138 0.0371 *
semesterSpring 2.318e-03 6.830e-03 0.339 0.7357
semesterSummer 8.116e-02 6.874e-03 11.807 <2e-16 ***
months_elapsed:periodCOVID -8.773e-04 7.233e-04 -1.213 0.2304
months_elapsed:periodPost-COVID 1.493e-03 6.130e-04 2.436 0.0182 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.02197 on 54 degrees of freedom
Multiple R-squared: 0.8232, Adjusted R-squared: 0.8002
F-statistic: 35.91 on 7 and 54 DF, p-value: < 2.2e-16
Interpertations :
We cannot say that the proportion of A grades is significantly increasing with a months_elapsed \(p\) -value greater than \(0.05\) pre-covid.
With \(p\) -values of \(0.2304\) and \(0.0182\) , our COVID regression line is not significantly different, but our post-COVID line is .
With a value of \(8.12\%\) and \(p\) -value of less than \(0.05\) , we can see that the summer semester significantly improves proportions of A grades.
Graphing regression lines
# Create a clean subset to act as your axis label blueprint. # This grabs every 10th chronological term and remembers its exact month number. <- combined_A_proportion_df |> distinct (term_code, months_elapsed) |> arrange (months_elapsed) |> slice (seq (1 , n (), by = 10 ))# Run the ggplot code ggplot (combined_A_proportion_df, aes (x = months_elapsed, y = A, color = period)) + # Plot the raw proportions as distinct points geom_point (size = 2.5 , alpha = 0.6 ) + # Connect chronological points horizontally (inherits the continuous numeric x-axis) geom_line (alpha = 0.3 , linetype = "dashed" ) + # Overlay the linear regression models flawlessly geom_smooth (method = "lm" , se = TRUE , linewidth = 1.2 ) + # We use continuous scaling, but override the breaks and labels with text! scale_x_continuous (breaks = axis_breaks_df$ months_elapsed,labels = axis_breaks_df$ term_code+ # Visual styling theme_minimal () + labs (title = "Proportion of A Grades Over Time by Historical Periods" ,subtitle = "Not accounting for summer bumps, lines show linear regression trends (Slopes are not statistically distinct)" ,x = "Semester" , # Fixed label text to match what's displaying y = "Proportion of A Grades" ,color = "Period" + scale_y_continuous (labels = scales:: percent_format (accuracy = 1 ), limits = c (0 , 1 )) + scale_color_brewer (palette = "Set1" )
`geom_smooth()` using formula = 'y ~ x'
# Create a clean subset to act as your axis label blueprint. <- combined_A_proportion_df |> distinct (term_code, months_elapsed) |> arrange (months_elapsed) |> slice (seq (1 , n (), by = 10 ))# Generate predictions <- augment (combined_model_with_semester, data = combined_A_proportion_df)# Run the ggplot code # FIX: Change y = A to y = .fitted to plot the adjusted data ggplot (plot_predictions_df, aes (x = months_elapsed, y = .fitted, color = period)) + # Plot the clean, seasonally-adjusted points geom_point (size = 2.5 , alpha = 0.6 ) + # Connect chronological points horizontally without summer spikes breaking the chain geom_line (alpha = 0.3 , linetype = "dashed" ) + # Overlay the adjusted linear regression models geom_smooth (method = "lm" , se = TRUE , linewidth = 1.2 ) + # Continuous scaling with text labels override scale_x_continuous (breaks = axis_breaks_df$ months_elapsed,labels = axis_breaks_df$ term_code+ # Visual styling theme_minimal () + labs (title = "Seasonally-Adjusted Proportion of A Grades Over Time" , # Updated title to match the data subtitle = "Accounting for summer spikes reveals a significant post-COVID acceleration" ,x = "Semester" , y = "Adjusted Proportion of A Grades" , # Updated label to be accurate color = "Period" + scale_y_continuous (labels = scales:: percent_format (accuracy = 1 ), limits = c (0 , 1 )) + scale_color_brewer (palette = "Set1" )
`geom_smooth()` using formula = 'y ~ x'
How has the proportion of DWF grades changed over time at Georgia State University?
To measure a courses’ difficulty, let’s investigate the proportion DF grades.
<- df_long_cleaned |> mutate (grade = case_match (grade,"D" ~ "DWF" ,"F" ~ "DWF" ,"W" ~ "DWF" ,"WF" ~ "DWF" ,.default = "Other" ),grade = factor (grade, levels = c ("Other" , "DWF" ))ggplot (data = df_DWF, aes (x = term_code, y = count, fill= grade)) + geom_col (position = "fill" , width = 0.7 ) + scale_x_discrete (breaks = unique (df_A$ term_code)[seq (1 , length (unique (df_A$ term_code)), by = 10 )]+ labs (title = "Proportion of DWF grades by semester" ,x = "Semester " ,y = "Proportion of DWFs" ,fill = "Grade" + scale_fill_brewer (palette = 1 ) + theme_minimal ()
<- df_DWF |> filter (term_code >= "200508" & term_code <= "201908" ) |> mutate (period = "Pre-COVID" )<- df_DWF |> filter (term_code >= "202001" & term_code <= "202208" ) |> mutate (period = "COVID" )<- df_DWF |> filter (term_code >= "202301" & term_code <= "202601" ) |> mutate (period = "Post-COVID" )<- bind_rows (pre_covid_DWF_df, covid_DWF_df, post_covid_DWF_df)<- combined_df_DWF %>% # Enforce the chronological order of time periods mutate (period = factor (period, levels = c ("Pre-COVID" , "COVID" , "Post-COVID" ))) %>% # Group by period and grade to sum up the counts group_by (period, grade) %>% summarize (total_count = sum (count, na.rm = TRUE ), .groups = "drop_last" ) %>% # Calculate proportion relative ONLY to that specific time period's total mutate (proportion = total_count / sum (total_count)) %>% ungroup ()ggplot (df_plot, aes (x = grade, y = proportion, fill = period)) + geom_col (position = "dodge" , width = 0.7 ) + scale_y_continuous (labels = scales:: percent) + # Changes 0.25 to 25% on the y-axis labs (title = "Proportion of A Grades COVID Time Periods" ,subtitle = "Proportions calculated within each respective time period" ,x = "grade" ,y = "Percentage of Total Grades" ,fill = "Time Period" + scale_fill_brewer (palette = 1 ) + theme_minimal ()
Interestingly, it seems COVID had the highest proportion of DWF grades. It seems that students pre-COVID / AI were less likely to drop a class than after. Additionally, it seems the “A grade cull” as posted above also increased the proportion of DWF grades.
Let’s do a statistical test of proportions to confirm our findings about DWF proportions changing across periods.
<- combined_df_DWF |> group_by (period) |> summarise (dwf_count = sum (count[grade == 'DWF' ]),total_count = sum (count)
# A tibble: 3 × 3
period dwf_count total_count
<chr> <int> <int>
1 COVID 233321 1245967
2 Post-COVID 226321 1398079
3 Pre-COVID 599195 4027666
# Proportion test <- prop.test (x = df_groups_DWF$ dwf_count, n = df_groups_DWF$ total_count)tidy (group_test_DWF) |> rename (` COVID ` = estimate1,` Post-COVID ` = estimate2,` Pre-COVID ` = estimate3|> mutate (across (all_of (as.character (df_groups_DWF$ period)), ~ scales:: label_percent (accuracy = 0.01 )(.)
# A tibble: 1 × 8
COVID `Post-COVID` `Pre-COVID` statistic p.value parameter method alternative
<chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr>
1 18.73% 16.19% 14.88% 10693. 0 2 3-sam… two.sided
Indeed, we can statistically confirm the proportion of DWF grades is different across time periods. Pre-COVID had the lowest proportion of DWF grades, followed by COVID gaining a significant bump, and Post-COVID the proportion of DWF grades shrunk.
Investigating rate of DWF grade proportions across different time periods
We now know that DWF grade proportions have changed during different time periods at GSU. However a natural question arises: How has the rate of DWF grade proportions changed across time periods?
I.E. if we draw a line which best describes the proportion of DWF grades, would the slope of that line be statistically different between time periods?
To answer this we’ll develop a linear model describing the proportion of DWF grades across time periods, then perform a statistical test to determine if the slopes are the same or different.
# Build pre COVID df <- pre_covid_DWF_df |> # Aggregate counts per term and grade group_by (term_code, semester, grade) |> summarise (total_count = sum (count), .groups = "drop_last" ) |> # Calculate proportions while 'other' is still present mutate (proportion = total_count / sum (total_count)) |> ungroup () |> # Drop the 'other' rows now that math is done filter (grade == "DWF" ) |> # Pivot wider (only the 'A' column will be created) pivot_wider (id_cols = c (semester, term_code),names_from = grade,values_from = proportion|> mutate (months_elapsed = calculate_months_elapsed (term_code)) |> mutate (period = "Pre-COVID" )# Build COVID df and model <- covid_DWF_df |> # Aggregate counts per term and grade group_by (term_code, semester, grade) |> summarise (total_count = sum (count), .groups = "drop_last" ) |> # Calculate proportions while 'other' is still present mutate (proportion = total_count / sum (total_count)) |> ungroup () |> # Drop the 'other' rows now that math is done filter (grade == "DWF" ) |> # Pivot wider (only the 'A' column will be created) pivot_wider (id_cols = c (semester, term_code),names_from = grade,values_from = proportion|> mutate (months_elapsed = calculate_months_elapsed (term_code)) |> mutate (period = "COVID" )# Build post COVID df <- post_covid_DWF_df |> # Aggregate counts per term and grade group_by (term_code, semester, grade) |> summarise (total_count = sum (count), .groups = "drop_last" ) |> # Calculate proportions while 'other' is still present mutate (proportion = total_count / sum (total_count)) |> ungroup () |> # Drop the 'other' rows now that math is done filter (grade == "DWF" ) |> # Pivot wider (only the 'A' column will be created) pivot_wider (id_cols = c (semester, term_code),names_from = grade,values_from = proportion|> mutate (months_elapsed = calculate_months_elapsed (term_code)) |> mutate (period = "Post-COVID" )<- bind_rows (pre_covid_DWF_proportion_df, covid_DWF_proportion_df, post_covid_DWF_proportion_df) |> mutate (period = factor (period, levels = c ("Pre-COVID" , "COVID" , "Post-COVID" )))# Account for semester based on A proportion results <- lm (DWF ~ months_elapsed * period + semester, data = combined_DWF_proportion_df)summary (combined_model)
Call:
lm(formula = DWF ~ months_elapsed * period + semester, data = combined_DWF_proportion_df)
Residuals:
Min 1Q Median 3Q Max
-0.033578 -0.013636 -0.001112 0.016232 0.035924
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.361e-01 6.924e-03 19.661 < 2e-16 ***
months_elapsed 1.469e-04 6.115e-05 2.403 0.0197 *
periodCOVID -2.556e-02 1.236e-01 -0.207 0.8369
periodPost-COVID 1.808e-01 1.255e-01 1.440 0.1556
semesterSpring -6.141e-04 6.185e-03 -0.099 0.9213
semesterSummer -3.933e-02 6.225e-03 -6.318 5.25e-08 ***
months_elapsed:periodCOVID 2.914e-04 6.550e-04 0.445 0.6582
months_elapsed:periodPost-COVID -7.935e-04 5.551e-04 -1.430 0.1586
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0199 on 54 degrees of freedom
Multiple R-squared: 0.6486, Adjusted R-squared: 0.6031
F-statistic: 14.24 on 7 and 54 DF, p-value: 2.578e-10
Interpertations :
The proportion of DWF grades is significantly increases with a slope greater than 0 and a \(p\) -value less than \(0.05\) .
Summer semesters lead to a significant decrease of DWF grades.
Across pre-COVID to post-COVID periods, there is no significant increase of DWF grade proportion change.
Graphing regression lines for DWF proportion change
# Create a clean subset to act as your axis label blueprint. <- combined_DWF_proportion_df |> distinct (term_code, months_elapsed) |> arrange (months_elapsed) |> slice (seq (1 , n (), by = 10 ))# Generate predictions <- augment (combined_model, data = combined_DWF_proportion_df)# Run the ggplot code # FIX: Change y = A to y = .fitted to plot the adjusted data ggplot (plot_predictions_df, aes (x = months_elapsed, y = .fitted, color = period)) + # Plot the clean, seasonally-adjusted points geom_point (size = 2.5 , alpha = 0.6 ) + # Connect chronological points horizontally without summer spikes breaking the chain geom_line (alpha = 0.3 , linetype = "dashed" ) + # Overlay the adjusted linear regression models geom_smooth (method = "lm" , se = TRUE , linewidth = 1.2 ) + # Continuous scaling with text labels override scale_x_continuous (breaks = axis_breaks_df$ months_elapsed,labels = axis_breaks_df$ term_code+ # Visual styling theme_minimal () + labs (title = "Seasonally-Adjusted Proportion of DWF Grades Over Time" , # Updated title to match the data subtitle = "Accounting for summer spikes reveals a significant post-COVID acceleration" ,x = "Semester" , y = "Adjusted Proportion of DWF Grades" , # Updated label to be accurate color = "Period" + scale_y_continuous (labels = scales:: percent_format (accuracy = 1 ), limits = c (0 , 1 )) + scale_color_brewer (palette = "Set1" )
`geom_smooth()` using formula = 'y ~ x'
How has the average GPA changed over time at Georgia State University?
id crn term_code grade_a grade_b
1 efd5d62f-2a36-4ff2-9056-340a16e0d38f 80001 200508 16 28
2 e3e5f3a3-2cc0-4e12-8998-078978a7a0e3 80002 200508 8 24
3 ac842608-1dfb-4c31-a83a-2afb21e0ea2e 80003 200508 14 29
4 568c2866-4efa-4613-8e4e-e1c72ec8f47b 80004 200508 19 40
5 d970fa44-eebe-461c-a15a-aa847b2b223a 80005 200508 5 23
6 5c1335a9-554b-49d1-946f-262099861973 80007 200508 5 36
7 b6d517be-e758-4c8f-aae5-c3ce47057e55 80009 200508 15 10
8 5d57de8e-0023-47f8-94bb-76b7614f988c 80010 200508 3 0
9 24893672-3cfc-48de-b4b3-22a08c482ec6 80012 200508 11 7
10 d5137e36-9ab7-4ff9-97ea-afa496c48e39 80014 200508 6 4
11 397c9fe5-7c4d-47a8-b09d-f036d0d476df 80015 200508 29 18
12 ce18eed4-b67c-472c-8dd1-d88154389d59 80017 200508 25 1
13 e9948d45-1747-441e-90f9-87c0e3352f9a 80018 200508 16 2
14 00d34ddb-db77-4d63-a59b-dffc2ecc7b86 80023 200508 7 3
15 163cd76c-3db2-4ec0-b1cb-278dbe3087b2 80029 200508 6 0
16 f7a941b4-f8cc-4b29-8d53-46dd212f6321 80030 200508 0 0
17 1f333219-a49b-4e6b-82bf-c1404d05cabf 80031 200508 0 0
18 3deac163-396e-4820-9b70-057d969e4e9f 80032 200508 0 0
19 36d7ece2-f1fe-476f-a3ab-845ae4cc5d48 80033 200508 0 0
20 2b952e77-84cb-47ea-8e01-d22eb6547295 80035 200508 16 16
21 877c78bd-d629-42ac-8b13-945e5253de86 80036 200508 13 17
22 f5e3b8c7-2f2a-45fa-a873-b09d2f60cb53 80037 200508 51 11
23 900afedc-eddb-432b-acf6-42854b00ce82 80038 200508 15 30
24 5c6ad316-53b8-49ae-873e-c38c5e7fae86 80039 200508 13 9
25 567a91b9-4d69-40b9-a91b-add5a3613140 80040 200508 12 10
26 7cd0ec5c-948c-4331-99e4-7a453216b5d9 80041 200508 2 12
27 5f62f595-3144-4e3e-b751-e595e01dc2aa 80042 200508 3 0
28 97f5ec2d-3128-4306-8b92-3b954fc5a12b 80044 200508 16 2
29 c4d70d2f-4a87-42f8-9046-fe3bac54d3e4 80046 200508 3 5
30 c3a98e3c-5a2d-4156-8659-565a8f210b80 80051 200508 0 0
31 3299d9c9-6bcb-4172-984a-a696285a20a3 80055 200508 0 0
32 0d2b073c-3ef9-49c1-acfe-b28875e0dc36 80128 200508 6 1
33 dfb679c2-e6b0-4174-bdb9-f38b710ac903 80178 200508 5 2
34 aea45f0c-3fdf-4be8-9763-b09615e2e536 80198 200508 6 0
35 28e12a74-dc4b-43ef-aa39-38817813e36e 80242 200508 5 0
36 27d4b599-5f9d-494e-8024-679413ee4430 80243 200508 5 2
37 4c2e47c0-955c-4b59-8450-4f28dd907ced 80244 200508 10 1
38 0c3325a2-0c31-4e99-bb1d-20d590129cb4 80259 200508 5 1
39 efaa1246-2662-4262-8862-3147b4e6eb99 80267 200508 16 6
40 9a63046e-c07a-4eb6-8094-0c54be1c8a37 80268 200508 8 3
41 4fc9db78-bcf1-4178-9718-cc763461e42b 80269 200508 5 4
42 4e5edde1-9ec1-4078-b2ce-b76fe14537bd 80270 200508 13 2
43 f5110d31-9b48-4971-9315-39f0cae0544f 80271 200508 11 4
44 b3c7e652-e4b5-4eda-a30f-e71f0b835039 80272 200508 6 10
45 542c1f2b-c389-4298-a49f-d959fb574c86 80273 200508 9 8
46 3a9bbdcf-5d34-4fbe-acf6-4874bbf22c1e 80274 200508 12 3
47 6919f8b0-65b2-4ad6-b735-f5292b299714 80276 200508 10 6
48 62477cef-426f-4a40-b8da-77e518d85e69 80277 200508 8 3
49 fed85ecc-0dd8-4684-ad3b-e11e24652e2d 80278 200508 8 5
50 f8d3fcbf-ce8d-4c84-ae75-ddd72b3e7d4e 80279 200508 11 5
51 7deda5ca-25aa-4f05-86b4-d19fec37c615 80280 200508 13 6
52 58a59739-f631-44c2-820a-0c8b46f6ff4b 80281 200508 14 3
53 c4ce3f99-4e28-45a5-a28e-8f3539eebb1c 80282 200508 7 0
54 f0e791f5-fd1a-4232-aa33-1cef2656ec20 80283 200508 8 2
55 5626da52-31f4-4949-b95b-39de4633f92e 80284 200508 3 5
56 71430837-372f-433b-baaf-6e568449d009 80285 200508 6 7
57 1b581fa8-9978-4fa0-8029-2bcbf3877dc5 80286 200508 14 4
58 86129f19-5ddc-4997-957a-f19e7fe4a9a0 80287 200508 13 0
59 3e903a11-1e5a-4519-a39a-cb606054bf9e 80288 200508 9 2
60 583b0608-c29d-4719-8463-5fb9faeb517c 80289 200508 9 1
61 83ac04c4-2ed8-4861-b08e-37d9d17f2362 80290 200508 15 0
62 776eadf1-6cd6-4868-aa59-6dac94ef6222 80291 200508 11 6
63 3f2b7a53-a1c6-4ec9-b584-db9d4696d5fb 80292 200508 1 2
64 2f4e7d73-5185-4d85-803e-038bd8755eb3 80293 200508 0 0
65 264557b6-4611-4a9a-a3ad-eb0429a83865 80294 200508 0 0
66 adf8388c-67d1-4e7f-8ad0-90ba6936548c 80296 200508 20 25
67 df19f6b0-00d3-4d5e-b69c-4389b872b1c5 80297 200508 6 3
68 81804198-f594-4cfc-a214-028555eec0d8 80298 200508 3 2
69 ea61a48c-d2e5-4c12-ad01-32b8e47d110e 80299 200508 7 5
70 4bd5e854-58bb-4afc-ac91-c24a3693905c 80300 200508 3 6
71 027ce795-20d8-46cf-b9bc-c3a1f9c4c7fc 80301 200508 4 2
72 35a59dce-1877-4fe0-8b17-61045384ffb8 80302 200508 9 2
73 26ffeea4-53fb-485e-ac16-1cf3a8e112d2 80303 200508 6 3
74 43c50e96-45fa-4640-b0d2-50322231fe33 80304 200508 2 0
75 b5ff4f3a-0572-4b1d-8ac7-840bf2834d5d 80305 200508 1 5
76 4d41af3c-1c8d-43b0-bf73-4dcc09767350 80306 200508 11 9
77 a55e523f-a9fd-4b56-8dc0-d7ab54f19f8c 80307 200508 12 6
78 f06fbeab-1299-4941-9a25-7bf4eda1a567 80308 200508 8 0
79 b544c168-cfaa-45a5-8f99-052211815e57 80309 200508 7 4
80 0e87ad16-97a4-4b5e-9e74-274893c21042 80310 200508 7 5
81 5b41e55f-2e46-4033-ba8d-ea1e160c99f7 80311 200508 8 3
82 91f0f75e-1329-4b58-b5b0-66320f9f29df 80312 200508 8 4
83 5303b7d8-3969-4d8a-8c50-f27919fa2d39 80313 200508 13 4
84 ee65dec3-7bd8-4a95-85b0-e87c487580bc 80314 200508 14 6
85 4ff93e9b-74cd-4148-9847-1dcd4635c5c8 80315 200508 3 4
86 d5eefa38-2f91-4292-b96a-9057a51e7949 80316 200508 2 4
87 496b2cdc-4335-4d70-8dd5-ff37374f96f9 80317 200508 3 5
88 24cc3466-a2fa-4c38-aec5-65de4ba7f42a 80318 200508 1 8
89 e23417c5-5c2e-4bcb-b8b6-a67a8e3a4748 80319 200508 3 4
90 7e096305-88d4-4e04-8187-98b33d7754e8 80320 200508 2 9
91 1d22d964-0e8e-4805-a119-d1165af817df 80325 200508 3 5
92 900ea955-26ec-431d-bca2-f7808fd7c391 80326 200508 2 14
93 82f12e3c-ae4b-4817-a05f-c51ccfc8e96c 80327 200508 2 2
94 c21d6a10-eed1-4f33-92cb-d97b729b4e8a 80328 200508 1 1
95 38325163-9e94-4ef8-b59f-e2dd3a5d30cc 80329 200508 7 9
96 aac69248-562e-44e5-a5b7-2bdf9f6ec2df 80330 200508 5 8
97 7f61f969-8433-474c-9e94-262de8c2c0a1 80331 200508 7 8
98 c707bf1d-1a61-4cd1-b1b0-f0855808b869 80334 200508 3 3
99 0ae8eb72-2c4c-47e3-8100-b8d632da6e0d 80340 200508 10 0
100 f5f27417-0d01-40cb-9670-a6036e48926d 80344 200508 0 5
101 6ff58e98-2cc5-499e-9780-ca5c585385b5 80345 200508 1 8
102 9a71d9fd-cf84-4174-94e8-f163bc9d5f0e 80346 200508 6 3
103 dccd043e-4c48-421e-a0f8-31523a97b880 80347 200508 4 3
104 4de4e603-2639-457d-91a6-02ab1c6fa813 80348 200508 3 6
105 08038cb5-c3c8-4aa2-a710-d566e29b3e16 80349 200508 0 4
106 c433e8a3-8d50-4481-9252-af526bdb0036 80350 200508 2 7
107 738fd993-4ef8-4bb1-a26b-c681ce02e5b9 80351 200508 6 4
108 7cdbc045-7dd2-46c1-8b3f-3c866cc301fb 80352 200508 8 3
109 6d97e20c-63e6-4ff6-836f-41eba1b7f68b 80353 200508 4 9
110 5d2ec316-e359-4a17-aa4c-2e0ac424076c 80354 200508 5 7
111 db1b971b-9db7-42b1-aa62-f31284a4e418 80355 200508 5 4
112 14fbfdc3-0fcb-4344-8177-24782a93d66d 80356 200508 5 4
113 9c102dec-6ca0-45bb-8457-5cc178bee88c 80357 200508 8 8
114 69d7ccc7-5cf1-4dfa-ac11-812d9a8e24b7 80358 200508 5 9
115 3113e937-4c4c-440d-ab47-575499ef6b15 80359 200508 2 10
116 34441eee-e1d4-4702-96e6-6cd4334dd961 80360 200508 6 6
117 86741881-5009-4ff4-803f-0d812ab7cc61 80361 200508 3 6
118 fc0675cf-7651-40cc-b084-512fa23db12a 80362 200508 10 9
119 b4e64cae-8107-4dcd-8bf2-a1c4d0f3b58d 80363 200508 11 5
120 1cb56e02-682b-4075-a8b0-03463a388264 80364 200508 5 9
121 4fcc0d8f-9584-463f-85b8-3a78ef3dffa3 80365 200508 7 8
122 8490ddea-cb31-4aa7-b9a1-01995fbbf2a3 80366 200508 3 2
123 a6c835f0-6c45-45d7-880d-5f1081d22340 80367 200508 16 22
124 936c4eee-1731-43bd-9866-a0d31375991b 80368 200508 7 4
125 fe6bc312-60db-4e67-894f-d86b5a4254f6 80369 200508 12 25
126 fa765c8d-957c-4f36-9a50-21de22924a7a 80370 200508 34 36
127 4631c95f-f5a6-469b-b16d-a8398c9ecf3a 80371 200508 17 2
128 72ad91e7-0ea2-43f7-8fc9-a7b2f1689941 80372 200508 17 4
129 251b1963-47e7-4010-bbc3-b8e4b0159178 80373 200508 11 8
130 a556adb4-6216-46c0-8c4a-3d3b45e00c8f 80374 200508 9 9
131 433fb9ce-4fd1-49e2-9f11-f5768c5d8e7d 80375 200508 21 14
132 e02ce642-15c4-4bb1-84b7-00ac213c1793 80376 200508 15 9
133 b89283cc-d7e1-47b9-bba5-41dbf95d8cec 80377 200508 11 1
134 617237a0-d8b1-4ab2-935c-f963f228f955 80378 200508 8 11
135 975f5f1a-7a69-42bc-a834-20bb00e964d1 80380 200508 16 6
136 33176e71-dc3a-465f-9b73-ba07e94f4153 80381 200508 18 3
137 72d9d9a1-c1f8-4f10-a36a-0d94afe2307f 80382 200508 20 2
138 0f73f825-8a1c-4439-b868-250a74925ecf 80383 200508 12 11
139 5d35e381-4231-44ab-ab0a-276e24848db0 80384 200508 26 17
140 01ca6010-9a34-4680-8baa-144770a72bc4 80386 200508 4 9
141 c2c15bd6-7297-4805-ad94-d63e5ff540ea 80387 200508 12 5
142 b66d067c-f210-44b5-9ad9-10dd24683b03 80388 200508 13 19
143 5dd8fce0-3f8d-4074-9b77-0aabe9dcafa7 80389 200508 9 12
144 3d8962f4-9400-4aa4-b63a-b7ed1c54d3d5 80391 200508 9 11
145 7dcda5f4-366f-4104-8551-07c3a401eef3 80392 200508 13 9
146 2fb50a49-3194-4281-9230-d9ef3a61527d 80393 200508 19 19
147 5560ef8d-944a-4747-8367-528d8c7488c3 80394 200508 19 19
148 5786e190-bd8c-4b21-8011-318e3193a007 80395 200508 5 1
149 af92c6a2-633d-41e8-8ea1-42ae99ab3455 80396 200508 8 7
150 8765c3a4-4dcb-4819-b000-620cc42b892e 80397 200508 9 2
151 33ca0cd3-ef2c-4dd4-bde4-a76cf0c1d41d 80398 200508 8 10
152 48d588b9-d547-44dd-9c57-c0789d8a44c2 80399 200508 14 21
153 016bc931-3667-4135-ada6-a57cb026c411 80400 200508 9 27
154 828cd380-5c97-4adf-b70d-66646709f788 80401 200508 4 4
155 db8b2797-e2fe-40be-8340-e831516a0312 80402 200508 11 0
156 a6452ae1-59bc-4043-a56f-67c5e94880ac 80403 200508 10 8
157 09eebccd-f6ae-46b4-8b4d-d8c1c4a5288c 80405 200508 0 0
158 ed1248b4-8b8a-4caa-b4f6-65636a19020b 80407 200508 4 5
159 ae5d9a3e-f8bd-409e-9a9b-0c5c89cbed5d 80408 200508 6 5
160 33535d2b-23b8-4040-94d1-e3bf79f39a28 80409 200508 12 10
161 49311c76-7a7e-4dfd-bc20-84a67a0c5e63 80410 200508 11 7
162 a0564849-0c8b-4677-980c-1f8f6368bf43 80411 200508 13 0
163 09a0d501-67dd-4434-a413-005df653d5eb 80412 200508 4 1
164 023dad28-fe93-4e0c-bfb0-183a87a32ffa 80413 200508 0 0
165 c082e6af-2241-4202-a806-73472eb319a0 80431 200508 11 0
166 9b7df73d-f3ed-49c5-8ad5-a5efa52eaedd 80432 200508 0 0
167 7e08437e-519a-4181-ba8d-fdac71018e79 80433 200508 4 6
168 53a0f5d7-071d-4fa4-a9ff-c442a2ab5379 80435 200508 0 0
169 2524bccb-5364-4af1-ae24-f2fe6e021e67 80439 200508 4 3
170 b2e0d421-dde4-4fb3-a3a7-aea527994ab5 80440 200508 8 1
171 725428aa-c852-44b5-be51-dc0127659473 80442 200508 7 2
172 1b0415b3-9e88-4dd2-a28e-a8a4c06da77a 80443 200508 3 3
173 d30594fb-e74a-406c-bfb5-52b6f0bac61d 80451 200508 8 2
174 176602d0-9b6e-423b-82aa-457afd27eca3 80452 200508 0 4
175 0c393f80-65ac-4f6f-8c8f-8cb01a5eb218 80453 200508 5 4
176 1ecc3743-a912-4bc5-8c35-363aacc30a9b 80454 200508 4 4
177 e47ffb0b-5383-44ed-903c-a94057fe6d0a 80455 200508 4 5
178 82bc0441-5a63-446f-b87d-15011547e1fe 80456 200508 5 5
179 0d7500ba-909c-4fdc-9c09-0bbe0a0b73d7 80457 200508 7 10
180 9639860a-e357-4fd9-9222-f2cfaf46ae5c 80458 200508 7 5
181 b7ff3aa3-8c68-40ad-9d5c-9d997ceb6f87 80459 200508 13 6
182 64bd16fe-0883-4f14-8f5c-0a60ad6baf1f 80460 200508 8 7
183 9f61a73c-083b-4a5b-bc28-331b5f73434a 80461 200508 1 4
184 df490997-81cf-4a1d-906e-b2b5ef9acf1c 80462 200508 3 2
185 bd53cad7-78af-421c-8ee3-6df9160ec52c 80463 200508 17 1
186 d967bbbd-facc-4ea6-b7f6-f42bb2a93212 80464 200508 8 0
187 7c62cb4c-8358-4fbc-a426-5ef9d131d126 80465 200508 7 0
188 2a4d9b23-73a9-42f1-bfdb-f1ade61a0d49 80466 200508 7 0
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6221 32cb6ace-c1e8-4814-918f-7fed46880a80 85128 200608 21 2
6222 a9294f3c-0651-46c3-b0a2-71c5d41d2ac4 85133 200608 18 0
6223 e476e335-7f33-4a9f-bb02-1830e12f1fe4 85135 200608 11 5
6224 df8d6f82-7d49-48ef-99d7-f9b266b0f310 85136 200608 15 0
6225 75cc1602-9db6-42ea-aa49-c48c1264b730 85137 200608 15 1
6226 5bd6456b-f610-4e9b-83f8-2b6989da4f22 85139 200608 6 1
6227 e1fab415-d422-48d5-ba05-e2f012544b40 85154 200608 8 8
6228 6774bb86-cd5a-4250-9cc7-cfcf1e851e89 85158 200608 15 1
6229 45774e26-4af7-4599-8fa9-a4d83a7e8939 85159 200608 18 0
6230 db484797-ab85-4218-aad8-29fe8d19028d 85164 200608 0 0
6231 cfbf275b-e537-45c5-99e4-96cc279ec120 85167 200608 43 49
6232 e372a773-850a-4f05-909c-a25afb4596a4 85170 200608 37 20
6233 84984c89-8301-42b9-9e3d-0d84390a7b18 85171 200608 7 13
6234 345d631e-7a1e-443f-a809-a0aa75d0c4af 85172 200608 21 1
6235 d9a05c40-6ef3-40da-878e-3268a404a9fa 85173 200608 4 2
6236 f513cd25-adb4-4ebf-a70a-bc035708e2b0 85174 200608 7 6
6237 ea24ac17-61c0-42d7-afe5-d56e17226b65 85175 200608 5 3
6238 ce4ed855-a30b-4568-a7e3-1e2e98c82efd 85176 200608 6 15
6239 39d9161c-332e-47d7-b80d-d68518f7eb75 85177 200608 18 9
6240 9372862f-f0cc-4bd6-adb8-96f5b32077ec 85178 200608 2 7
6241 9c2bf414-b668-4b93-b84d-38bbf68abd5f 85179 200608 8 1
6242 0b0f301a-085c-463e-a5f9-a4b226038110 85185 200608 13 9
6243 411f28df-ef5e-4290-8b5d-bb46f45983b7 85197 200608 26 17
6244 346cedc8-baf2-4e78-8006-ff0fb847b5c9 85198 200608 17 0
6245 6e501f26-cbdb-4695-9422-9565386e3daa 85199 200608 4 5
6246 a1f838de-a194-4f64-a1c0-23105409baac 85202 200608 5 1
6247 fcdd964a-9d40-4b79-8f50-77dc4ce70d58 85206 200608 15 3
6248 2f08fcbd-9fa7-4a92-b96a-229ed6df548d 85207 200608 4 4
6249 223363bd-0b5b-4a29-bb18-177b9acee1ec 85208 200608 8 6
grade_c grade_d grade_f wf w other gpa_avg dwf_pct total
1 19 6 2 0 3 0 2.70 14.9 NA
2 25 6 6 0 1 0 2.32 18.6 NA
3 19 4 3 0 4 0 2.68 15.1 NA
4 9 0 0 1 5 0 3.10 8.1 NA
5 6 2 3 0 20 0 2.64 42.4 NA
6 0 0 6 0 14 1 2.72 32.3 NA
7 3 2 1 0 0 1 3.16 9.4 NA
8 2 2 0 0 0 0 2.57 28.6 NA
9 1 0 0 0 0 0 3.53 0.0 NA
10 1 0 2 0 0 0 2.92 15.4 NA
11 0 0 1 0 5 0 3.54 11.3 NA
12 0 0 0 0 0 0 3.96 0.0 NA
13 3 0 0 0 0 0 3.62 0.0 NA
14 1 0 0 0 1 0 3.55 8.3 NA
15 0 0 0 0 0 0 4.00 0.0 NA
16 0 0 0 0 0 23 NA 0.0 NA
17 0 0 0 0 0 16 NA 0.0 NA
18 0 0 0 0 0 14 NA 0.0 NA
19 0 0 0 0 0 11 NA 0.0 NA
20 27 12 4 0 16 5 2.37 33.3 NA
21 22 4 4 2 10 1 2.44 27.4 NA
22 0 0 2 1 1 0 3.65 6.1 NA
23 14 5 4 2 8 0 2.61 24.4 NA
24 5 2 0 0 1 0 3.14 10.0 NA
25 10 7 4 0 10 1 2.44 38.9 NA
26 11 5 3 1 3 1 2.09 31.6 NA
27 0 0 0 0 2 1 4.00 33.3 NA
28 0 0 0 0 1 0 3.89 5.3 NA
29 1 0 0 0 1 0 3.22 10.0 NA
30 0 0 0 0 0 14 NA 0.0 NA
31 0 0 0 0 0 6 NA 0.0 NA
32 0 0 0 0 0 0 3.86 0.0 NA
33 1 0 0 0 0 0 3.50 0.0 NA
34 0 0 0 0 0 0 4.00 0.0 NA
35 1 0 1 0 0 0 3.14 14.3 NA
36 0 0 0 0 0 0 3.71 0.0 NA
37 0 0 0 0 0 0 3.91 0.0 NA
38 0 0 0 0 0 0 3.83 0.0 NA
39 0 0 0 0 1 0 3.73 4.4 NA
40 0 0 0 0 3 0 3.73 21.4 NA
41 1 0 0 0 2 1 3.40 15.4 NA
42 1 0 2 0 2 0 3.33 20.0 NA
43 0 0 0 0 4 1 3.73 20.0 NA
44 1 0 0 1 3 0 3.11 19.1 NA
45 1 0 1 0 1 0 3.26 10.0 NA
46 1 0 1 0 3 0 3.47 20.0 NA
47 0 1 0 0 2 0 3.47 15.8 NA
48 2 1 1 0 3 0 3.07 27.8 NA
49 2 0 1 0 3 0 3.19 21.1 NA
50 1 0 1 0 3 0 3.39 19.1 NA
51 0 0 0 0 3 0 3.68 13.6 NA
52 0 0 0 0 2 0 3.82 10.5 NA
53 1 0 1 0 0 0 3.33 11.1 NA
54 2 0 0 0 3 0 3.50 20.0 NA
55 3 0 2 0 5 1 2.54 36.8 NA
56 2 0 0 0 0 0 3.27 0.0 NA
57 0 0 0 0 1 0 3.78 5.3 NA
58 0 0 0 0 1 0 4.00 7.1 NA
59 1 0 0 0 1 0 3.67 7.7 NA
60 3 0 1 0 1 0 3.21 13.3 NA
61 0 0 1 0 1 0 3.75 11.8 NA
62 0 0 0 0 0 0 3.65 0.0 NA
63 5 0 1 0 2 0 2.22 27.3 NA
64 0 0 0 0 2 20 NA 9.1 NA
65 0 0 0 0 2 16 NA 11.1 NA
66 23 7 3 0 9 0 2.67 21.8 NA
67 1 0 1 0 1 2 3.18 14.3 NA
68 1 0 1 0 0 1 2.86 12.5 NA
69 0 0 0 0 1 0 3.58 7.7 NA
70 9 3 1 0 1 0 2.32 21.7 NA
71 7 5 2 0 2 0 2.05 40.9 NA
72 2 3 3 0 4 0 2.58 43.5 NA
73 7 2 0 0 4 0 2.72 27.3 NA
74 1 2 2 0 1 0 1.71 62.5 NA
75 3 0 0 0 0 0 2.78 0.0 NA
76 2 0 0 0 0 0 3.41 0.0 NA
77 4 0 0 0 2 0 3.36 8.3 NA
78 1 0 2 0 0 0 3.09 18.2 NA
79 3 0 2 0 2 0 2.88 22.2 NA
80 5 4 1 0 2 0 2.59 29.2 NA
81 7 0 0 0 5 0 3.06 21.7 NA
82 3 4 1 0 2 0 2.70 31.8 NA
83 5 0 1 0 1 0 3.22 8.3 NA
84 1 2 1 0 0 0 3.25 12.5 NA
85 2 0 0 0 1 1 3.11 9.1 NA
86 2 1 1 0 3 0 2.50 38.5 NA
87 3 0 0 0 4 0 3.00 26.7 NA
88 6 5 0 0 1 0 2.25 28.6 NA
89 5 2 3 0 5 0 2.12 45.5 NA
90 3 3 1 0 2 0 2.44 30.0 NA
91 4 2 2 0 0 0 2.31 25.0 NA
92 5 0 0 0 1 0 2.86 4.6 NA
93 4 1 1 0 0 0 2.30 20.0 NA
94 5 0 0 0 0 0 2.43 0.0 NA
95 0 0 1 0 2 0 3.24 15.8 NA
96 4 2 0 0 1 0 2.84 15.0 NA
97 2 0 0 0 1 0 3.29 5.6 NA
98 0 0 0 0 0 0 3.50 0.0 NA
99 0 0 0 0 0 0 4.00 0.0 NA
100 8 1 4 0 4 0 1.78 40.9 NA
101 8 0 4 0 3 0 2.10 29.2 NA
102 9 2 3 0 1 0 2.30 25.0 NA
103 8 4 2 0 4 0 2.14 40.0 NA
104 1 3 3 0 6 1 2.19 52.2 NA
105 9 2 5 0 3 0 1.60 43.5 NA
106 9 2 1 0 2 0 2.33 21.7 NA
107 10 2 1 0 1 0 2.52 16.7 NA
108 3 3 1 0 3 0 2.78 33.3 NA
109 7 0 1 1 2 0 2.59 16.7 NA
110 5 2 2 0 2 1 2.52 25.0 NA
111 4 1 0 0 1 2 2.93 11.8 NA
112 11 1 1 0 2 0 2.50 16.7 NA
113 1 3 0 0 1 1 3.05 18.2 NA
114 6 3 0 0 2 0 2.70 20.0 NA
115 3 0 1 0 7 0 2.75 34.8 NA
116 6 0 1 0 3 0 2.84 18.2 NA
117 3 5 0 0 4 0 2.41 42.9 NA
118 4 0 0 0 1 0 3.26 4.2 NA
119 2 2 0 0 3 0 3.25 21.7 NA
120 3 2 0 0 3 0 2.89 22.7 NA
121 6 0 2 2 0 0 2.56 16.0 NA
122 9 2 1 0 1 1 2.24 21.1 NA
123 8 0 0 0 3 2 3.17 5.9 NA
124 0 0 0 0 2 0 3.64 15.4 NA
125 20 11 9 0 10 2 2.26 33.7 NA
126 23 11 5 0 8 0 2.76 20.5 NA
127 0 0 3 0 3 0 3.36 24.0 NA
128 1 0 0 0 1 0 3.73 4.4 NA
129 2 0 0 0 1 0 3.43 4.6 NA
130 1 0 0 0 3 0 3.42 13.6 NA
131 16 7 2 0 7 0 2.75 23.9 NA
132 16 3 5 2 10 0 2.44 33.3 NA
133 0 1 0 0 1 0 3.69 14.3 NA
134 9 4 2 0 7 0 2.56 31.7 NA
135 1 0 0 0 2 0 3.65 8.0 NA
136 0 0 0 0 3 1 3.86 12.0 NA
137 0 0 0 0 2 0 3.91 8.3 NA
138 2 0 0 0 0 0 3.40 0.0 NA
139 16 1 0 1 7 0 3.08 13.2 NA
140 2 0 2 0 0 0 2.76 11.8 NA
141 2 0 0 1 2 0 3.35 13.6 NA
142 5 1 2 0 2 0 3.00 11.9 NA
143 10 1 0 0 9 0 2.91 24.4 NA
144 2 0 0 0 0 1 3.32 0.0 NA
145 0 0 0 0 0 0 3.59 0.0 NA
146 11 1 3 0 0 0 2.94 7.6 NA
147 11 3 2 0 4 0 2.93 15.5 NA
148 0 0 0 0 0 0 3.83 0.0 NA
149 1 0 1 0 1 0 3.24 11.1 NA
150 0 0 0 0 0 0 3.82 0.0 NA
151 14 1 0 0 1 0 2.76 5.9 NA
152 8 0 1 0 0 0 3.07 2.3 NA
153 14 0 0 0 1 1 2.90 1.9 NA
154 7 5 0 0 2 0 2.35 31.8 NA
155 0 0 0 0 0 0 4.00 0.0 NA
156 1 0 0 0 1 0 3.47 5.0 NA
157 0 0 0 0 4 37 NA 9.8 NA
158 1 0 0 0 1 0 3.30 9.1 NA
159 1 0 0 0 0 0 3.42 0.0 NA
160 2 0 0 0 1 1 3.42 3.9 NA
161 1 0 0 0 1 0 3.53 5.0 NA
162 0 0 0 0 0 0 4.00 0.0 NA
163 1 0 0 0 2 0 3.50 25.0 NA
164 0 0 0 0 1 20 NA 4.8 NA
165 0 0 0 0 0 0 4.00 0.0 NA
166 0 0 0 0 0 29 NA 0.0 NA
167 3 1 0 0 0 3 2.93 5.9 NA
168 0 0 0 0 2 36 NA 5.3 NA
169 4 0 0 0 0 0 3.00 0.0 NA
170 0 0 0 0 2 0 3.89 18.2 NA
171 0 0 0 0 2 0 3.78 18.2 NA
172 0 0 0 0 1 0 3.50 14.3 NA
173 1 1 1 2 2 0 2.73 35.3 NA
174 1 0 1 0 3 0 2.33 44.4 NA
175 2 0 0 0 1 0 3.27 8.3 NA
176 6 2 2 0 3 0 2.33 33.3 NA
177 5 0 2 0 7 0 2.56 39.1 NA
178 1 0 1 0 6 0 3.08 38.9 NA
179 3 2 3 0 2 0 2.64 25.9 NA
180 2 0 1 0 2 0 3.13 17.7 NA
181 0 2 2 1 4 2 3.00 30.0 NA
182 2 0 1 0 5 0 3.17 26.1 NA
183 1 1 1 0 5 0 2.38 53.9 NA
184 1 0 2 0 2 1 2.50 36.4 NA
185 0 0 0 0 5 0 3.94 21.7 NA
186 0 0 0 0 2 0 4.00 20.0 NA
187 0 0 0 0 2 0 4.00 22.2 NA
188 0 0 0 0 1 0 4.00 12.5 NA
189 1 0 0 1 7 0 3.62 24.2 NA
190 0 0 0 0 6 1 3.92 18.8 NA
191 0 0 0 0 0 0 4.00 0.0 NA
192 11 0 1 0 12 0 2.83 27.1 NA
193 4 0 2 0 7 0 2.65 37.5 NA
194 1 1 3 0 3 0 1.17 77.8 NA
195 4 0 3 0 11 0 2.80 38.9 NA
196 14 0 4 0 11 0 2.50 31.9 NA
197 10 0 0 0 2 0 2.91 8.3 NA
198 3 0 1 0 3 0 3.06 20.0 NA
199 3 3 1 0 6 1 2.41 41.7 NA
200 6 2 1 1 3 2 2.42 29.2 NA
201 6 2 3 0 2 0 2.20 31.8 NA
202 7 1 2 0 3 2 2.64 18.2 NA
203 12 0 1 0 5 1 2.63 16.7 NA
204 6 0 0 1 10 4 2.73 30.6 NA
205 7 0 0 0 15 0 2.86 41.7 NA
206 42 16 29 1 37 1 2.27 39.3 NA
207 5 0 0 0 7 1 3.23 20.6 NA
208 3 0 1 0 10 0 3.00 30.6 NA
209 7 0 0 0 8 0 3.14 22.2 NA
210 1 0 0 0 1 0 3.67 2.9 NA
211 63 12 21 0 39 1 2.03 40.0 NA
212 3 0 0 0 1 0 3.10 9.1 NA
213 5 1 0 0 0 0 2.54 7.7 NA
214 8 3 2 0 7 0 2.57 34.3 NA
215 0 0 0 0 1 0 3.11 10.0 NA
216 4 0 1 0 5 2 2.44 37.5 NA
217 42 6 13 0 14 1 2.58 21.2 NA
218 1 0 0 0 0 1 3.36 0.0 NA
219 0 0 0 0 0 0 3.50 0.0 NA
220 0 0 0 0 2 1 3.43 7.7 NA
221 0 0 0 0 0 0 4.00 0.0 NA
222 0 0 0 0 0 7 NA 0.0 NA
223 0 0 0 0 0 6 NA 0.0 NA
224 0 0 0 0 0 6 NA 0.0 NA
225 3 1 0 0 4 0 2.73 33.3 NA
226 3 1 0 0 6 0 3.06 30.4 NA
227 0 0 0 0 0 0 3.71 0.0 NA
228 2 0 1 0 2 0 3.04 12.0 NA
229 0 0 0 0 0 0 3.67 0.0 NA
230 0 0 0 0 2 0 4.00 20.0 NA
231 0 0 0 0 1 0 3.40 9.1 NA
232 1 0 0 0 3 1 3.40 33.3 NA
233 0 0 0 0 0 0 3.78 0.0 NA
234 0 0 0 0 0 1 4.00 0.0 NA
235 3 1 1 0 1 0 3.31 7.5 NA
236 4 5 2 1 3 0 2.41 34.4 NA
237 7 2 1 0 3 1 2.54 21.4 NA
238 4 0 0 0 1 0 3.52 1.8 NA
239 1 0 0 0 3 0 3.79 5.0 NA
240 10 0 0 0 0 0 3.08 0.0 NA
241 13 1 1 2 1 0 3.06 7.6 NA
242 5 1 0 0 6 0 2.86 25.0 NA
243 16 3 0 0 4 0 2.96 11.5 NA
244 11 6 0 0 4 0 2.81 17.2 NA
245 0 0 2 0 1 0 3.72 4.0 NA
246 13 3 0 0 2 0 2.94 9.8 NA
247 9 1 2 0 6 0 3.03 12.9 NA
248 0 0 0 0 0 0 3.33 0.0 NA
249 0 0 0 0 0 33 NA 0.0 NA
250 0 0 0 0 4 1 3.36 21.1 NA
251 0 0 1 0 4 0 3.31 25.0 NA
252 3 0 1 0 4 0 3.35 18.5 NA
253 4 0 1 0 2 0 3.14 9.7 NA
254 1 2 2 0 9 0 2.90 43.3 NA
255 5 0 2 0 5 0 2.84 23.3 NA
256 4 1 2 0 7 0 2.88 30.3 NA
257 3 1 2 0 9 0 3.00 36.4 NA
258 0 0 1 0 5 0 3.60 40.0 NA
259 2 0 0 0 3 0 3.40 23.1 NA
260 0 1 1 0 4 0 2.88 50.0 NA
261 2 1 0 0 1 1 3.27 8.3 NA
262 1 1 0 0 3 0 3.30 30.8 NA
263 2 0 2 0 1 0 2.38 33.3 NA
264 1 1 0 0 0 0 2.88 12.5 NA
265 4 0 5 0 0 0 2.18 29.4 NA
266 1 0 1 0 4 0 3.13 14.3 NA
267 2 2 1 0 1 0 2.00 44.4 NA
268 3 0 3 0 2 0 2.31 33.3 NA
269 6 2 0 0 2 0 2.69 22.2 NA
270 0 2 0 0 1 0 2.83 42.9 NA
271 4 1 2 0 1 0 2.75 19.1 NA
272 2 1 1 0 2 0 2.75 28.6 NA
273 6 7 2 0 7 0 2.21 45.7 NA
274 2 0 0 0 2 0 2.78 18.2 NA
275 2 0 0 0 3 0 3.00 30.0 NA
276 2 0 0 0 2 0 3.27 15.4 NA
277 2 0 2 0 1 0 3.03 10.0 NA
278 2 1 0 0 0 0 2.94 6.3 NA
279 0 0 0 0 0 0 4.00 0.0 NA
280 1 0 0 0 0 1 3.73 0.0 NA
281 0 0 0 0 1 0 3.36 8.3 NA
282 0 0 0 0 0 0 3.88 0.0 NA
283 0 0 0 0 2 1 3.83 22.2 NA
284 0 0 0 0 0 1 4.00 0.0 NA
285 0 0 0 0 2 0 4.00 10.5 NA
286 0 0 0 0 1 0 3.73 6.3 NA
287 3 0 1 0 0 0 3.22 4.4 NA
288 2 0 0 0 2 0 3.25 9.1 NA
289 2 0 1 0 2 1 3.18 21.4 NA
290 1 0 0 0 1 1 3.55 4.2 NA
291 2 0 1 0 1 0 3.50 8.7 NA
292 0 0 0 0 0 0 3.44 0.0 NA
293 0 0 1 0 1 0 3.42 15.4 NA
294 0 0 0 0 2 0 3.67 25.0 NA
295 0 0 1 0 0 0 3.50 4.6 NA
296 1 0 0 0 1 0 3.83 4.0 NA
297 3 0 1 0 0 0 3.16 5.3 NA
298 2 0 0 0 1 0 3.13 4.0 NA
299 0 0 2 1 1 0 3.29 16.0 NA
300 3 0 0 1 1 0 2.72 10.5 NA
301 3 0 2 0 0 0 3.35 7.7 NA
302 5 6 2 0 0 0 2.13 34.8 NA
303 3 1 0 0 4 0 3.14 20.0 NA
304 2 0 1 0 0 0 3.46 3.9 NA
305 0 0 1 0 0 0 3.67 4.2 NA
306 1 1 1 0 0 0 3.07 13.3 NA
307 1 0 1 0 0 0 3.00 4.2 NA
308 2 0 1 0 0 0 3.20 5.0 NA
309 4 0 0 0 0 0 3.32 0.0 NA
310 2 0 3 0 1 0 2.92 14.8 NA
311 4 0 1 0 1 0 3.04 8.3 NA
312 5 1 0 0 0 0 3.08 3.9 NA
313 3 0 1 0 2 0 3.13 11.5 NA
314 0 2 0 1 0 0 3.48 12.0 NA
315 4 1 3 1 0 0 2.71 20.8 NA
316 4 2 2 0 0 0 2.69 15.4 NA
317 5 0 1 0 3 0 2.78 15.4 NA
318 5 1 1 0 1 0 2.84 15.0 NA
319 3 0 0 0 0 0 3.40 0.0 NA
320 1 0 0 0 0 0 3.50 0.0 NA
321 3 0 0 1 0 0 3.00 4.6 NA
322 3 2 1 0 0 0 3.24 12.0 NA
323 2 0 0 0 0 0 3.27 0.0 NA
324 1 0 2 0 0 0 2.96 8.3 NA
325 0 0 0 0 0 0 3.35 0.0 NA
326 1 0 3 0 0 0 2.89 16.7 NA
327 8 3 0 0 1 0 2.46 16.0 NA
328 3 1 2 0 0 0 2.76 14.3 NA
329 5 0 2 0 0 0 2.86 6.9 NA
330 1 3 2 0 1 0 2.81 21.4 NA
331 0 0 0 0 0 0 3.87 0.0 NA
332 5 4 0 0 0 1 2.53 20.0 NA
333 5 0 1 0 0 0 2.81 4.8 NA
334 3 1 3 0 0 0 2.84 16.0 NA
335 4 0 2 0 1 0 3.21 12.0 NA
336 1 1 1 0 0 0 3.08 8.3 NA
337 0 0 0 1 0 0 3.64 4.6 NA
338 6 0 1 0 0 0 2.86 4.6 NA
339 4 0 0 0 0 1 3.19 0.0 NA
340 1 1 3 0 0 1 2.61 21.1 NA
341 1 1 0 0 1 0 3.68 7.7 NA
342 4 0 3 0 0 0 2.79 10.7 NA
343 1 1 1 0 6 0 3.41 34.8 NA
344 4 1 4 0 4 0 2.35 37.5 NA
345 8 0 1 0 6 0 2.50 31.8 NA
346 4 0 2 0 3 0 3.04 18.5 NA
347 3 0 1 0 0 0 3.37 3.7 NA
348 2 0 0 0 2 0 3.43 8.0 NA
349 3 2 1 0 2 0 3.00 20.0 NA
350 5 1 1 1 2 0 2.67 19.2 NA
351 3 2 2 0 2 0 2.91 24.0 NA
352 1 4 0 0 1 0 3.04 19.2 NA
353 4 2 1 0 2 0 2.77 20.8 NA
354 2 2 0 1 6 1 2.95 34.6 NA
355 1 1 0 1 4 0 3.14 23.1 NA
356 2 0 0 0 0 0 3.47 0.0 NA
357 1 3 2 0 6 0 2.80 42.3 NA
358 2 0 2 0 1 0 3.15 11.1 NA
359 2 0 0 0 0 0 3.56 0.0 NA
360 2 0 0 0 1 0 3.52 3.9 NA
361 1 0 0 0 3 0 3.68 12.0 NA
362 1 3 4 1 0 0 2.48 32.0 NA
363 1 1 3 0 1 0 2.89 26.3 NA
364 0 0 2 0 7 1 3.25 37.5 NA
365 1 0 5 1 1 0 2.74 29.2 NA
366 1 0 1 0 0 0 3.40 6.7 NA
367 0 2 0 0 9 0 3.17 52.4 NA
368 7 2 2 1 8 1 2.56 36.1 NA
369 8 0 1 0 4 0 3.00 15.2 NA
370 0 0 1 0 4 0 3.33 14.7 NA
371 4 1 1 0 3 0 3.13 14.3 NA
372 0 0 0 1 1 1 3.41 8.3 NA
373 1 1 0 0 2 0 2.90 25.0 NA
374 2 0 0 0 3 0 3.00 37.5 NA
375 5 1 0 0 12 0 2.69 52.0 NA
376 3 2 1 0 5 0 2.96 24.2 NA
377 3 0 2 0 8 0 2.93 28.6 NA
378 3 0 0 0 2 0 3.45 9.1 NA
379 3 2 1 0 6 0 2.36 45.0 NA
380 9 1 0 0 16 0 2.56 53.1 NA
381 4 0 2 0 2 0 3.30 11.4 NA
382 6 2 3 1 12 0 2.05 52.9 NA
383 2 0 3 0 1 2 3.33 11.1 NA
384 3 0 3 1 4 0 2.31 40.0 NA
385 3 1 3 0 2 1 2.88 17.1 NA
386 3 0 2 0 0 1 2.89 10.5 NA
387 1 0 3 0 3 0 3.15 16.7 NA
388 0 0 0 0 0 0 3.80 0.0 NA
389 0 0 0 0 4 0 3.76 19.1 NA
390 3 0 1 0 4 0 2.94 25.0 NA
391 0 0 0 0 0 1 3.71 0.0 NA
392 3 0 0 0 6 0 3.08 33.3 NA
393 1 0 1 0 0 1 3.33 5.3 NA
394 2 1 0 0 2 1 2.88 15.8 NA
395 3 0 0 0 1 0 3.42 5.0 NA
396 1 0 1 0 0 0 3.45 4.6 NA
397 0 0 0 0 1 0 4.00 10.0 NA
398 0 0 1 1 1 0 3.25 23.1 NA
399 0 0 0 0 0 0 3.91 0.0 NA
400 0 0 0 0 1 1 4.00 10.0 NA
401 0 0 0 0 0 95 NA 0.0 NA
402 0 0 0 0 0 73 NA 0.0 NA
403 1 0 2 0 2 1 3.62 9.5 NA
404 9 0 1 0 4 0 2.92 12.2 NA
405 3 1 2 0 3 0 3.18 14.6 NA
406 7 1 5 0 6 0 2.63 29.3 NA
407 12 9 13 0 9 0 2.85 25.6 NA
408 16 4 6 0 7 0 2.99 14.2 NA
409 20 9 7 0 5 0 2.23 30.0 NA
410 31 9 9 1 10 0 2.54 24.2 NA
411 12 2 2 0 4 0 2.70 16.0 NA
412 21 6 4 0 12 0 2.62 27.5 NA
413 8 1 4 0 6 0 3.26 9.4 NA
414 2 2 0 0 0 0 2.63 25.0 NA
415 0 0 0 1 1 0 3.10 18.2 NA
416 0 0 0 0 0 0 3.60 0.0 NA
417 4 2 2 0 1 0 2.96 17.9 NA
418 2 1 1 0 2 0 3.08 26.7 NA
419 4 1 1 0 6 1 2.83 22.2 NA
420 6 2 0 0 2 1 2.97 9.8 NA
421 7 2 2 0 4 0 2.81 22.9 NA
422 6 0 0 0 2 0 3.04 7.1 NA
423 4 1 0 0 3 1 3.24 10.8 NA
424 0 0 0 0 0 1 3.67 0.0 NA
425 3 1 0 0 0 0 2.92 8.3 NA
426 1 0 1 0 3 0 3.15 25.0 NA
427 0 0 0 0 0 0 3.50 0.0 NA
428 4 0 0 0 1 0 3.19 5.9 NA
429 2 3 0 0 2 0 2.71 26.3 NA
430 2 4 2 0 4 0 2.87 29.4 NA
431 5 1 1 0 5 0 3.07 21.9 NA
432 9 2 2 0 1 0 2.63 15.2 NA
433 0 0 0 0 6 0 4.00 21.4 NA
434 1 1 0 0 2 0 3.24 15.8 NA
435 8 3 1 0 2 0 2.72 19.4 NA
436 4 0 0 0 7 2 3.22 21.9 NA
437 7 1 2 0 2 0 2.77 15.2 NA
438 2 0 0 0 2 0 3.00 16.7 NA
439 8 1 0 2 1 0 2.78 12.1 NA
440 3 1 1 0 4 0 2.94 30.0 NA
441 0 0 0 0 3 0 3.29 17.7 NA
442 5 0 1 0 8 1 2.93 37.5 NA
443 5 0 1 0 3 0 2.93 22.2 NA
444 1 0 0 1 3 0 3.09 28.6 NA
445 0 0 0 0 0 0 3.56 0.0 NA
446 0 0 0 0 0 6 NA 0.0 NA
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1927 6 0 0 0 0 0 2.83 0.0 NA
1928 0 0 0 0 2 0 3.33 14.3 NA
1929 4 2 2 0 2 0 2.69 21.4 NA
1930 9 3 1 1 5 0 3.14 13.0 NA
1931 1 0 2 0 1 0 3.08 11.5 NA
1932 10 1 2 0 2 0 3.16 7.1 NA
1933 6 0 1 0 0 0 3.24 3.0 NA
1934 0 0 0 0 1 0 3.73 8.3 NA
1935 1 0 2 0 5 0 2.91 43.8 NA
1936 3 0 0 0 1 0 3.31 7.1 NA
1937 9 3 1 0 1 1 3.00 11.9 NA
1938 3 0 0 0 3 0 3.18 8.3 NA
1939 2 0 0 0 0 0 2.88 0.0 NA
1940 11 6 0 0 3 1 2.76 18.4 NA
1941 0 0 0 0 0 14 NA 0.0 NA
1942 0 0 0 0 0 22 NA 0.0 NA
1943 0 0 0 0 0 18 NA 0.0 NA
1944 2 1 1 0 3 0 2.85 31.3 NA
1945 1 1 0 0 0 0 3.00 9.1 NA
1946 4 0 1 0 1 1 2.94 10.5 NA
1947 2 2 0 1 0 0 2.74 15.8 NA
1948 2 0 0 0 2 0 3.50 6.7 NA
1949 3 0 1 0 3 1 3.15 23.5 NA
1950 2 0 0 0 0 0 3.80 0.0 NA
1951 0 0 0 0 0 6 NA 0.0 NA
1952 0 0 0 0 0 7 NA 0.0 NA
1953 14 6 6 0 4 0 1.86 40.0 NA
1954 3 0 0 0 1 0 3.08 7.1 NA
1955 7 2 1 0 8 0 2.81 32.4 NA
1956 0 0 0 0 0 1 4.00 0.0 NA
1957 0 0 0 0 0 0 3.75 0.0 NA
1958 2 0 1 0 3 0 3.48 14.3 NA
1959 0 0 0 0 0 0 3.83 0.0 NA
1960 0 0 0 0 1 0 3.83 3.3 NA
1961 0 0 0 0 0 0 3.80 0.0 NA
1962 0 0 0 0 0 2 4.00 0.0 NA
1963 4 0 1 0 2 0 2.87 17.7 NA
1964 9 0 0 0 0 0 2.77 0.0 NA
1965 0 0 11 0 4 1 3.41 15.8 NA
1966 0 0 0 0 1 0 3.67 4.0 NA
1967 9 2 1 1 2 0 2.57 20.0 NA
1968 9 0 2 0 7 0 2.59 25.0 NA
1969 23 14 3 0 3 2 2.75 16.5 NA
1970 3 0 0 0 2 0 2.71 22.2 NA
1971 1 0 0 0 1 0 3.24 4.6 NA
1972 3 0 0 0 1 0 3.24 4.6 NA
1973 7 0 0 1 10 0 2.71 32.4 NA
1974 0 0 0 0 0 0 3.47 0.0 NA
1975 4 1 0 0 1 0 3.00 13.3 NA
1976 0 0 0 0 0 0 4.00 0.0 NA
1977 6 3 1 2 2 0 2.00 40.0 NA
1978 0 0 0 0 1 0 3.71 12.5 NA
1979 0 0 1 0 2 0 2.80 42.9 NA
1980 0 0 0 0 0 6 NA 0.0 NA
1981 0 0 0 0 0 6 NA 0.0 NA
1982 0 0 0 0 1 28 NA 3.5 NA
1983 5 2 0 0 0 0 2.63 12.5 NA
1984 1 0 0 0 0 0 3.17 0.0 NA
1985 0 0 0 0 2 0 3.66 4.1 NA
1986 0 0 0 0 0 0 3.81 0.0 NA
1987 0 0 0 0 0 0 3.88 0.0 NA
1988 10 1 4 0 6 1 2.81 22.5 NA
1989 1 0 1 0 1 0 3.58 7.4 NA
1990 0 0 0 0 2 0 3.43 8.7 NA
1991 5 2 4 1 8 0 2.90 25.9 NA
1992 0 0 0 0 0 2 3.93 0.0 NA
1993 6 2 4 1 6 0 2.58 33.3 NA
1994 7 2 1 0 3 0 2.87 14.6 NA
1995 2 0 0 0 2 0 3.42 9.5 NA
1996 0 0 0 0 1 0 4.00 10.0 NA
1997 12 2 4 1 0 0 2.18 20.6 NA
1998 6 2 0 0 6 0 2.65 30.8 NA
1999 0 0 0 0 0 6 NA 0.0 NA
2000 0 0 0 0 1 18 NA 5.3 NA
2001 0 0 0 0 0 16 NA 0.0 NA
2002 0 0 0 0 0 0 4.00 0.0 NA
2003 0 0 0 0 0 2 3.71 0.0 NA
2004 5 1 1 0 2 0 2.96 13.3 NA
2005 18 4 0 1 6 0 2.37 25.0 NA
2006 0 1 1 0 1 0 3.27 25.0 NA
2007 14 7 0 0 5 0 2.52 23.5 NA
2008 34 6 2 0 10 0 2.53 18.0 NA
2009 22 1 5 1 14 1 2.71 21.4 NA
2010 13 1 2 0 9 0 2.85 19.1 NA
2011 9 2 12 0 5 2 2.84 19.4 NA
2012 1 1 1 0 2 0 2.73 30.8 NA
2013 4 0 1 0 1 0 3.05 10.0 NA
2014 2 0 0 0 0 0 3.37 0.0 NA
2015 3 0 1 0 0 0 2.86 7.1 NA
2016 5 0 0 0 0 0 3.13 0.0 NA
2017 0 0 0 0 0 0 3.83 0.0 NA
2018 7 0 0 0 5 2 3.08 10.6 NA
2019 0 0 0 0 3 0 3.54 18.8 NA
2020 14 3 1 0 2 0 2.62 13.6 NA
2021 1 0 0 0 2 0 3.42 14.3 NA
2022 10 0 0 0 10 2 2.74 28.6 NA
2023 3 4 0 0 4 1 2.47 40.0 NA
2024 3 0 0 0 0 0 3.38 0.0 NA
2025 0 1 0 0 0 0 3.25 12.5 NA
2026 10 1 1 3 4 0 2.69 19.6 NA
2027 0 0 0 0 3 0 4.00 18.8 NA
2028 0 0 0 0 5 0 4.00 19.2 NA
2029 0 0 0 0 3 0 3.96 11.5 NA
2030 0 0 0 0 2 0 4.00 6.1 NA
2031 0 0 2 0 6 0 3.67 26.7 NA
2032 0 0 0 0 7 0 4.00 20.6 NA
2033 1 0 0 0 9 0 3.83 42.9 NA
2034 0 0 0 0 9 0 4.00 28.1 NA
2035 1 0 0 0 0 0 3.11 0.0 NA
2036 1 0 0 0 0 1 3.43 0.0 NA
2037 0 0 0 0 0 0 3.40 0.0 NA
2038 6 1 0 0 5 0 3.00 24.0 NA
2039 5 1 5 1 5 0 2.43 34.3 NA
2040 0 0 0 0 1 1 3.50 12.5 NA
2041 0 0 0 0 2 0 3.40 28.6 NA
2042 5 0 0 0 3 2 3.00 12.0 NA
2043 0 0 0 0 0 0 4.00 0.0 NA
2044 12 3 3 0 4 1 2.84 13.5 NA
2045 3 2 2 0 0 0 3.11 11.4 NA
2046 4 1 3 0 0 0 2.65 17.4 NA
2047 0 0 0 0 0 0 3.63 0.0 NA
2048 5 0 1 0 0 0 3.04 4.4 NA
2049 1 1 0 0 0 0 3.60 5.0 NA
2050 0 0 1 0 0 0 3.84 4.0 NA
2051 1 1 1 0 0 0 3.58 7.7 NA
2052 0 0 0 0 0 0 3.71 0.0 NA
2053 0 0 0 0 1 0 4.00 16.7 NA
2054 0 0 0 0 0 0 3.43 0.0 NA
2055 0 0 0 0 0 0 3.27 0.0 NA
2056 1 0 0 0 1 0 3.29 6.7 NA
2057 0 0 0 0 3 0 4.00 23.1 NA
2058 1 0 1 0 3 2 3.29 13.8 NA
2059 0 0 0 0 0 0 3.62 0.0 NA
2060 0 0 0 0 0 1 4.00 0.0 NA
2061 1 0 0 0 3 0 3.10 23.1 NA
2062 8 4 1 0 11 0 3.00 25.8 NA
2063 0 0 0 0 0 0 4.00 0.0 NA
2064 0 0 0 0 0 0 4.00 0.0 NA
2065 9 4 4 0 8 0 1.92 47.1 NA
2066 11 5 3 0 7 1 1.92 44.1 NA
2067 18 4 1 0 1 0 2.19 18.2 NA
2068 11 3 2 0 10 0 2.08 44.1 NA
2069 9 2 3 1 4 0 2.17 35.7 NA
2070 12 4 2 0 4 0 1.76 40.0 NA
2071 7 2 0 0 0 0 2.50 10.0 NA
2072 2 1 1 0 0 0 2.30 20.0 NA
2073 6 4 4 0 6 0 2.17 40.0 NA
2074 10 1 3 0 5 0 2.34 26.5 NA
2075 9 5 2 0 2 0 2.18 30.0 NA
2076 10 4 1 0 2 0 2.41 20.6 NA
2077 3 6 4 0 10 0 1.68 69.0 NA
2078 1 0 0 0 0 0 3.38 0.0 NA
2079 4 5 3 0 2 0 2.44 29.4 NA
2080 15 3 4 0 2 0 1.97 27.3 NA
2081 16 4 0 0 6 0 2.15 30.3 NA
2082 6 0 2 0 2 0 2.70 11.4 NA
2083 7 6 6 0 5 0 1.70 53.1 NA
2084 4 5 1 0 6 0 2.26 48.0 NA
2085 4 0 1 0 1 1 2.56 18.2 NA
2086 13 3 2 0 3 0 2.34 22.9 NA
2087 13 0 1 1 7 0 2.44 26.5 NA
2088 6 0 0 0 5 0 3.08 17.2 NA
2089 4 0 1 0 3 0 2.14 40.0 NA
2090 8 1 1 0 5 0 2.56 23.3 NA
2091 0 1 0 0 0 0 3.42 8.3 NA
2092 12 0 1 0 1 1 2.83 5.4 NA
2093 4 0 2 0 5 0 2.53 35.0 NA
2094 5 0 0 0 3 1 2.88 14.3 NA
2095 4 8 0 1 2 0 2.29 42.3 NA
2096 2 0 0 0 0 0 2.88 0.0 NA
2097 0 0 1 0 0 0 3.79 5.3 NA
2098 7 0 2 0 2 0 2.81 13.8 NA
2099 5 1 2 0 0 0 3.15 6.5 NA
2100 3 1 3 0 0 1 3.31 8.7 NA
2101 9 3 3 0 2 1 3.43 6.7 NA
2102 6 0 0 0 5 1 3.31 11.1 NA
2103 0 0 0 0 3 2 3.75 23.1 NA
2104 1 0 3 0 5 0 2.91 29.6 NA
2105 5 4 1 0 1 0 2.35 28.6 NA
2106 2 0 0 0 0 0 3.45 0.0 NA
2107 7 0 0 0 0 0 2.87 0.0 NA
2108 6 2 0 0 1 0 2.70 14.3 NA
2109 10 1 2 0 6 0 2.55 24.3 NA
2110 7 3 3 0 4 0 2.56 27.8 NA
2111 4 0 0 0 0 0 3.24 0.0 NA
2112 0 0 0 0 3 0 3.36 12.0 NA
2113 1 0 0 0 4 0 3.81 16.0 NA
2114 0 0 0 0 0 0 3.73 0.0 NA
2115 0 0 0 0 0 0 3.69 0.0 NA
2116 5 1 0 1 5 0 3.04 24.1 NA
2117 0 0 0 0 0 2 3.80 0.0 NA
2118 0 0 0 0 3 1 4.00 23.1 NA
2119 4 0 2 0 1 0 2.80 11.5 NA
2120 0 0 0 0 0 0 3.68 0.0 NA
2121 1 0 4 0 2 0 3.08 22.2 NA
2122 2 1 1 0 1 0 3.20 11.5 NA
2123 3 0 0 0 1 0 3.05 4.6 NA
2124 2 1 0 0 0 0 3.09 4.4 NA
2125 0 0 0 1 1 0 3.42 8.0 NA
2126 1 2 1 0 1 0 3.17 16.0 NA
2127 4 0 0 0 0 0 3.25 0.0 NA
2128 1 0 1 0 1 0 3.50 7.4 NA
2129 3 0 0 0 0 0 3.36 0.0 NA
2130 2 0 0 0 0 0 3.30 0.0 NA
2131 2 0 2 0 1 0 3.00 12.5 NA
2132 2 0 0 0 1 0 3.30 3.6 NA
2133 0 2 2 0 0 0 2.95 18.2 NA
2134 1 2 0 0 0 0 3.21 8.3 NA
2135 1 1 0 0 0 0 3.15 5.0 NA
2136 2 0 0 0 1 0 3.73 4.4 NA
2137 2 1 2 0 0 0 3.30 13.0 NA
2138 2 0 1 0 1 0 3.31 7.4 NA
2139 0 0 0 0 4 0 3.55 15.4 NA
2140 1 0 0 0 1 0 3.39 4.2 NA
2141 2 1 3 0 0 0 2.81 19.1 NA
2142 0 0 0 0 0 0 3.78 0.0 NA
2143 0 0 0 0 0 1 4.00 0.0 NA
2144 0 0 0 0 0 0 3.96 0.0 NA
2145 0 0 0 0 0 0 3.63 0.0 NA
2146 18 2 0 0 0 0 2.74 4.4 NA
2147 1 2 1 0 4 0 3.06 17.5 NA
2148 17 1 2 1 2 1 2.54 14.3 NA
2149 0 0 0 0 1 0 3.68 5.0 NA
2150 3 0 0 0 0 1 3.06 0.0 NA
2151 2 0 0 0 0 0 3.11 0.0 NA
2152 2 0 0 0 2 0 3.37 5.0 NA
2153 5 0 0 0 0 0 3.37 0.0 NA
2154 0 0 0 0 2 0 3.08 7.7 NA
2155 0 0 1 0 3 0 3.77 13.8 NA
2156 3 0 0 0 4 0 3.22 18.2 NA
2157 8 2 0 0 5 0 2.47 29.2 NA
2158 0 0 0 0 0 0 3.93 0.0 NA
2159 0 0 0 0 0 1 3.70 0.0 NA
2160 0 0 0 0 1 0 3.73 8.3 NA
2161 0 0 0 0 1 0 3.78 5.3 NA
2162 0 0 0 0 1 0 4.00 5.3 NA
2163 0 0 0 0 1 0 3.74 5.0 NA
2164 0 0 0 0 0 1 4.00 0.0 NA
2165 0 0 0 0 1 0 3.90 4.8 NA
2166 0 0 0 0 2 0 3.60 11.8 NA
2167 0 0 0 0 0 0 3.77 0.0 NA
2168 0 0 0 0 1 0 3.73 3.2 NA
2169 0 0 0 0 0 0 4.00 0.0 NA
2170 1 0 1 0 0 0 2.88 6.3 NA
2171 11 4 6 0 16 0 2.24 48.2 NA
2172 3 0 0 0 1 0 3.23 3.7 NA
2173 6 2 1 0 8 0 2.75 30.6 NA
2174 0 0 0 0 2 0 4.00 15.4 NA
2175 5 0 0 0 3 0 3.33 7.7 NA
2176 0 0 1 0 0 0 3.50 7.1 NA
2177 0 0 0 0 2 0 3.58 14.3 NA
2178 7 1 2 0 6 0 2.00 45.0 NA
2179 5 4 1 0 1 0 2.07 40.0 NA
2180 5 0 0 0 2 0 2.43 22.2 NA
2181 3 1 0 0 2 0 2.94 15.8 NA
2182 4 0 1 0 5 0 3.24 14.0 NA
2183 0 0 0 0 1 0 3.86 12.5 NA
2184 1 2 2 0 1 0 2.38 35.7 NA
2185 0 0 0 0 2 15 NA 11.8 NA
2186 5 2 2 0 8 0 2.67 37.5 NA
2187 0 0 0 0 0 0 3.90 0.0 NA
2188 2 1 0 0 2 2 3.11 13.6 NA
2189 0 0 0 0 0 6 NA 0.0 NA
2190 0 0 0 1 0 0 3.50 12.5 NA
2191 0 0 0 0 0 0 4.00 0.0 NA
2192 7 1 3 1 4 1 2.78 20.0 NA
2193 29 10 11 0 9 0 2.33 25.9 NA
2194 2 0 0 0 2 1 3.74 7.7 NA
2195 0 1 0 0 0 0 3.52 4.4 NA
2196 0 0 0 0 0 0 3.58 0.0 NA
2197 0 0 0 0 0 0 3.73 0.0 NA
2198 6 0 0 0 2 0 2.89 10.0 NA
2199 3 0 1 0 1 0 2.86 13.3 NA
2200 0 0 1 0 4 0 3.25 20.8 NA
2201 11 1 1 0 3 0 2.80 11.6 NA
2202 19 2 5 0 5 0 2.98 10.2 NA
2203 0 0 0 0 0 11 NA 0.0 NA
2204 0 0 0 0 0 12 NA 0.0 NA
2205 1 1 1 0 0 0 2.00 33.3 NA
2206 3 2 0 0 7 0 3.00 30.0 NA
2207 0 0 0 0 3 9 NA 25.0 NA
2208 3 0 0 1 0 0 2.73 9.1 NA
2209 12 2 2 0 4 0 2.82 13.3 NA
2210 0 0 0 0 3 0 3.60 13.0 NA
2211 1 0 0 0 0 0 3.50 0.0 NA
2212 0 0 0 0 0 3 3.95 0.0 NA
2213 0 0 0 0 3 1 3.44 23.1 NA
2214 0 0 0 0 1 0 3.71 12.5 NA
2215 0 0 0 0 0 0 4.00 0.0 NA
2216 0 0 1 0 0 1 3.12 3.9 NA
2217 0 0 0 0 0 1 4.00 0.0 NA
2218 0 0 1 0 2 0 3.44 27.3 NA
2219 0 0 0 0 0 3 4.00 0.0 NA
2220 1 0 0 0 1 0 3.24 2.4 NA
2221 1 0 0 0 0 0 3.55 0.0 NA
2222 1 0 2 0 0 0 2.00 33.3 NA
2223 0 0 1 0 0 0 3.59 5.9 NA
2224 1 0 0 0 1 0 3.40 16.7 NA
2225 0 0 0 0 0 31 NA 0.0 NA
2226 2 0 0 0 2 0 3.50 9.1 NA
2227 1 1 1 0 2 1 2.89 19.1 NA
2228 0 0 0 0 2 0 3.65 8.0 NA
2229 1 0 1 1 2 1 3.49 8.7 NA
2230 0 0 0 0 0 0 3.89 0.0 NA
2231 1 0 0 0 0 0 3.73 0.0 NA
2232 5 1 1 0 2 0 3.00 14.8 NA
2233 0 0 0 0 0 0 3.89 0.0 NA
2234 3 0 1 1 1 2 2.83 14.3 NA
2235 0 0 0 0 1 0 4.00 7.7 NA
2236 4 0 4 1 0 0 2.00 31.3 NA
2237 0 0 0 0 2 0 4.00 8.7 NA
2238 0 0 0 0 0 0 4.00 0.0 NA
2239 5 5 4 1 17 0 3.30 23.5 NA
2240 3 4 6 1 10 0 2.87 37.5 NA
2241 12 3 2 0 3 0 2.89 14.0 NA
2242 2 0 1 0 1 0 3.00 22.2 NA
2243 0 0 0 0 0 0 3.89 0.0 NA
2244 0 0 0 0 0 0 4.00 0.0 NA
2245 82 20 5 0 9 0 2.33 19.1 NA
2246 2 0 0 0 2 0 3.33 6.3 NA
2247 1 0 0 0 5 0 3.64 16.7 NA
2248 1 0 0 0 0 0 3.80 0.0 NA
2249 13 1 2 0 7 0 2.21 32.3 NA
2250 1 0 0 0 3 0 3.41 15.0 NA
2251 33 17 9 0 5 0 2.37 25.8 NA
2252 24 7 0 0 7 0 2.71 19.2 NA
2253 7 1 3 0 4 0 2.39 29.6 NA
2254 0 0 0 0 1 0 3.87 6.3 NA
2255 38 14 10 0 3 0 1.99 30.3 NA
2256 6 0 4 0 7 0 2.78 28.2 NA
2257 37 4 2 1 1 0 2.62 7.0 NA
2258 14 1 0 0 9 0 2.44 29.4 NA
2259 31 11 9 0 15 0 2.37 29.2 NA
2260 10 3 7 0 14 1 2.83 26.4 NA
2261 10 2 1 0 3 1 2.89 12.5 NA
2262 8 1 2 0 4 0 2.93 15.6 NA
2263 14 0 1 0 7 0 2.52 23.5 NA
2264 29 11 9 2 5 0 2.42 23.5 NA
2265 12 1 0 0 0 0 2.65 2.9 NA
2266 13 0 3 0 12 0 3.16 12.3 NA
2267 0 0 0 0 0 0 4.00 0.0 NA
2268 9 0 0 0 3 0 3.13 7.3 NA
2269 13 0 0 0 0 0 2.80 0.0 NA
2270 6 0 0 0 0 1 3.08 0.0 NA
2271 0 0 0 0 0 0 3.10 0.0 NA
2272 0 0 0 0 0 1 3.25 0.0 NA
2273 0 0 0 0 0 76 NA 0.0 NA
2274 0 0 0 0 0 0 3.13 0.0 NA
2275 0 0 0 0 0 0 3.95 0.0 NA
2276 0 0 0 0 1 0 3.93 3.6 NA
2277 0 0 0 0 2 0 3.93 11.8 NA
2278 0 0 0 0 1 51 NA 1.9 NA
2279 1 0 0 0 0 0 3.25 0.0 NA
2280 1 0 0 0 0 0 3.80 0.0 NA
2281 3 0 1 0 7 0 2.80 47.1 NA
2282 0 0 1 0 1 0 3.20 33.3 NA
2283 0 1 2 0 0 0 2.93 20.0 NA
2284 0 0 0 0 1 2 3.69 3.5 NA
2285 0 0 0 0 0 0 4.00 0.0 NA
2286 0 0 0 0 0 0 3.81 0.0 NA
2287 1 0 0 0 0 0 3.40 0.0 NA
2288 0 0 0 0 0 0 3.92 0.0 NA
2289 0 0 0 0 0 0 3.91 0.0 NA
2290 0 0 0 0 0 0 3.91 0.0 NA
2291 1 1 1 0 2 0 3.43 13.3 NA
2292 0 0 0 0 0 0 4.00 0.0 NA
2293 0 0 0 0 0 0 3.29 0.0 NA
2294 0 0 0 0 0 0 3.09 0.0 NA
2295 0 0 0 0 0 14 NA 0.0 NA
2296 0 0 0 0 0 8 NA 0.0 NA
2297 0 0 0 0 1 3 4.00 10.0 NA
2298 0 0 0 0 0 0 3.91 0.0 NA
2299 0 0 0 0 0 0 4.00 0.0 NA
2300 0 0 0 0 0 0 3.83 0.0 NA
2301 1 0 0 0 0 0 3.40 0.0 NA
2302 13 3 2 0 0 0 2.25 17.9 NA
2303 3 1 0 0 0 0 3.07 3.6 NA
2304 1 1 0 0 2 0 3.76 7.7 NA
2305 8 1 2 0 6 0 2.66 23.7 NA
2306 1 0 1 0 2 0 2.83 37.5 NA
2307 0 0 1 0 0 0 3.57 3.3 NA
2308 11 4 3 0 6 0 2.38 28.3 NA
2309 0 0 0 0 0 6 NA 0.0 NA
2310 0 0 0 0 0 6 NA 0.0 NA
2311 4 1 3 0 0 0 2.68 16.0 NA
2312 0 0 0 0 1 0 3.40 16.7 NA
2313 19 0 3 1 3 0 2.17 18.0 NA
2314 0 0 0 0 0 7 NA 0.0 NA
2315 0 0 0 0 0 8 NA 0.0 NA
2316 0 0 0 0 0 7 NA 0.0 NA
2317 0 0 0 0 0 7 NA 0.0 NA
2318 0 0 0 0 0 10 NA 0.0 NA
2319 0 0 0 0 0 31 NA 0.0 NA
2320 0 0 0 0 1 12 NA 7.7 NA
2321 0 0 0 0 0 8 NA 0.0 NA
2322 0 0 0 0 0 8 NA 0.0 NA
2323 0 0 0 0 3 0 3.60 37.5 NA
2324 0 0 0 0 3 0 4.00 37.5 NA
2325 7 0 1 0 4 0 2.65 20.8 NA
2326 1 0 0 0 0 0 3.85 0.0 NA
2327 0 0 0 0 0 0 3.50 0.0 NA
2328 1 0 0 1 7 4 3.37 17.4 NA
2329 0 0 0 0 0 2 3.78 0.0 NA
2330 16 3 3 0 2 0 2.30 19.1 NA
2331 3 0 0 0 4 1 3.25 13.8 NA
2332 5 1 0 0 5 0 3.14 22.2 NA
2333 14 5 8 0 14 0 2.80 25.2 NA
2334 4 2 3 1 0 0 2.67 13.3 NA
2335 0 1 0 0 1 1 3.64 15.4 NA
2336 0 0 0 0 0 0 4.00 0.0 NA
2337 5 0 0 0 0 0 3.20 0.0 NA
2338 1 0 0 0 0 0 3.70 0.0 NA
2339 0 0 0 0 0 1 4.00 0.0 NA
2340 0 0 0 0 0 8 NA 0.0 NA
2341 3 0 1 0 1 0 3.36 8.7 NA
2342 2 1 1 0 2 0 2.80 23.5 NA
2343 7 1 2 0 5 0 2.82 24.2 NA
2344 9 0 0 0 0 0 3.19 0.0 NA
2345 0 0 0 0 1 0 3.86 12.5 NA
2346 1 2 1 0 2 0 3.17 19.2 NA
2347 6 1 2 0 1 0 2.74 16.7 NA
2348 1 0 1 0 0 0 3.75 3.6 NA
2349 11 2 0 0 2 0 2.61 13.3 NA
2350 8 1 3 0 1 1 2.35 20.0 NA
2351 0 0 1 0 3 0 3.17 26.7 NA
2352 3 1 0 0 0 0 3.38 4.2 NA
2353 9 0 2 0 3 0 2.74 13.5 NA
2354 4 1 1 0 1 0 3.24 7.1 NA
2355 0 0 0 0 0 0 4.00 0.0 NA
2356 2 0 0 0 3 1 3.00 25.0 NA
2357 9 0 1 0 3 0 2.98 7.8 NA
2358 1 0 0 0 1 0 3.24 5.6 NA
2359 2 0 0 0 2 0 3.24 10.5 NA
2360 2 3 1 0 5 0 2.71 40.9 NA
2361 0 0 0 0 0 14 NA 0.0 NA
2362 0 0 0 0 1 0 4.00 14.3 NA
2363 0 0 1 0 0 0 3.00 16.7 NA
2364 0 0 1 0 0 0 3.28 5.6 NA
2365 8 1 2 0 3 0 2.54 20.7 NA
2366 0 0 0 0 2 2 3.72 6.9 NA
2367 6 2 8 0 5 0 1.26 62.5 NA
2368 9 8 1 0 3 0 2.48 27.9 NA
2369 8 1 2 0 6 0 2.98 19.6 NA
2370 8 2 2 1 11 1 2.56 36.4 NA
2371 1 0 3 0 1 0 3.17 16.0 NA
2372 4 1 1 0 0 0 3.09 8.7 NA
2373 0 0 3 1 2 1 2.87 23.1 NA
2374 0 0 0 0 1 0 3.68 4.4 NA
2375 0 0 0 0 0 13 NA 0.0 NA
2376 1 0 0 0 3 0 3.69 8.6 NA
2377 1 0 0 1 0 0 3.44 3.7 NA
2378 0 0 0 0 0 0 3.75 0.0 NA
2379 0 0 0 0 0 12 NA 0.0 NA
2380 4 0 0 0 1 0 3.00 6.7 NA
2381 0 0 0 0 0 0 3.55 0.0 NA
2382 2 1 0 0 1 0 3.29 11.1 NA
2383 0 0 0 0 1 1 3.78 9.1 NA
2384 0 0 0 0 5 0 4.00 15.6 NA
2385 2 0 0 0 4 0 3.60 21.1 NA
2386 1 0 1 1 3 0 3.74 10.9 NA
2387 0 0 0 0 0 0 3.86 0.0 NA
2388 2 1 2 0 2 0 3.10 22.7 NA
2389 0 0 0 0 0 0 3.82 0.0 NA
2390 2 0 1 0 3 1 3.20 16.7 NA
2391 4 0 0 0 3 1 3.13 11.1 NA
2392 6 2 2 0 1 0 2.57 22.7 NA
2393 1 0 1 0 3 0 3.41 13.3 NA
2394 2 0 0 0 0 0 3.18 0.0 NA
2395 1 0 0 1 0 1 3.36 4.4 NA
2396 3 0 0 0 1 0 3.57 3.5 NA
2397 0 0 0 0 0 0 3.78 0.0 NA
2398 0 1 1 0 8 0 3.92 8.7 NA
2399 0 0 0 0 1 0 4.00 8.3 NA
2400 0 0 0 0 0 15 NA 0.0 NA
2401 3 2 0 0 1 0 3.14 13.6 NA
2402 3 4 0 0 2 0 2.85 27.3 NA
2403 4 4 2 0 1 0 2.30 33.3 NA
2404 6 5 4 0 4 0 1.75 54.2 NA
2405 28 8 9 0 21 2 2.57 31.9 NA
2406 0 0 0 0 2 0 3.95 8.3 NA
2407 3 0 1 0 0 1 2.85 7.1 NA
2408 2 0 0 0 3 0 3.20 16.7 NA
2409 2 0 0 2 1 3 2.86 12.0 NA
2410 0 0 1 0 3 0 3.71 8.9 NA
2411 4 3 5 0 1 0 2.90 21.4 NA
2412 17 3 1 0 4 0 2.67 17.4 NA
2413 1 0 1 1 0 0 3.11 10.5 NA
2414 0 0 0 0 2 1 3.08 13.3 NA
2415 1 0 0 0 1 0 3.40 16.7 NA
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3110 0 0 0 0 1 0 3.64 6.7 NA
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3355 1 2 1 0 1 0 3.43 16.7 NA
3356 2 0 1 0 3 0 3.48 14.3 NA
3357 0 0 0 0 0 8 NA 0.0 NA
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3359 1 0 0 0 2 1 3.60 11.1 NA
3360 1 0 0 0 2 0 3.83 10.0 NA
3361 0 0 0 0 0 0 3.78 0.0 NA
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3365 0 0 0 0 1 31 NA 3.1 NA
3366 1 0 0 0 1 2 3.52 3.1 NA
3367 0 0 0 0 2 0 3.89 18.2 NA
3368 0 0 0 0 0 0 3.29 0.0 NA
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3375 1 0 0 0 0 0 3.20 0.0 NA
3376 0 0 0 0 1 0 3.80 6.3 NA
3377 1 0 0 0 2 1 3.40 11.1 NA
3378 0 0 1 0 7 0 2.83 61.5 NA
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3873 1 0 2 0 1 0 3.21 12.0 NA
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3881 0 0 0 0 3 0 3.67 14.3 NA
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3886 1 0 4 1 6 0 2.90 31.4 NA
3887 1 0 0 0 3 0 3.54 9.7 NA
3888 0 1 0 0 2 0 3.84 8.8 NA
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3890 0 0 1 0 4 0 3.74 14.3 NA
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3893 3 2 3 0 3 1 3.00 22.9 NA
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3898 9 3 1 0 5 0 2.48 28.1 NA
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3900 2 0 0 0 4 0 3.35 16.7 NA
3901 0 2 3 0 0 0 2.95 26.3 NA
3902 2 0 0 0 1 1 3.40 8.3 NA
3903 2 0 1 0 4 0 3.35 14.3 NA
3904 1 0 0 0 2 0 3.33 18.2 NA
3905 0 0 0 0 1 0 3.64 6.7 NA
3906 0 0 2 0 1 0 3.38 17.7 NA
3907 0 0 0 0 1 0 3.80 16.7 NA
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3909 2 0 0 0 1 0 3.26 5.0 NA
3910 2 2 2 0 1 0 2.89 25.0 NA
3911 2 0 0 0 1 0 3.61 5.3 NA
3912 1 1 0 0 4 0 3.29 27.8 NA
3913 0 0 0 0 0 10 NA 0.0 NA
3914 0 0 0 0 0 0 3.71 0.0 NA
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3916 0 0 0 0 0 1 4.00 0.0 NA
3917 0 0 0 0 0 94 NA 0.0 NA
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3920 5 1 0 0 2 0 3.03 7.3 NA
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3922 5 1 0 0 2 0 3.29 7.5 NA
3923 25 4 9 0 12 0 2.64 21.0 NA
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3925 19 8 4 0 11 1 2.94 19.5 NA
3926 28 9 5 0 14 0 2.75 23.3 NA
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3929 10 5 4 0 6 1 3.15 12.7 NA
3930 0 0 0 0 1 0 3.57 6.7 NA
3931 1 0 2 0 3 0 2.81 26.3 NA
3932 3 0 0 0 1 0 2.93 6.3 NA
3933 3 1 1 0 3 0 2.64 35.7 NA
3934 1 0 0 0 2 0 3.15 9.1 NA
3935 8 1 2 0 2 0 2.81 14.7 NA
3936 7 0 1 0 3 0 2.81 13.8 NA
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3938 2 1 0 0 4 0 3.23 29.4 NA
3939 2 0 0 0 2 0 3.38 13.3 NA
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3941 7 2 3 0 4 0 2.53 26.5 NA
3942 5 1 3 0 9 1 2.50 38.2 NA
3943 6 1 2 0 5 0 2.60 26.7 NA
3944 3 0 0 0 3 0 2.90 23.1 NA
3945 7 0 1 0 7 0 2.81 24.2 NA
3946 2 2 0 0 5 0 2.94 33.3 NA
3947 3 0 2 0 3 0 3.03 14.7 NA
3948 6 0 0 0 3 0 3.00 15.8 NA
3949 6 3 1 0 2 0 2.29 31.6 NA
3950 4 0 0 0 2 0 3.00 16.7 NA
3951 1 1 2 0 4 1 3.11 29.2 NA
3952 4 0 0 0 4 1 3.08 23.5 NA
3953 2 2 0 0 4 1 3.07 30.0 NA
3954 11 3 0 0 3 0 2.96 10.5 NA
3955 0 0 0 0 2 0 3.48 3.3 NA
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3957 9 2 0 0 0 0 2.65 8.7 NA
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3959 2 0 0 0 1 0 3.22 4.2 NA
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3962 2 2 0 0 1 0 3.14 13.0 NA
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3967 5 1 1 0 0 0 3.04 7.1 NA
3968 5 1 1 1 0 0 2.79 10.7 NA
3969 3 0 2 0 1 0 2.95 13.0 NA
3970 6 0 1 0 0 0 3.04 4.4 NA
3971 5 1 0 0 1 0 3.19 7.4 NA
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3973 5 5 1 0 0 0 2.64 24.0 NA
3974 4 1 0 0 3 1 2.88 20.0 NA
3975 0 1 0 0 2 0 3.20 25.0 NA
3976 1 0 0 0 2 0 3.55 15.4 NA
3977 0 0 0 0 0 1 3.71 0.0 NA
3978 0 0 0 0 1 0 3.91 8.3 NA
3979 0 0 0 0 0 9 NA 0.0 NA
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3982 8 1 3 0 4 0 2.41 30.8 NA
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3984 10 3 4 1 1 0 1.84 34.6 NA
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3988 9 0 0 0 2 0 2.53 10.5 NA
3989 12 0 1 0 1 0 2.19 9.1 NA
3990 17 0 0 1 2 0 2.24 11.1 NA
3991 7 3 1 0 3 0 3.05 15.9 NA
3992 4 1 0 1 1 0 3.06 15.8 NA
3993 0 0 0 0 0 1 3.63 0.0 NA
3994 0 0 0 0 0 0 4.00 0.0 NA
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3997 3 0 0 0 2 0 2.94 10.5 NA
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3999 3 0 0 0 1 1 2.86 6.3 NA
4000 4 1 2 0 8 0 2.53 44.0 NA
4001 9 2 3 0 2 0 2.38 25.0 NA
4002 1 0 2 0 6 0 2.91 47.1 NA
4003 7 6 0 1 2 1 2.39 26.5 NA
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4005 0 0 0 0 0 0 3.80 0.0 NA
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4007 0 0 0 0 0 0 3.83 0.0 NA
4008 1 0 2 0 0 1 3.26 8.3 NA
4009 1 0 1 0 7 0 3.67 32.0 NA
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4012 1 2 2 0 1 0 2.88 20.0 NA
4013 5 0 4 0 0 0 2.67 16.7 NA
4014 0 0 3 0 3 0 2.95 26.1 NA
4015 5 0 0 0 0 0 3.04 0.0 NA
4016 0 0 0 0 9 0 4.00 37.5 NA
4017 0 0 0 0 3 0 4.00 12.0 NA
4018 0 1 1 0 0 1 3.70 8.3 NA
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4020 0 0 0 0 0 0 4.00 0.0 NA
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4022 2 1 0 0 0 0 3.58 4.2 NA
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4024 0 0 0 0 0 0 4.00 0.0 NA
4025 1 0 0 0 1 0 3.83 7.7 NA
4026 0 0 0 0 4 0 3.91 8.5 NA
4027 2 0 0 0 1 2 3.64 2.1 NA
4028 1 0 0 0 1 2 3.67 1.7 NA
4029 1 0 0 0 3 0 3.71 12.5 NA
4030 0 0 0 0 1 0 3.61 4.2 NA
4031 0 0 0 0 0 0 4.00 0.0 NA
4032 0 0 0 0 0 11 NA 0.0 NA
4033 0 0 0 0 0 0 3.96 0.0 NA
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4035 9 4 4 0 5 0 2.46 31.0 NA
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4037 16 0 2 0 2 0 2.66 8.7 NA
4038 6 1 0 0 5 0 3.21 13.6 NA
4039 8 0 2 0 3 1 3.07 11.1 NA
4040 12 1 5 0 11 0 2.15 37.8 NA
4041 10 1 2 0 2 0 2.83 11.6 NA
4042 10 3 0 1 15 0 2.30 45.2 NA
4043 9 2 2 0 3 0 2.80 15.9 NA
4044 8 0 0 2 14 0 2.56 41.0 NA
4045 8 2 6 1 4 2 2.09 33.3 NA
4046 2 2 0 0 0 0 3.07 13.3 NA
4047 8 0 4 0 4 0 2.58 22.9 NA
4048 10 1 4 0 3 0 2.74 19.1 NA
4049 7 2 5 0 1 0 2.71 17.4 NA
4050 11 5 1 1 5 0 2.59 26.1 NA
4051 10 4 1 0 14 0 2.59 41.3 NA
4052 7 0 1 0 12 0 3.00 28.9 NA
4053 7 1 1 0 5 1 3.00 17.5 NA
4054 6 0 1 0 0 1 2.91 4.2 NA
4055 5 1 1 0 1 2 3.24 7.3 NA
4056 3 0 3 0 7 0 3.13 25.6 NA
4057 8 2 3 0 4 1 2.62 21.4 NA
4058 10 7 3 2 4 1 2.03 37.2 NA
4059 8 6 4 0 2 0 2.26 33.3 NA
4060 6 8 1 0 6 0 2.69 36.6 NA
4061 12 4 7 0 5 0 2.05 34.8 NA
4062 2 0 0 0 0 0 3.13 0.0 NA
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4064 15 4 1 0 2 0 2.60 15.6 NA
4065 5 4 3 0 1 1 2.58 22.9 NA
4066 15 4 1 0 3 1 2.35 18.2 NA
4067 7 1 3 0 2 0 2.84 13.3 NA
4068 6 0 2 0 8 0 3.03 24.4 NA
4069 11 2 2 0 2 0 2.77 13.0 NA
4070 3 3 1 0 2 2 2.71 28.6 NA
4071 3 1 0 0 2 0 3.11 14.3 NA
4072 0 0 0 0 8 1 3.77 18.2 NA
4073 12 4 6 0 2 0 2.34 26.1 NA
4074 6 5 3 0 3 0 2.39 32.4 NA
4075 5 0 0 0 0 0 2.95 0.0 NA
4076 18 1 1 0 5 0 2.48 15.6 NA
4077 9 10 4 0 4 0 2.10 39.1 NA
4078 2 0 0 0 2 0 3.26 4.4 NA
4079 9 2 7 1 2 0 2.45 26.1 NA
4080 8 3 5 0 4 0 2.59 26.7 NA
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4082 7 0 3 2 6 0 2.66 25.0 NA
4083 16 1 4 0 4 2 2.25 19.6 NA
4084 14 1 0 0 4 0 2.83 11.4 NA
4085 3 4 0 0 2 1 2.92 21.4 NA
4086 9 1 4 0 5 0 2.13 35.7 NA
4087 4 0 1 0 1 0 3.19 6.3 NA
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4098 5 0 4 0 11 0 2.33 51.7 NA
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4100 6 2 0 0 8 0 2.36 52.6 NA
4101 5 2 2 0 8 1 2.47 42.9 NA
4102 26 2 5 0 17 1 2.83 20.3 NA
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4110 1 0 1 0 0 0 3.06 6.3 NA
4111 2 0 0 0 1 0 3.06 5.6 NA
4112 4 0 0 0 1 0 3.11 5.3 NA
4113 1 0 0 0 1 0 3.13 5.9 NA
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4149 5 1 7 0 4 0 2.81 26.1 NA
4150 13 4 6 0 10 0 2.16 42.6 NA
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4154 5 2 0 0 3 0 3.28 10.9 NA
4155 7 1 2 0 6 0 2.93 19.6 NA
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4157 6 0 0 0 6 1 3.35 12.8 NA
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4159 7 3 4 0 6 0 2.78 30.2 NA
4160 3 2 2 0 2 0 3.00 17.7 NA
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4164 12 8 6 1 13 1 1.34 65.1 NA
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4170 18 6 12 1 4 0 1.63 46.0 NA
4171 12 8 3 0 9 0 2.14 45.5 NA
4172 9 3 10 1 21 0 1.30 72.9 NA
4173 9 0 3 0 7 1 3.05 21.3 NA
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4175 20 6 2 0 1 0 2.47 18.0 NA
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4185 3 0 1 0 3 1 2.82 26.7 NA
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4187 1 0 2 0 2 0 2.92 28.6 NA
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4200 5 1 1 1 2 0 2.00 38.5 NA
4201 5 5 4 0 9 0 2.12 52.9 NA
4202 4 1 1 0 2 0 2.62 26.7 NA
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4205 12 8 7 0 15 0 1.48 65.2 NA
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4209 6 2 7 0 18 1 2.04 61.4 NA
4210 9 9 9 0 14 0 1.57 65.3 NA
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4212 2 1 2 1 5 0 1.67 64.3 NA
4213 11 3 9 1 13 0 1.72 57.8 NA
4214 7 4 2 0 9 0 2.50 40.5 NA
4215 11 1 10 0 12 0 1.94 48.9 NA
4216 6 1 1 0 4 0 3.10 17.1 NA
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4218 6 0 0 0 6 0 2.62 31.6 NA
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4220 9 2 2 0 5 0 2.84 25.0 NA
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4265 0 0 1 0 1 0 3.64 16.7 NA
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4299 0 0 1 0 1 0 3.18 16.7 NA
4300 0 0 1 0 0 0 3.00 16.7 NA
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4309 0 0 0 0 0 0 4.00 0.0 NA
4310 0 0 0 0 1 0 4.00 14.3 NA
4311 0 0 0 0 0 0 4.00 0.0 NA
4312 0 0 0 0 1 0 3.86 12.5 NA
4313 0 0 0 0 0 0 4.00 0.0 NA
4314 0 0 0 0 0 0 3.86 0.0 NA
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4316 1 0 1 0 2 0 3.68 11.1 NA
4317 0 0 0 0 0 37 NA 0.0 NA
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4319 3 0 0 0 3 1 3.72 4.8 NA
4320 12 1 3 0 0 2 2.94 6.1 NA
4321 7 3 0 0 1 0 2.84 10.3 NA
4322 10 0 0 0 0 0 2.88 0.0 NA
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4324 1 0 0 0 0 0 3.81 0.0 NA
4325 0 0 0 0 0 31 NA 0.0 NA
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4329 1 0 0 0 1 0 3.40 16.7 NA
4330 1 0 1 0 0 0 3.23 3.9 NA
4331 0 0 0 0 0 0 3.89 0.0 NA
4332 0 0 1 0 0 1 3.80 4.8 NA
4333 0 0 0 0 0 0 3.88 0.0 NA
4334 0 0 0 0 0 7 NA 0.0 NA
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4336 14 1 0 1 5 0 3.23 5.8 NA
4337 13 6 1 1 5 0 2.21 33.3 NA
4338 9 3 0 0 2 0 2.98 10.6 NA
4339 1 0 0 0 0 0 3.82 0.0 NA
4340 1 0 0 0 1 0 3.74 3.1 NA
4341 0 0 0 0 1 0 3.83 3.3 NA
4342 0 0 0 0 1 0 3.44 3.6 NA
4343 0 0 0 0 1 0 4.00 3.6 NA
4344 1 1 2 0 6 0 3.35 13.6 NA
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created_at section_id
1 2026-05-09 21:59:31 8f0131ed-e968-4e14-925e-660edf44f405
2 2026-05-09 21:59:31 60b4e609-9187-4fbe-8d3c-e906da6219e2
3 2026-05-09 21:59:31 b168177b-58ea-4412-abbb-378593ff3f5b
4 2026-05-09 21:59:31 ab2a7e0a-1909-4b41-b486-6a37af3f1f91
5 2026-05-09 21:59:31 4e25fe05-ce97-4678-a1d2-f8bbf5a6ef03
6 2026-05-09 21:59:31 5927c06e-91f5-49cf-a4ec-163627824af5
7 2026-05-09 21:59:31 467c8f78-e29a-4586-be98-52d3af872256
8 2026-05-09 21:59:31 d7261ba4-98b7-4792-a398-76358913a1c1
9 2026-05-09 21:59:31 2b3636b7-f5aa-46f3-9894-344412813fbe
10 2026-05-09 21:59:31 1c1390fe-7a0a-428d-b63a-20d83ba8efb1
11 2026-05-09 21:59:31 06d72fea-87dd-45ac-bc54-22d58102b295
12 2026-05-09 21:59:31 54087860-5c7e-4598-89d6-995f2dde70f6
13 2026-05-09 21:59:31 9244a74e-5c09-495c-a2dd-c77e8fc115fc
14 2026-05-09 21:59:31 07448bee-34e3-44a0-a337-fd85a4704771
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1915 2026-05-09 21:59:31 a71a122a-f3d9-49fd-8237-96fdfc75b50e
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1918 2026-05-09 21:59:31 c647b831-6efe-4d61-881c-b332e4c04b69
1919 2026-05-09 21:59:31 4c58b5e0-219e-440a-9897-03ab05895fa9
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1921 2026-05-09 21:59:31 e5d52fc7-fa5e-4bff-af88-022586d413ea
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1923 2026-05-09 21:59:31 edf78888-e5cd-4bbc-806e-982b61d65f49
1924 2026-05-09 21:59:31 eea8b44b-df69-418d-973d-56760882b3fc
1925 2026-05-09 21:59:31 6dddd83f-a053-446a-99f3-0d4abd4d1e51
1926 2026-05-09 21:59:31 76f0a6da-842b-4be8-bf5b-292d1e037b15
1927 2026-05-09 21:59:31 62b976c7-7804-4be9-a66f-a69a301a437e
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1929 2026-05-09 21:59:31 32de3364-f090-4efa-9304-66ebcdfe00d2
1930 2026-05-09 21:59:31 8f86a44e-a378-4478-adae-85e9dec83443
1931 2026-05-09 21:59:31 b391f583-3684-4dc0-b1c6-4626355a27f7
1932 2026-05-09 21:59:31 acc9682a-897c-4d6e-bf39-7712b50243b3
1933 2026-05-09 21:59:31 ed98dda1-1de2-4680-b6f2-96cdcee07e2c
1934 2026-05-09 21:59:31 a03610c2-22c4-4c47-9cfa-097c8f9f09c9
1935 2026-05-09 21:59:31 cf1de566-141f-4854-aef7-c090a700d543
1936 2026-05-09 21:59:31 a62f51fe-8043-499f-9967-ff7fb3f750d3
1937 2026-05-09 21:59:31 a514f989-07c7-4af6-986b-980a8c055d7d
1938 2026-05-09 21:59:31 e70cab97-95ef-406e-9da5-60efd595bb26
1939 2026-05-09 21:59:31 3e5bf836-e3c2-4289-8771-9a1e19ec8603
1940 2026-05-09 21:59:31 3ff8fa2c-d9ab-48fb-9ff1-bebacb9696dd
1941 2026-05-09 21:59:31 404bbb90-23f0-4930-ad9a-febb6c5a0a7f
1942 2026-05-09 21:59:31 ae3fe23c-408d-496c-be8c-f79509b70bd9
1943 2026-05-09 21:59:31 462bd128-d813-4262-a4d9-a34c25f084f6
1944 2026-05-09 21:59:31 402b7eb0-b66d-4579-9628-a17f8f21726a
1945 2026-05-09 21:59:31 4f08d644-224c-4df2-87e3-97c4f35a599c
1946 2026-05-09 21:59:31 9fd5c9a7-9784-4dbb-8cba-13fae505136c
1947 2026-05-09 21:59:31 95352748-270b-4e68-93c5-46c2179622f1
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1950 2026-05-09 21:59:31 442e22ff-a6d0-49c3-b4c4-d2b0f06a2292
1951 2026-05-09 21:59:31 6e78b50a-1e70-4326-8e3e-dba41daa4435
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1953 2026-05-09 21:59:31 f2c23b8e-c756-4dd5-845d-fc7ac5306d5c
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1955 2026-05-09 21:59:31 8ba8b186-525f-4b92-81b7-2ca427749174
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1958 2026-05-09 21:59:31 d12e68ed-aee6-436b-9d33-ed76d6cf5c55
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1962 2026-05-09 21:59:31 3590b312-cf3c-492a-9914-eecac7b5cbc8
1963 2026-05-09 21:59:31 bbe4e5f7-7e63-4e79-b147-ed0a3b85043e
1964 2026-05-09 21:59:31 b0d24a0e-24ed-4054-b7ed-0b6651ad4c2a
1965 2026-05-09 21:59:31 d79e77d0-a523-4bdc-9cd1-232aa102bb86
1966 2026-05-09 21:59:31 8b996710-b0a3-4e04-a425-21bcef8558be
1967 2026-05-09 21:59:31 5f4c5afc-9a5d-4629-a2dc-411bc44a5543
1968 2026-05-09 21:59:31 6be80e04-b3e6-4374-84fd-778bc17ab6ac
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1970 2026-05-09 21:59:31 3f1398f0-80b4-413b-9126-a5ccf3ddfeaa
1971 2026-05-09 21:59:31 fed9297b-5a18-4f8a-9890-a81fa65fb2e1
1972 2026-05-09 21:59:31 a7cb89d5-cd2c-4c69-b798-8a92d8e76d5c
1973 2026-05-09 21:59:31 f0aa1d0c-2b42-463e-8bd3-aeb01e76aaba
1974 2026-05-09 21:59:31 46b83ab1-c241-4875-96f8-df08bf1aae02
1975 2026-05-09 21:59:31 bcdaf6dc-814b-4aff-a47c-e3882abb77ed
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1977 2026-05-09 21:59:31 74a95bde-1946-4f88-b3a9-2547cde97b56
1978 2026-05-09 21:59:31 82ef3b05-1cac-43fa-9e0a-cb3067de99d9
1979 2026-05-09 21:59:31 76c51860-68bc-4e9f-9271-f03c4a935aa1
1980 2026-05-09 21:59:31 4e98b366-6332-4148-986f-74cbc87fc957
1981 2026-05-09 21:59:31 93943bab-3426-4997-90d3-eda7f000388e
1982 2026-05-09 21:59:31 7d9102cd-8fe1-4475-9f6b-183398bef8a9
1983 2026-05-09 21:59:31 db55635e-1262-4304-a784-41f397909670
1984 2026-05-09 21:59:31 64ea9f92-6904-4413-91a3-4d8b918f744a
1985 2026-05-09 21:59:31 e8ef249f-ee8b-40c6-9863-5bbb31b27bc6
1986 2026-05-09 21:59:31 306e4c5e-d229-434d-8ba9-68c218e730a6
1987 2026-05-09 21:59:31 b23d248c-fa89-4794-8dba-e565e9b45e77
1988 2026-05-09 21:59:31 63927a1f-1e1b-4f3f-9ec7-114ceddb5e9f
1989 2026-05-09 21:59:31 402a0d33-fa30-4427-9116-f8a3289a925c
1990 2026-05-09 21:59:31 f1bb3a9e-50f3-4c39-a3f0-be8c02c61ab3
1991 2026-05-09 21:59:31 cf072ab0-b260-4a7f-9525-7b9b2011705e
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1993 2026-05-09 21:59:31 3709180a-2082-4f88-a4fd-b320a746cb69
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1995 2026-05-09 21:59:31 64c14c8b-feca-4338-b87e-ffd2e155b8c9
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1998 2026-05-09 21:59:31 f35cdf4d-8739-41ef-bc8c-28dcd1cd4983
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2002 2026-05-09 21:59:31 19e0b014-c590-4a32-b005-3e14b33f1b6b
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2008 2026-05-09 21:59:31 582a9e12-99ed-4ed7-a3c7-5e7fce37c2d9
2009 2026-05-09 21:59:31 8e6ff439-9419-4dc5-ac37-07d065a3d42c
2010 2026-05-09 21:59:31 82ee70aa-2e0a-485d-85af-0b1aaacf4d45
2011 2026-05-09 21:59:31 e6a3f8ae-ec13-4677-8429-43f25f2bfad1
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2093 2026-05-09 21:59:31 3f9a7292-7d1c-45f7-b115-a13cd2ad8080
2094 2026-05-09 21:59:31 a15627b2-448c-403b-8417-093574188f9f
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6205 2026-05-09 21:59:33 c9e822c9-9a73-4569-a6db-72df4a96c872
6206 2026-05-09 21:59:33 0b79e63e-1fa9-435b-b30c-6037adbe05f4
6207 2026-05-09 21:59:33 49874671-d787-43aa-979e-090e00924f27
6208 2026-05-09 21:59:33 36bad1a1-9967-49d9-9c14-e0cf8f7fdeec
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6219 2026-05-09 21:59:33 1b5f7631-b5e5-4a9f-bd5e-6ce4e0de9767
6220 2026-05-09 21:59:33 cb784d29-0e32-4c26-9714-cb973cc6f3b0
6221 2026-05-09 21:59:33 d025ae71-3568-452e-98fb-a623ff921e81
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6223 2026-05-09 21:59:33 47cbc95d-e17b-4721-ad43-b329b177efa3
6224 2026-05-09 21:59:33 87d7a853-ce04-430f-a5f3-db42772a38e8
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6227 2026-05-09 21:59:33 3e176ee5-21d1-424b-82a1-98b8446a01b3
6228 2026-05-09 21:59:33 ff5df7a1-0186-4750-b2c7-09d3c4d55b6b
6229 2026-05-09 21:59:33 093c16d5-fed3-4580-a596-678f11e0f584
6230 2026-05-09 21:59:33 6e165647-6419-4ea1-88ae-314fc6d8e7b7
6231 2026-05-09 21:59:33 f08729b4-6e8b-429d-b0b5-7e84b838a2e6
6232 2026-05-09 21:59:33 c4f4dbdf-3998-443b-95ed-b0ee0c72b8c8
6233 2026-05-09 21:59:33 c7633009-c0ca-4a65-b1bf-dbbe73545545
6234 2026-05-09 21:59:33 5297ca7f-8588-41dc-9936-bb273f84b6ee
6235 2026-05-09 21:59:33 9891ea58-62a1-4f72-a700-ff2a58d402b7
6236 2026-05-09 21:59:33 58987acd-0a3c-45e4-b7b5-e261abcd9d09
6237 2026-05-09 21:59:33 8e4d8529-e04c-4f61-9fa9-f8a20379e0d3
6238 2026-05-09 21:59:33 a3c6b743-d7e8-49d2-bde7-a106854f189b
6239 2026-05-09 21:59:33 84c55b9d-0764-4d3b-b746-e674d9301cda
6240 2026-05-09 21:59:33 50adb8b7-c7ab-41e4-987d-45fd4ea4d031
6241 2026-05-09 21:59:33 0dc3a515-6558-4f29-a55d-5b4242f1c93f
6242 2026-05-09 21:59:33 f495ef45-1926-447e-8f55-6f52003c78a8
6243 2026-05-09 21:59:33 43a92dc6-2da6-4410-8c41-7f5c64ad95f2
6244 2026-05-09 21:59:33 c58f9bdf-8478-46ed-9da5-1f292f7a0ffe
6245 2026-05-09 21:59:33 fa2c72e2-8e7e-47a6-a31c-0d913104f47d
6246 2026-05-09 21:59:33 457bb176-0a57-4d32-92d7-3bc6c581b1f6
6247 2026-05-09 21:59:33 d23a4632-cd60-420a-a53d-232307ce9004
6248 2026-05-09 21:59:33 792e077a-5a72-4f6c-9135-8dfc9eccefdc
6249 2026-05-09 21:59:33 fdd36c1c-7325-46fd-9994-c961b60a294c
[ reached 'max' / getOption("max.print") -- omitted 256375 rows ]
<- function (term_code) {<- substr (term_code, 5 , 6 )<- case_when (%in% c ("01" , "1" ) ~ "Spring" ,%in% c ("06" , "05" ) ~ "Summer" ,%in% c ("08" , "8" ) ~ "Fall" return (term)
<- function (months_elapsed) {<- case_when (< 173 ~ "Pre-COVID" ,>= 173 & months_elapsed < 209 ~ "COVID" ,>= 209 ~ "Post-COVID" return (period)
<- df |> # Drop rows where gpa_avg is missing filter (! is.na (gpa_avg)) |> # Use rowSums and across() to sum columns row-by-row mutate (course_weight = rowSums (across (grade_a: grade_f), na.rm = TRUE )) |> # Filter out rows with zero weight filter (course_weight > 0 ) |> # Group by semester group_by (term_code) |> # Divide the total grade points by the total grades to get the final GPA summarise (weighted_gpa = sum (gpa_avg * course_weight) / sum (course_weight),total_grade_term = sum (course_weight),.groups = "drop" |> mutate (months_elapsed = calculate_months_elapsed (term_code)) |> mutate (term = calculate_term (term_code)) |> mutate (period = calculate_period (months_elapsed)) |> mutate (period = factor (period, levels = c ("Pre-COVID" , "COVID" , "Post-COVID" )))
# A tibble: 62 × 6
term_code weighted_gpa total_grade_term months_elapsed term period
<chr> <dbl> <dbl> <dbl> <chr> <fct>
1 200508 3.04 81822 0 Fall Pre-COVID
2 200601 3.05 77597 5 Spring Pre-COVID
3 200605 3.21 26570 10 Summer Pre-COVID
4 200608 3.03 82480 12 Fall Pre-COVID
5 200701 3.02 79165 17 Spring Pre-COVID
6 200705 3.22 26726 22 Summer Pre-COVID
7 200708 3.05 85785 24 Fall Pre-COVID
8 200801 3.03 82858 29 Spring Pre-COVID
9 200805 3.27 28003 34 Summer Pre-COVID
10 200808 3.05 93380 36 Fall Pre-COVID
# ℹ 52 more rows
<- unique (weighted_gpa_df$ term_code)[seq (1 , length (unique (weighted_gpa_df$ term_code)), by = 10 )]# 2. Generate the plot ggplot (data = weighted_gpa_df, aes (x = term_code, y = weighted_gpa, group = 1 )) + # A. Add a soft background shading for the COVID-19 Era disruption # It dynamically reads your timeline limits using your month index thresholds annotate ("rect" , xmin = "202001" , xmax = "202105" , ymin = 0 , ymax = 4.3 , fill = "red" , alpha = 0.07 ) + # B. Draw the base connecting timeline line geom_line (color = "grey65" , linewidth = 0.8 ) + # C. Color and shape points by semester type to instantly reveal the Summer effect geom_point (aes (color = term, shape = term), size = 2.5 ) + # D. Add a text callout for the shaded pandemic block annotate ("text" , x = "202005" , y = 4.1 , label = "COVID-19 Era" , color = "red4" , fontface = "italic" , size = 3.5 , hjust = 0.5 ) + # E. Manage layout, enforce standard 0-4.3 limits, and trim margins scale_x_discrete (breaks = axis_breaks) + scale_y_continuous (limits = c (0 , 4.3 ), expand = c (0 , 0 )) + # F. Custom color palette for clean visual presentation scale_color_manual (values = c ("Fall" = "#2c3e50" , "Spring" = "#2980b9" , "Summer" = "#e67e22" )) + scale_shape_manual (values = c ("Fall" = 16 , "Spring" = 16 , "Summer" = 17 )) + # G. Professional theme polish theme_minimal (base_size = 12 ) + theme (panel.grid.minor = element_blank (),plot.title = element_text (face = "bold" , size = 14 , color = "#1a1a1a" ),plot.subtitle = element_text (size = 10 , color = "grey40" ),legend.position = "bottom" + labs (title = "Historical Analysis of Weighted Institutional GPA" ,subtitle = "Exposing cyclical summer semester spikes and pandemic-era structural shifts" ,x = "Academic Semester (Term Code)" ,y = "Weighted Average GPA (0.0 - 4.3 Scale)" ,color = "Semester:" ,shape = "Semester:"
= lm (weighted_gpa ~ months_elapsed * period + term, data = weighted_gpa_df)summary (gpa_model)
Call:
lm(formula = weighted_gpa ~ months_elapsed * period + term, data = weighted_gpa_df)
Residuals:
Min 1Q Median 3Q Max
-0.099083 -0.050478 0.002367 0.044618 0.127922
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.0970512 0.0217960 142.093 < 2e-16 ***
months_elapsed -0.0004729 0.0001925 -2.457 0.0172 *
periodCOVID 0.9448962 0.3890253 2.429 0.0185 *
periodPost-COVID -0.9763831 0.3951309 -2.471 0.0167 *
termSpring -0.0012554 0.0194688 -0.064 0.9488
termSummer 0.1927499 0.0195933 9.838 1.22e-13 ***
months_elapsed:periodCOVID -0.0051027 0.0020616 -2.475 0.0165 *
months_elapsed:periodPost-COVID 0.0045876 0.0017472 2.626 0.0112 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06263 on 54 degrees of freedom
Multiple R-squared: 0.7378, Adjusted R-squared: 0.7038
F-statistic: 21.71 on 7 and 54 DF, p-value: 1.292e-13
Interpretations :
Prior to the pandemic, institutional GPA was on a slow but statistically significant downward trajectory, decreasing by approximately \(0.028\) points per year (\(p = 0.0172\) ).
The pandemic created a significant structural break. While the active COVID period saw a sharp acceleration of decreasing GPAs (\(p = 0.0165\) ), the Post-COVID era experienced a highly significant, positive slope reversal (\(p = 0.0112\) ). Accounting for baseline trends, Post-COVID institutional GPAs are now increasing at a rate of roughly \(0.05\) points per year
Independent of historical era, summer semesters display a massive, structurally consistent increase of \(0.193\) GPA points (\(p < 0.001\) ) relative to Fall semesters
Conclusions:
Grade Inflation is Top-Heavy : The post-pandemic environment triggered a structural increase in academic excellence. The university transitioned from a flat historical baseline of ‘A’ grades and a slowly declining overall GPA to a lasting, aggressive acceleration in top marks post-2021.
Failure Rates Were Unfazed : While the proportion of A grades increased, failure and withdrawal rates (DWF) were completely un-deflected by the pandemic. They simply continued along the same slow, upward historical climb that predated COVID-19.
The “Summer Mirage” is Real : Across every single metric, summer semesters act as an independent grade-inflating anomaly. By mathematically isolating this effect, the models were able to reveal the true, underlying historical trends that would have otherwise been masked by seasonal noise. ## Closing database connection
