LIS 4273 Course

Hypothesis  - Null Hypothesis (H₀): There is no significant difference in math scores among the three program types. - Alternative Hypothesis (H₁): According to the hypothesis, the Academic program type impacts students' academic achievement, especially their math scores. Compared to general or vocational programs, which concentrate on general education or career-specific skills, educational programs offer more demanding courses and resources. It is predicted that kids in academic programs will score higher on math tests due to this distinction. Research Implications : -Confirming this hypothesis could inform educational policy, guide resource allocation to different program types, or adapt  curricula to improve student outcomes. Related to Classwork: -  One-way ANOVA is based on the assumption of normally distributed data and equal variances across groups. The analysis verified these assumptions to ensure the test results' validity .  - Post-hoc tests, such as ...

> # Load the forecast package > library(forecast) Registered S3 method overwritten by 'quantmod': method from as.zoo.data.frame zoo Warning message: package ‘forecast’ was built under R version 4.4.2 > # Define the data > months <- c("Jan", "Feb", "March", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec") > data_2012 <- c(31.9, 27, 31.3, 31, 39.4, 40.7, 42.3, 49.5, 45, 50, 50.9, 58.5) > data_2013 <- c(39.4, 36.2, 40.5, 44.6, 46.8, 44.7, 52.2, 54, 48.8, 55.8, 58.7, 63.4) > # Combine data and create time series objects > time_series_2012 <- ts(data_2012, start = c(2012, 1), frequency = 12) > time_series_2013 <- ts(data_2013, start = c(2013, 1), frequency = 12) > # Plot the time series data > plot(time_series_2012, type = "o", col = "blue", ylim = range(data_2012,...

#Part 1: Additive Model for the Ashina Dataset > library(ISwR) > data(ashina) > ashina$subject <- factor(1:16) > act <- data.frame(vas = ashina$vas.active, subject = ashina$subject, treat = 1, period = ashina$grp) > plac <- data.frame(vas = ashina$vas.plac, subject = ashina$subject, treat = 0, period = ashina$grp) > combined <- rbind(act, plac) > additive_model <- lm(vas ~ subject + treat + period, data = combined) > summary(additive_model) Call: lm(formula = vas ~ subject + treat + period, data = combined) Residuals: Min 1Q Median 3Q Max -48.94 -18.44 0.00 18.44 48.94 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|) (Intercept) -113.06 27.39 -4.128 0.000895 *** subject2 51.50 37.58 1.370 0.190721 subject3 121.50 37.58 3.233 0.005573 ** subject4 97.00 37.58 2.581 0.020867 * subject5 125.00 37.58 3.32...