setwd("C:/Users/USER/Dropbox/PC/Desktop/Regression Analysis/Reg Final")
library(readxl)
B<- read_excel("C:/Users/USER/Dropbox/PC/Desktop/Regression Analysis/Reg Final//Data.xlsx")
## New names:
## • `` -> `...1`
## • `` -> `...22`
## • `` -> `...23`
## • `DDP` -> `DDP...75`
## • `DDP` -> `DDP...82`
B
## # A tibble: 298 × 126
##     ...1 Gender `Year Level` Devices primarily used by …¹ Students’ ownership …²
##    <dbl> <chr>  <chr>        <chr>                        <chr>                 
##  1     1 Female 4th Year     Desktop/ Laptop and Mobile … Personal: used exclus…
##  2     2 Female 4th Year     Desktop/ Laptop and Mobile … Shared between member…
##  3     3 Female 2nd Year     Desktop/ Laptop and Mobile … Personal: used exclus…
##  4     4 Female 4th Year     Desktop/ Laptop and Mobile … Personal: used exclus…
##  5     5 Female 4th Year     Mobile phone                 Personal: used exclus…
##  6     6 Female 4th Year     Mobile phone                 Personal: used exclus…
##  7     7 Female 1st Year     Desktop or laptop            Personal: used exclus…
##  8     8 Female 3rd Year     Desktop/ Laptop and Mobile … Personal: used exclus…
##  9     9 Male   2nd Year     Desktop or laptop            Personal: used exclus…
## 10    10 Female 2nd Year     Desktop or laptop            Shared between member…
## # ℹ 288 more rows
## # ℹ abbreviated names:
## #   ¹​`Devices primarily used by students for their online classes.`,
## #   ²​`Students’ ownership of devices used for online classes.`
## # ℹ 121 more variables:
## #   `Students primary means of access to internet connectivity.` <chr>,
## #   `Internet connectivity` <lgl>, …

Objectives of the study

The purpose of this research is to examine the impact of the perceived effectiveness of the Flexible Learning System (FLS) towards the academic performance of Central Mindanao University Students during COVID-19. Specifically, the paper aims to: 1. Determine the group of respondents in terms of:

  1. Gadgets Used
B$Device <- B$`Devices primarily used by students for their online classes.`
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
B%>%
  group_by(Device)%>%
  summarise(count=n())%>%
  mutate(Percentage =round((count/sum(count)*100),2))
## # A tibble: 6 × 3
##   Device                                   count Percentage
##   <chr>                                    <int>      <dbl>
## 1 Desktop or laptop                           61      20.5 
## 2 Desktop/ Laptop and Mobile Phone           126      42.3 
## 3 Desktop/ Laptop, Mobile Phone and Tablet     3       1.01
## 4 Mobile Phone and Tablet                      1       0.34
## 5 Mobile phone                               105      35.2 
## 6 Tablet                                       2       0.67
  1. Internet Connectivity
B$Internet <- B$`Internet connectivity`
B%>%
  group_by(Internet)%>%
  summarise(count=n())%>%
  mutate(Percentage =round((count/sum(count)*100),2))
## # A tibble: 1 × 3
##   Internet count Percentage
##   <lgl>    <int>      <dbl>
## 1 NA         298        100
  1. Year level
B$yrlvl <- B$`Year Level`
B%>%
  group_by(yrlvl)%>%
  summarise(count=n())%>%
  mutate(Percentage =round((count/sum(count)*100),2))
## # A tibble: 4 × 3
##   yrlvl    count Percentage
##   <chr>    <int>      <dbl>
## 1 1st Year    96       32.2
## 2 2nd Year    97       32.6
## 3 3rd Year    61       20.5
## 4 4th Year    44       14.8

D. Gender

B%>%
  group_by(Gender)%>%
  summarise(count=n())%>%
  mutate(Percentage =round((count/sum(count)*100),2))
## # A tibble: 2 × 3
##   Gender count Percentage
##   <chr>  <int>      <dbl>
## 1 Female   224       75.2
## 2 Male      74       24.8
  1. Determine the perceived effectiveness of FLS in terms of:
  1. Learners’ Dimension
  2. Attitude of the learner towards FLS (Provide the mean score of columns J to O)
table(B$`Attitude of the learner towards FLS [2. I feel that it is desirable to use the FLS.]`)
## 
##   1   2   3   4   5 
##  25  82 117  59  15
J <- (B$`Attitude of the learner towards FLS [1. I feel that using the flexible learning system (FLS) is a good idea.]`)

K <- (B$`Attitude of the learner towards FLS [2. I feel that it is desirable to use the FLS.]`)

L <- (B$`Attitude of the learner towards FLS [3. I feel that the FLS makes learning easier.]`)

M <- (B$`Attitude of the learner towards FLS [4. I have a generally favorable attitude toward the FLS.]`)

N <- (B$`Attitude of the learner towards FLS [5. I feel that I can learn actively in the FLS setup.]`)

O <-(B$LDAttitude)
mean(J)
## [1] 3.016779
mean(K)
## [1] 2.855705
mean(M)
## [1] 2.765101
mean(N)
## [1] 2.402685
mean(O)
## [1] 2.704698
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.2
BienD <- B
ggplot(BienD, aes(x = LDAttitude, y = GWA)) +
  geom_point() +
  labs(
    y = "General Weighted Average",
    x = "Attitude of the Learners"
  ) +
  theme_minimal()

model <- lm(GWA ~ LDAttitude, data = BienD)
model
## 
## Call:
## lm(formula = GWA ~ LDAttitude, data = BienD)
## 
## Coefficients:
## (Intercept)   LDAttitude  
##     1.66034     -0.02908
summary(model)
## 
## Call:
## lm(formula = GWA ~ LDAttitude, data = BienD)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.41418 -0.16639 -0.03046  0.12689  1.11874 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.66034    0.04386  37.856   <2e-16 ***
## LDAttitude  -0.02908    0.01548  -1.878   0.0614 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2249 on 296 degrees of freedom
## Multiple R-squared:  0.01177,    Adjusted R-squared:  0.008435 
## F-statistic: 3.527 on 1 and 296 DF,  p-value: 0.06137

The intercept 1.66034 shows that we can anticipate, on average, a General Weighted Average of 1.66034 for a hypothetical Learner Attitude of 0. The general weighted average and learners’ attitudes are negatively correlated, as seen by the slope of -0.02908. In this instance, the negative coefficient (-0.02908) indicates that the GWA tends to drop by about 0.02908 units for every unit rise in the attitude toward FLS (LDAttitude).

At the traditional significance level of 0.05, the p-value of 0.0614 indicates that the coefficient for LDAttitude is not statistically significant. Consequently, the null hypothesis is not strongly supported by the data, suggesting that the learner’s attitude toward FLS (LDAttitude) may not have a statistically significant impact on the General Weighted Average (GWA).

library(performance)
## Warning: package 'performance' was built under R version 4.3.2
check_model(model)

  1. Mental Health (Provide the mean score of Columns P, Q, R, X, and Y)
P <- mean(B$`Mental Health [1. I often feel nervous, anxious, or on edge during the  flexible learning system (FLS).]`)
Q <- mean(B$`Mental Health [2. I often feel not being able to stop or control worrying during the FLS.]`)
R <- mean(B$`Mental Health [3. I often feel so restless that it’s hard to sit still during the FLS.]`)
X <- mean(B$`Mental Health [4. I often feel active and in good spirits during the FLS.]`)
Y <- mean(B$`Mental Health [4. I often feel active and in good spirits during the FLS.]`)
Z <- mean(B$`Mental Health [5. My daily life has been filled with things that interest me during the FLS.]`)
AA <- mean(B$LDMental)
P
## [1] 1.942953
Q
## [1] 1.979866
R
## [1] 2.020134
X
## [1] 2.828859
Y
## [1] 2.828859
Z
## [1] 2.805369
AA
## [1] 3.538255
BienD1 <- B
ggplot(BienD1, aes(x = LDMental, y = GWA)) +
  geom_point() +
  labs(
    y = "General Weighted Average",
    x = "Mental Health of the Learners"
  ) +
  theme_minimal()

model1 <- lm(GWA ~ LDMental, data = BienD1)
model1
## 
## Call:
## lm(formula = GWA ~ LDMental, data = BienD1)
## 
## Coefficients:
## (Intercept)     LDMental  
##     1.51719      0.01823
summary(model1)
## 
## Call:
## lm(formula = GWA ~ LDMental, data = BienD1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.39376 -0.16239 -0.03346  0.13131  1.16718 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.51719    0.07384  20.548   <2e-16 ***
## LDMental     0.01823    0.02054   0.888    0.375    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2259 on 296 degrees of freedom
## Multiple R-squared:  0.002655,   Adjusted R-squared:  -0.0007148 
## F-statistic: 0.7879 on 1 and 296 DF,  p-value: 0.3755
check_model(model1)

3. Determine the relationship between the perceived effectiveness of FLS towards the academic performance of CMU students. Dependent Variable: Column “DV” Independent Variables: Column “AM”, Column “BB”, Column “BQ”, and Column “CF”

model2 <- lm(GWA ~ B$LD + B$ID + B$CD + B$DD,
  data = B
)
summary(model1)
## 
## Call:
## lm(formula = GWA ~ LDMental, data = BienD1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.39376 -0.16239 -0.03346  0.13131  1.16718 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.51719    0.07384  20.548   <2e-16 ***
## LDMental     0.01823    0.02054   0.888    0.375    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2259 on 296 degrees of freedom
## Multiple R-squared:  0.002655,   Adjusted R-squared:  -0.0007148 
## F-statistic: 0.7879 on 1 and 296 DF,  p-value: 0.3755

3.1 Which of the independent variables significantly predicts the dependent variable? As a result, Column AM(LD), the independent variable, has the coefficient with the biggest absolute value that predicts Column DV, the dependent variable, considerably. This indicates that Column AM(LD) is the most significant independent variable in the regression model.

3.2 Provide the coefficient of the independent variables.

check_model(model2)

3.3 Can we conclude that the model is better than a model having the intercept alone? The model is superior to a model that simply includes the intercept, as seen by the findings above, since at least one coefficient, β, has a p-value of 0.5884, indicating that it is statistically different from 0. This indicates that none of the factors we chose contribute to the explanation of the dependent variable. To put it another way, this model is entirely useless and should be ignored; its best application is to take the dependent variable’s mean.