1. What is the socio- demographic profile of the respondents in terms of:

1.1 Gender

c<-Comparative%>%
  group_by(Sex)%>%
  summarise(count=n())%>%
  mutate(Percentage =round((count/sum(count)*100),2))
paged_table(c)
pie3D(x, labels = piepercent,
    main = "Male and Female Respondents", col = rainbow(length(x)))
legend("topright", c("Male", "Female"),
                    cex = 0.5, fill = rainbow(length(x)))

2. What is the level of learning strategies between male and female students in terms of:

2.1 Visual 2.2 Auditory 2.3 Tactile

j<-f%>%
  group_by(Variable) %>%
  get_summary_stats(Score, type = "mean_sd")
paged_table(j)
boxplot(Visual ~ Sex, data = Comparative, main = "Visual Learning by Gender")

boxplot(Auditory ~ Sex, data = Comparative, main = "Auditory Learning by Gender")

boxplot(Tactile ~ Sex, data = Comparative, main = "Tactile Learning by Gender")

print(ttest_visual)

    Welch Two Sample t-test

data:  Visual by Sex
t = 1.381, df = 108.1, p-value = 0.1701
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
 -0.4436711  2.4819828
sample estimates:
mean in group Female   mean in group Male 
            19.07273             18.05357 
print(ttest_auditory)

    Welch Two Sample t-test

data:  Auditory by Sex
t = -0.54464, df = 108.59, p-value = 0.5871
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
 -2.485301  1.413872
sample estimates:
mean in group Female   mean in group Male 
            25.00000             25.53571 
print(ttest_tactile)

    Welch Two Sample t-test

data:  Tactile by Sex
t = -0.15927, df = 107.34, p-value = 0.8738
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
 -2.156659  1.835880
sample estimates:
mean in group Female   mean in group Male 
            26.01818             26.17857

3. Evaluate the academic performance between male and female in terms of:

3.1. Excellent 1.0 - 1.5 3.2. Good 1.5 - 2.0 3.3. Satisfactory 2.0 - 2.25

j<-Data%>%
  group_by(Comparative) %>%
  get_summary_stats(Score, type = "mean_sd")
paged_table(j)
print(performance_counts)
        
         Excellent Good Satisfactory Unspecified
  Female         6   43            5           1
  Male           2   47            7           0

4. Is there a significant relationship of learning strategies and academic performance between male and females?

Null Hypothesis (H0):

There is no significant relationship between learning strategies (Visual, Auditory, Tactile) and academic performance (GWA) among both males and females.

Alternative Hypothesis (H1):

There is a significant relationship between at least one learning strategy (Visual, Auditory, Tactile) and academic performance (GWA) among males and/or females.

linear_model_male <- lm(GWA ~ Visual + Auditory + Tactile, data = data_male)
summary(linear_model_male)

Call:
lm(formula = GWA ~ Visual + Auditory + Tactile, data = data_male)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.65296 -0.10283  0.00451  0.11059  0.39288 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.0360810  0.1546994  13.162   <2e-16 ***
Visual       0.0009637  0.0074461   0.129    0.898    
Auditory    -0.0003674  0.0062891  -0.058    0.954    
Tactile     -0.0098588  0.0059690  -1.652    0.105    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1937 on 52 degrees of freedom
Multiple R-squared:  0.07887,   Adjusted R-squared:  0.02572 
F-statistic: 1.484 on 3 and 52 DF,  p-value: 0.2297

Analysis for Males:

Visual, Auditory, Tactile:
    None of the learning strategies (Visual, Auditory, Tactile) show a significant relationship with academic performance for males.
    All p-values are above the typical significance threshold of 0.05.
linear_model_female <- lm(GWA ~ Visual + Auditory + Tactile, data = data_female)
summary(linear_model_female)

Call:
lm(formula = GWA ~ Visual + Auditory + Tactile, data = data_female)

Residuals:
   Min     1Q Median     3Q    Max 
-7.369 -3.175 -1.514  0.025 85.747 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)   9.8554    11.2527   0.876    0.385
Visual        0.6730     0.6276   1.072    0.289
Auditory     -0.1654     0.4188  -0.395    0.695
Tactile      -0.5800     0.3706  -1.565    0.124

Residual standard error: 12.45 on 51 degrees of freedom
Multiple R-squared:  0.05374,   Adjusted R-squared:  -0.001921 
F-statistic: 0.9655 on 3 and 51 DF,  p-value: 0.4162

Analysis for Females:

Visual, Auditory, Tactile:
    Similar to males, for females, none of the learning strategies (Visual, Auditory, Tactile) display a significant relationship with academic performance.
    Again, all p-values are above 0.05.

Interpretation:

In both male and female groups, the learning strategies (Visual, Auditory, Tactile) do not seem to have a statistically significant effect on academic performance, based on these regression models.
The coefficients for these learning strategies are not significantly different from zero for either gender, indicating that these variables don't predict academic performance in this analysis.

It’s important to note that these results suggest no significant relationship within the parameters of this analysis.

5. Is there a significance difference in the learning strategies between male and female on their academic performance?

model <- lm(GWA ~ Sex * Visual + Sex * Auditory + Sex * Tactile, data = Comparative)
summary(model)

Call:
lm(formula = GWA ~ Sex * Visual + Sex * Auditory + Sex * Tactile, 
    data = Comparative)

Residuals:
   Min     1Q Median     3Q    Max 
-7.369 -1.486 -0.073  0.100 85.747 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)  
(Intercept)        9.8554     7.9191   1.245   0.2161  
SexMale           -7.8193    10.5665  -0.740   0.4610  
Visual             0.6730     0.4417   1.524   0.1306  
Auditory          -0.1654     0.2947  -0.561   0.5759  
Tactile           -0.5800     0.2608  -2.224   0.0283 *
SexMale:Visual    -0.6721     0.5554  -1.210   0.2290  
SexMale:Auditory   0.1650     0.4096   0.403   0.6878  
SexMale:Tactile    0.5702     0.3753   1.519   0.1318  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 8.76 on 103 degrees of freedom
Multiple R-squared:  0.06253,   Adjusted R-squared:  -0.00118 
F-statistic: 0.9815 on 7 and 103 DF,  p-value: 0.4489

Interpretation of Coefficients:

Main Effects: Visual, Auditory, and the ‘SexMale’ coefficient (which represents the effect of being male as opposed to female) are not statistically significant. Tactile has a statistically significant coefficient (-0.5800) at a significance level of 0.05.

Interaction Effects: Interaction terms like ‘SexMale:Visual’, ‘SexMale:Auditory’, and ‘SexMale:Tactile’ don’t show statistically significant coefficients (p-values are above 0.05).

Interpretation of Significance:

The overall model doesn't appear to be significant (p-value: 0.4489).
The adjusted R-squared is negative, suggesting the model doesn't explain much variance in the data.

Conclusion:

Based on this analysis, the interaction terms (which measure the difference in the effect of learning strategies between males and females) do not appear to be statistically significant. This suggests that, according to this model, there isn’t strong evidence for a significant difference in the impact of learning strategies on academic performance between males and females.