Data

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


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

Sex

Strand

Daily Allowance

The tables above provide the distributions of respondents in terms of sex, strand, and daily allowance. It can be seen that there are 38 females and 62 males; 31 of which are from ABM, 26 from GAS, 37 from HUMSS, and 6 from STEM. Moreover, 8 students have a daily allowance 30-50 pesos, 21 have a daily allowance of 51-80 pesos, and 71 have a daily allowance of 100-above pesos.

2. Is there a significant difference on the students’ financial stability (Budgeting, Saving Strategy, Cost of Necessities, and Spending Plan) when grouped according to:

2.1 Sex


Call:
lm(formula = `Cost of Necessities` ~ Budgeting + `Saving Strategy` + 
    `Spending Plan`, data = Data)

Coefficients:
      (Intercept)          Budgeting  `Saving Strategy`    `Spending Plan`  
           1.0727             0.2748             0.1378             0.2174

From this, we may deduce that the data fail to satisfy the two assumptions – Linearity and Homogeneity of Variance.

2.1.1 Sex and Budgeting

`summarise()` has grouped output by 'Sex'. You can override using the `.groups`
argument.

Attaching package: 'rstatix'
The following object is masked from 'package:stats':

    filter

The mean for male and female is 2.979 and 2.935, respectively.

The above graph shows the plotting of data by sex, which contains two sexes – male and female.

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Warning: The following aesthetics were dropped during statistical transformation: fill
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

It clearly shows that there is no significant difference between the financial stability of the students in terms of budgeting when grouped according to their sex.

Loading required package: carData

Attaching package: 'car'
The following object is masked from 'package:purrr':

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The following object is masked from 'package:dplyr':

    recode

The histogram resembles a bell curve as seen above, means that the residuals have a normal distribution. Moreover, the points in the QQ-plots roughly follow the straight line, with the majority of them falling within the confidence bands. This also indicates that residuals have a normal distribution.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.98027, p-value = 0.1396

The Shapiro-Wilk p-value = 0.1396 on the residuals is greater than the usual significance level of 0.05. Thus, we fail to reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  1   2e-04  0.989
      98

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

Two Sample T-test


    Welch Two Sample t-test

data:  a and b
t = 0.1472, df = 82.356, p-value = 0.8833
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.2145749  0.2488703
sample estimates:
mean of x mean of y 
 2.952632  2.935484

Since the p-value is larger than 0.05, we fail to reject the null hypothesis, that is, there is no significant difference between the impact of eatery inflation to financial stability of the students in terms of budgeting when grouped according to sex.

2.1.2 Sex and Saving Strategy

`summarise()` has grouped output by 'Sex'. You can override using the `.groups`
argument.

The mean for male and female is 2.816 and 2.761, respectively.

The above graph shows the plotting of data by sex, which contains two sexes – male and female.

Warning: The following aesthetics were dropped during statistical transformation: fill
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

It clearly shows that there is no significant difference between the financial stability of the student in terms of saving strategy when grouped according to their sex.

The histogram does not resemble a bell curve as seen above. However, the points in the QQ-plots roughly follow the straight line, with the majority of them falling within the confidence bands. This indicates that residuals have a normal distribution. However, since the curve does not follow that of a bell, we may proceed to normality test to see its distribution.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.97919, p-value = 0.1149

The Shapiro-Wilk p-value = 0.1149 on the residuals is greater than the usual significance level of 0.05. Thus, we fail to reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  1  1.2229 0.2715
      98

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

Two Sample T-test


    Welch Two Sample t-test

data:  c and d
t = 0.45044, df = 67.796, p-value = 0.6538
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.1869472  0.2959455
sample estimates:
mean of x mean of y 
 2.815789  2.761290

Since the p-value is larger than 0.05, we fail to reject the null hypothesis, that is, there is no significant difference between the impact of eatery inflation to financial stability of the students in terms of saving strategy when grouped according to sex.

2.1.3 Sex and Cost of Necessities

`summarise()` has grouped output by 'Sex'. You can override using the `.groups`
argument.

The mean for male and female is 2.984 and 2.890, respectively.

The above graph shows the plotting of data by sex, which contains two sexes – male and female.

Warning: The following aesthetics were dropped during statistical transformation: fill
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

It clearly shows that there is no significant difference between the financial stability of the students in terms of cost of necessities when grouped according to their sex.

The histogram does not resemble a bell curve as seen above. However, the points in the QQ-plots roughly follow the straight line, with the majority of them falling within the confidence bands. This indicates that residuals have a normal distribution. However, since the curve does not follow that of a bell, we may proceed to normality test to see its distribution.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.98262, p-value = 0.2116

The Shapiro-Wilk p-value = 0.2116 on the residuals is greater than the usual significance level of 0.05. Thus, we fail to reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  1  0.5393 0.4645
      98

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

Two Sample T-test


    Welch Two Sample t-test

data:  e and f
t = 0.88358, df = 72.228, p-value = 0.3799
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.1179221  0.3056980
sample estimates:
mean of x mean of y 
 2.984211  2.890323

Since the p-value is larger than 0.05, we fail to reject the null hypothesis, that is, there is no significant difference between the impact of eatery inflation to financial stability of the students in terms of cost of necessities when grouped according to sex.

2.1.4 Sex and Spending Plan

`summarise()` has grouped output by 'Sex'. You can override using the `.groups`
argument.

The mean for male and female is 2.963 and 3.071, respectively.

The above graph shows the plotting of data by sex, which contains two sexes – male and female.

Warning: The following aesthetics were dropped during statistical transformation: fill
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

It clearly shows that there is a difference between the financial stability of the students in terms of spending plan when grouped according to their sex. However, its significance still needs to be checked.

The histogram does not resemble a bell curve as seen above. However, the points in the QQ-plots roughly follow the straight line, with the majority of them falling within the confidence bands. This indicates that residuals have a normal distribution. However, since the curve does not follow the shape that of a bell, we may proceed to normality test to see its distribution.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.95651, p-value = 0.002284

The Shapiro-Wilk p-value = 0.002284 on the residuals is less than the usual significance level of 0.05. Thus, we reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  1  0.0848 0.7715
      98

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

Wilcoxon Rank Sum Test


    Wilcoxon rank sum test with continuity correction

data:  g and h
W = 979.5, p-value = 0.1576
alternative hypothesis: true location shift is not equal to 0

Since the p-value= 0.1576 is greater than 0.05, we fail to reject the null hypothesis. Hence, there is no significant difference between the financial stability of the students in terms of spending plan when grouped according to their sex.

2.2.1 Daily Allowance and Budgeting

`summarise()` has grouped output by 'Daily Allowance'. You can override using
the `.groups` argument.

The mean of 100-above, 30-50 and 51-80 is 2.913, 3.100, and 3.029, respectively.

The above graph shows the plotting of data by daily allowance of the students.

It clearly shows that there is a difference between the financial stability of the students in terms of budgeting when grouped according to their daily allowance. However, its significance still needs to be checked.

The histogram resembles a bell curve as seen above. Moreover, the points in the QQ-plots roughly follow the straight line, with the majority of them falling within the confidence bands. This indicates that residuals have a normal distribution.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.98064, p-value = 0.1491

The Shapiro-Wilk p-value = 0.1491 on the residuals is greater than the usual significance level of 0.05. Thus, we fail to reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.4033 0.6692
      97

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

One-way ANOVA

                  Df Sum Sq Mean Sq F value Pr(>F)
`Daily Allowance`  2   0.41  0.2041   0.614  0.543
Residuals         97  32.24  0.3324

Since p-value = 0.543 > 0.05, we fail to reject the null hypothesis, that is, the financial stability in terms of budgeting do not differ when grouped according to daily allowance.

2.2.2 Daily Allowance and Saving Strategy

`summarise()` has grouped output by 'Daily Allowance'. You can override using
the `.groups` argument.

The mean of 100-above, 30-50 and 51-80 is 2.727, 2.950, and 2.905, respectively.

The above graph shows the plotting of data by daily allowance of the students.

It clearly shows that there is a difference between the financial stability of the students in terms of budgeting when grouped according to their daily allowance. However, its significance still needs to be checked.

The histogram does not resemble a bell curve as seen above. Moreover, the points in the QQ-plots roughly follow the straight line, with few of them falling outside the confidence bands. This indicates that residuals do not have a normal distribution.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.97435, p-value = 0.04802

The Shapiro-Wilk p-value = 0.04802 on the residuals is less than the usual significance level of 0.05. Thus, we reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  1.2617 0.2878
      97

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

Kruskal-wallis Test

# A tibble: 1 × 6
  .y.                 n statistic    df     p method        
* <chr>           <int>     <dbl> <int> <dbl> <chr>         
1 Saving Strategy   100      2.43     2 0.297 Kruskal-Wallis

Based on the p-value, no significant difference was observed between the group pairs.

2.2.3 Daily Allowance and Cost of Necessities

`summarise()` has grouped output by 'Daily Allowance'. You can override using
the `.groups` argument.
# A tibble: 26 × 3
# Groups:   Daily Allowance [3]
   `Daily Allowance` `Cost of Necessities` count
   <fct>                             <dbl> <int>
 1 100 - Above                         1.8     1
 2 100 - Above                         2       6
 3 100 - Above                         2.2     1
 4 100 - Above                         2.4     8
 5 100 - Above                         2.6    11
 6 100 - Above                         2.8    11
 7 100 - Above                         3       9
 8 100 - Above                         3.2    11
 9 100 - Above                         3.4     6
10 100 - Above                         3.6     4
# ℹ 16 more rows

The mean for 100 - Above, 30-50, and 51-80 is 2.865, 3.125, and 3.057, respectively.

The histogram does not form a bell curve as seen above. Moreover, the points in the QQ-plots roughly follow the straight line, with the majority of them falling within the confidence bands. However, we are assured that the data follows a normal distribution when both diagrams have the same output.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.98028, p-value = 0.1397

The Shapiro-Wilk p-value = 0.1397 on the residuals is greater than the usual significance level of 0.05. Thus, we fail to reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.7505 0.4749
      97

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

One Way ANOVA

                  Df Sum Sq Mean Sq F value Pr(>F)
`Daily Allowance`  2  0.944  0.4720   1.902  0.155
Residuals         97 24.068  0.2481

Based on the p-value, no significant difference was observed between the group pairs.

2.2.4 Daily Allowance and Spending Plan

`summarise()` has grouped output by 'Daily Allowance'. You can override using
the `.groups` argument.
# A tibble: 25 × 3
# Groups:   Daily Allowance [3]
   `Daily Allowance` `Spending Plan` count
   <fct>                       <dbl> <int>
 1 100 - Above                   2       7
 2 100 - Above                   2.2     5
 3 100 - Above                   2.4    12
 4 100 - Above                   2.6     6
 5 100 - Above                   2.8     7
 6 100 - Above                   3       9
 7 100 - Above                   3.2     7
 8 100 - Above                   3.4     2
 9 100 - Above                   3.6     4
10 100 - Above                   3.8     4
# ℹ 15 more rows

The mean for 100 - above, 30-50, and 51-80 is 2.913, 3.300, and 3.324, respectively.

Warning: The following aesthetics were dropped during statistical transformation: fill
ℹ This can happen when ggplot fails to infer the correct grouping structure in
  the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
  variable into a factor?

It clearly shows that there is a difference between the social behavior of the student in terms of emotional aspect when grouped according to their strand. However, illustrations does only give overviews of the data. It does not guarantee the exact result.

The histogram does not resemble a bell curve as seen above, means that the residuals have a normal distribution. Moreover, the points in the QQ-plots roughly follow the straight line, with the majority of them falling within the confidence bands. However, we are assured that the data follows a normal distribution when both diagrams have the same output.

Normality Test


    Shapiro-Wilk normality test

data:  res_aov$residuals
W = 0.97069, p-value = 0.02499

The Shapiro-Wilk p-value = 0.02499 on the residuals is less than the usual significance level of 0.05. Thus, we reject the hypothesis that residuals have a normal distribution.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  1.6429 0.1988
      97

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.

Kruskal-wallis Test

# A tibble: 3 × 11
  `Daily Allowance` variable        n   min   max median   iqr  mean    sd    se
  <fct>             <fct>       <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>
1 100 - Above       Spending P…    71     2     4    2.8   0.9  2.91 0.63  0.075
2 30-50             Spending P…     8     2     4    3.4   0.7  3.3  0.65  0.23 
3 51-80             Spending P…    21     2     4    3.4   0.4  3.32 0.492 0.107
# ℹ 1 more variable: ci <dbl>

# A tibble: 1 × 6
  .y.               n statistic    df       p method        
* <chr>         <int>     <dbl> <int>   <dbl> <chr>         
1 Spending Plan   100      9.22     2 0.00995 Kruskal-Wallis

Based on the p-value, there is a significant difference was observed between the group pairs.

3. Is there a significant difference between budgeting, saving strategy, cost of necessities, and spending Plan in terms of the impact of eatery inflation?

Normality Test


    Shapiro-Wilk normality test

data:  Data1$`Score in terms of Impact of Eatery Inflation to Students' Financial Stability`
W = 0.97593, p-value = 3.404e-06

Since p-value = 3.404e-06 < 0.05, it is conclusive that we should reject the null hypothesis. That is, we cannot assume normality.

Equality of Variance

Warning in leveneTest.default(y = y, group = group, ...): group coerced to
factor.
Levene's Test for Homogeneity of Variance (center = median)
       Df F value  Pr(>F)  
group   3  2.4768 0.06098 .
      396                  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The p-value is greater than the 0.05 level of significance. Thus, the homogeneity assumption of the variance is met.


Attaching package: 'gplots'
The following object is masked from 'package:stats':

    lowess
Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
graphical parameter
Warning in axis(1, at = 1:length(means), labels = legends, ...): "frame" is not
a graphical parameter
Warning in plot.xy(xy.coords(x, y), type = type, ...): "frame" is not a
graphical parameter

# A tibble: 4 × 11
  Variables      variable     n   min   max median   iqr  mean    sd    se    ci
  <fct>          <fct>    <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Budgeting      Score i…   100   1.4     4    3    0.8   2.95 0.574 0.057 0.114
2 Saving Strate… Score i…   100   1.4     4    2.8  0.65  2.78 0.56  0.056 0.111
3 Cost of Neces… Score i…   100   1.8     4    3    0.6   2.93 0.503 0.05  0.1  
4 Spending Plan  Score i…   100   2       4    3    1.2   3.03 0.628 0.063 0.125

The mean of budgeting, saving strategy, cost of necessities, and spending plan is 2.952, 2.782, 2.926, and 3.030, respectively.

Kruskal-wallis Test

# A tibble: 1 × 6
  .y.                                            n statistic    df      p method
* <chr>                                      <int>     <dbl> <int>  <dbl> <chr> 
1 Score in terms of Impact of Eatery Inflat…   400      8.72     3 0.0332 Krusk…

Based on the p-value, there is a significant difference was observed between the group pairs.

Pairwise Comparisons

# A tibble: 6 × 9
  .y.            group1 group2    n1    n2 statistic       p  p.adj p.adj.signif
* <chr>          <chr>  <chr>  <int> <int>     <dbl>   <dbl>  <dbl> <chr>       
1 Score in term… Budge… Savin…   100   100    -2.10  0.0361  0.216  ns          
2 Score in term… Budge… Cost …   100   100    -0.397 0.691   1      ns          
3 Score in term… Budge… Spend…   100   100     0.753 0.452   1      ns          
4 Score in term… Savin… Cost …   100   100     1.70  0.0893  0.536  ns          
5 Score in term… Savin… Spend…   100   100     2.85  0.00439 0.0263 *           
6 Score in term… Cost … Spend…   100   100     1.15  0.250   1      ns

There is a significant difference between budgeting and saving strategy in terms of score in terms of impact of eatery inflation to students’ financial stability. Similarly, there is a significant difference between saving strategy and spending plan.

4. 4. Which among the four: Budgeting, Saving Strategy, Cost of Necessities, and Spending Plan, does eatery have the most significant impact?

# A tibble: 4 × 11
  Variables      variable     n   min   max median   iqr  mean    sd    se    ci
  <fct>          <fct>    <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Budgeting      Score i…   100   1.4     4    3    0.8   2.95 0.574 0.057 0.114
2 Saving Strate… Score i…   100   1.4     4    2.8  0.65  2.78 0.56  0.056 0.111
3 Cost of Neces… Score i…   100   1.8     4    3    0.6   2.93 0.503 0.05  0.1  
4 Spending Plan  Score i…   100   2       4    3    1.2   3.03 0.628 0.063 0.125

Based on these results, it is the spending plan that is most affected by the eatery inflation.