Sound & Light Level Analysis Report
MD MAHFUZUL HAQUE
2025-09-29
Introduction
At the beginning of this analysis, we recorded and compared both sound levels and light intensity across four different locations.
• For Location 1 (Lab 240), sound was measured under high and low frequency sub conditions (dBA and dBC), and light was measured under low and maximum light conditions.
• For Locations 2, 3, and 4, only dBA (sound) and lux (light) readings were recorded, each with three repetitions.
This report evaluates how frequency, environmental setting, and lighting conditions affect both sound levels and light intensity, using statistical tools (t-tests and ANOVA) supported by visualizations.
Location wise Sound & Noise Level analysis
We measured sound levels (dBA and dBC) at four locations. In Lab 240, measurements were taken under High Frequency and Low Frequency sub conditions, allowing for two-sample t-tests. For Locations 2, 3, and 4, only dBA values were recorded and compared using ANOVA.
Location 1: High vs Low Frequency
Data Input
data1 <- data.frame(
Location_ID = rep(1, 6),
Location_Name = rep("Location 1 (Lab 240)", 6),
Subcondition = c(rep("High frequency",3), rep("Low frequency",3)),
dBA = c(80.5, 91, 84.3, 48.3, 48.6, 49.5),
dBC = c(80.8, 91.6, 93.2, 72.4, 73, 73.3)
)
data1## Location_ID Location_Name Subcondition dBA dBC
## 1 1 Location 1 (Lab 240) High frequency 80.5 80.8
## 2 1 Location 1 (Lab 240) High frequency 91.0 91.6
## 3 1 Location 1 (Lab 240) High frequency 84.3 93.2
## 4 1 Location 1 (Lab 240) Low frequency 48.3 72.4
## 5 1 Location 1 (Lab 240) Low frequency 48.6 73.0
## 6 1 Location 1 (Lab 240) Low frequency 49.5 73.3High Frequency Condition
- Range of dBA: 80.5 – 91.0
- Range of dBC: 80.8 – 93.2
Visually, both dBA and dBC values are close in range under high frequency.
Statistical Test
high <- subset(data1, Subcondition == "High frequency")
t_high <- t.test(high$dBA, high$dBC, var.equal = FALSE)
t_high##
## Welch Two Sample t-test
##
## data: high$dBA and high$dBC
## t = -0.65882, df = 3.793, p-value = 0.5479
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -17.33475 10.80142
## sample estimates:
## mean of x mean of y
## 85.26667 88.53333Visualization
boxplot(high$dBA, high$dBC, names = c("dBA High","dBC High"),
main = "High Frequency Comparison", ylab="Sound Level (dB)", col=c( "lightblue", "red"))
Interpretation: Since p-value > 0.05, there is no statistically significant difference between dBA and dBC at high frequency. Both follow a similar trend.
Low Frequency Condition
- Range of dBA: 48.3 – 49.5
- Range of dBC: 72.4 – 73.3
Here, dBC values are consistently much higher than dBA.
Statistical Test
low <- subset(data1, Subcondition == "Low frequency")
t_low <- t.test(low$dBA, low$dBC, var.equal = FALSE)
t_low##
## Welch Two Sample t-test
##
## data: low$dBA and low$dBC
## t = -53.889, df = 3.6697, p-value = 1.868e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -25.387 -22.813
## sample estimates:
## mean of x mean of y
## 48.8 72.9Visualization
boxplot(low$dBA, low$dBC, names = c("dBA Low","dBC Low"),
main = "Low Frequency Comparison", ylab="Sound Level (dB)", col= c("lightblue", "red"))
Interpretation: Since p-value << 0.05, there is a highly significant difference between dBA and dBC at low frequency. dBC remains much higher compared to dBA.
Locations 2–4: ANOVA on dBA
We further extended the study to three additional areas where only dBA readings were taken. Each location had three repeated measurements.
Data Input
data2 <- data.frame(
Location_ID = c(2,2,2,3,3,3,4,4,4),
Location_Name = c(rep("Location 2 (IMSE Hallway)",3),
rep("Location 3 (Livermore center 1)",3),
rep("Location 4 (Livermore center 2)",3)),
dBA = c(40.8, 43.2, 41.0, 32.7, 33.1, 34.2, 35.5, 36.7, 37.2)
)
data2## Location_ID Location_Name dBA
## 1 2 Location 2 (IMSE Hallway) 40.8
## 2 2 Location 2 (IMSE Hallway) 43.2
## 3 2 Location 2 (IMSE Hallway) 41.0
## 4 3 Location 3 (Livermore center 1) 32.7
## 5 3 Location 3 (Livermore center 1) 33.1
## 6 3 Location 3 (Livermore center 1) 34.2
## 7 4 Location 4 (Livermore center 2) 35.5
## 8 4 Location 4 (Livermore center 2) 36.7
## 9 4 Location 4 (Livermore center 2) 37.2Location Ranges
aggregate(dBA ~ Location_Name, data = data2,
FUN = function(x) c(min=min(x), max=max(x), mean=mean(x)))## Location_Name dBA.min dBA.max dBA.mean
## 1 Location 2 (IMSE Hallway) 40.80000 43.20000 41.66667
## 2 Location 3 (Livermore center 1) 32.70000 34.20000 33.33333
## 3 Location 4 (Livermore center 2) 35.50000 37.20000 36.46667- Location 2 (IMSE Hallway): dBA ~ 40.8–43.2 (mean ≈
41.7)
- Location 3 (Livermore Center 1): dBA ~ 32.7–34.2
(mean ≈ 33.3)
- Location 4 (Livermore Center 2): dBA ~ 35.5–37.2 (mean ≈ 36.5)
ANOVA Test
anova_result <- aov(dBA ~ Location_Name, data=data2)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Location_Name 2 106.30 53.15 50.78 0.000174 ***
## Residuals 6 6.28 1.05
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Interpretation: Since p-value << 0.05, there is a statistically significant difference in mean dBA levels across the three locations.
Visualization
boxplot(dBA ~ Location_Name, data=data2,
main="Comparison of dBA across Locations 2, 3, and 4",
ylab="Sound Level (dBA)", col="lightblue")
- Hallway has the highest average sound level.
- Livermore Center 1 has the lowest noise.
- Livermore Center 2 falls in between.
Final Decision Points
Taking into account both the dBA vs dBC frequency analysis (Location 1) and the ANOVA results (Locations 2–4), the overall findings are:
- At high frequency (Lab 240): No significant
difference between dBA and dBC.
- At low frequency (Lab 240): dBC is significantly higher than dBA; frequency reduction impacts dBA more strongly. 3.With the reduction of frequency: dBA levels drop significantly more than dBC, indicating that dBA is more sensitive to frequency change.
- Across different locations (Hallway vs. Centers): dBA levels vary significantly; hallway is noisier than reading spaces.
- Quiet spaces (Centers) consistently maintain lower
dBA values, suitable for reading/study.
- General insight: Both frequency type (high vs low) and environmental location (hallway vs reading center) have strong influence on measured sound levels.
Location wise Lightness (Lux) value Analysis
We measured light intensity (lux) at four
locations.
In Lab 240, measurements were taken under Low
Light and Maximum Light conditions, allowing
for a two-sample t-test.
For Locations 2, 3, and 4, measurements were compared
with ANOVA.
Location 1 (Lab 240): Low vs Maximum Light
Observations
- Low Light (3 obs.): 445 – 446 (mean ≈ 445.3)
- Maximum Light (3 obs.): 554 – 560 (mean ≈ 557.3)
Statistical Test (Welch’s t-test)
df_lux <- data.frame(
Location_ID = c(rep(1,6), rep(2,3), rep(3,3), rep(4,3)),
Location_Name = c(rep("Location 1 (Lab 240)",6),
rep("Location 2 (IMSE Hallway)",3),
rep("Location 3 (Livermore center 1)",3),
rep("Location 4 (Livermore center 2)",3)),
Lux = c(445,446,445,560,554,558,1200,1183,1197,126,127,126,126,127,127),
Light_Condition = c(rep("Low Light condition",3),
rep("Maximum Light condition",3),
rep(NA,9))
)
lab240 <- subset(df_lux, Location_ID==1)
low_light <- lab240[lab240$Light_Condition=="Low Light condition","Lux"]
max_light <- lab240[lab240$Light_Condition=="Maximum Light condition","Lux"]
t_lab240 <- t.test(low_light, max_light, var.equal = FALSE)
t_lab240##
## Welch Two Sample t-test
##
## data: low_light and max_light
## t = -62.394, df = 2.1427, p-value = 0.0001568
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -119.251 -104.749
## sample estimates:
## mean of x mean of y
## 445.3333 557.3333Interpretation: The p-value < 0.001 indicates a highly significant difference between low and maximum light in Lab 240.
Visualization
boxplot(Lux ~ Light_Condition, data=lab240,
main="Lab 240: Lux under Low vs Maximum Light",
ylab="Lux Level", col=c("orange","skyblue"))
Locations 2, 3, and 4
Observations (Lux Ranges)
aggregate(Lux ~ Location_Name, data = subset(df_lux, Location_ID!=1),
FUN = function(x) c(min=min(x), max=max(x), mean=mean(x)))## Location_Name Lux.min Lux.max Lux.mean
## 1 Location 2 (IMSE Hallway) 1183.0000 1200.0000 1193.3333
## 2 Location 3 (Livermore center 1) 126.0000 127.0000 126.3333
## 3 Location 4 (Livermore center 2) 126.0000 127.0000 126.6667- Location 2 (IMSE Hallway): 1183 – 1200 (mean ≈
1193.3)
- Location 3 (Livermore Center 1): 126 – 127 (mean ≈
126.3)
- Location 4 (Livermore Center 2): 126 – 127 (mean ≈ 126.7)
Statistical Test (ANOVA)
anova_lux <- aov(Lux ~ Location_Name, data=subset(df_lux, Location_ID!=1))
summary(anova_lux)## Df Sum Sq Mean Sq F value Pr(>F)
## Location_Name 2 2276267 1138133 41137 3.88e-13 ***
## Residuals 6 166 28
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Interpretation:
The ANOVA shows p-value < 0.001, meaning there is a
significant difference among locations.
The hallway is much brighter compared to reading centers, which are
consistently around 126–127.
Visualization
boxplot(Lux ~ Location_Name, data=subset(df_lux, Location_ID!=1),
main="Lux Comparison Across Locations 2, 3, 4",
ylab="Lux Level", col="lightgreen")
Final Observation on Lux measurements:
· In Lab 240, lux increases significantly from
low light to maximum light.
· Across Locations 2, 3, and 4, the hallway
has very higher lux, while reading centers remain consistently dim
(~126–127).
· General Insight: Hallways designated tested are much brighter with higher lux value, while reading centers lightness are comparatively dim which is reflecting in their respective Lux value designed to maintain low and stable light levels, suitable for study environments.
Conclusion
- This study demonstrates that by combining t-tests (for paired conditions like dBA vs dBC or low vs maximum lux) and ANOVA (for multi-location comparisons), we can better understand environmental conditions in terms of both sound and light.
The results highlight that:
Sound levels are influenced by both frequency type and location, with dBA being more sensitive to frequency reduction and hallways being noisier than reading centers.
Light levels vary significantly depending on lighting conditions and location, with hallways being much brighter, while reading centers maintain stable and dimmer lux values, suitable for study.
Overall, both noise and lighting measurements confirm that environmental design choices (frequency control, lighting setup, and location type) play a crucial role in shaping the comfort and usability of spaces.