The data in the table are used to create a calibration curve for the determination of RNA from | RNA | Absorbance at 260
its absorbance at 260 nm. (ue) am
Use a spreadsheet and the least-squares method to determine the slope and y-inercept of the [000 | om |
best straight line fit to the calibration curve. Do not subtract the blank reading when creating
ly
3267 1.032
as 2 2°22 aaa
Using th slope and intercept of th calibration cure, determine the quanity of RNA in a sample that gives an absbance of
0.620.
aa
Question:
The data in the table are used to create a calibration curve for the determination of RNA from | RNA | Absorbance at 260
its absorbance at 260 nm. (ue) am
Use a spreadsheet and the least-squares method to determine the slope and y-inercept of the [000 | om |
best straight line fit to the calibration curve. Do not subtract the blank reading when creating
ly
3267 1.032
as 2 2°22 aaa
Using th slope and intercept of th calibration cure, determine the quanity of RNA in a sample that gives an absbance of
0.620.
aa
This is AI generated Response.
Asked by: Nikita Gaikwad
Created at: 2025-05-28 16:25:40
Final Answer:
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Let's solve the problem step by step:
## **Step 1: Arrange the Data**
Let \( x \) = RNA (μg)
Let \( y \) = Absorbance at 260 nm
| \( x \) (μg) | \( y \) (Abs) |
|--------------|--------------|
| 0.00 | 0.160 |
| 10.89 | 0.472 |
| 21.78 | 0.714 |
| 32.67 | 1.032 |
| 43.56 | 1.258 |
---
## **Step 2: Least Squares Method Formula**
The equation of the best fit line is:
\[ y = mx + b \]
where \( m \) is the slope and \( b \) is the intercept.
### **Formulas:**
\[
m = \frac{N\sum(xy) - \sum x \sum y}{N\sum(x^2) - (\sum x)^2}
\]
\[
b = \frac{\sum y - m\sum x}{N}
\]
Where \( N \) is the number of data points (here, \( N = 5 \)).
---
## **Step 3: Calculate the Sums**
| \( x \) | \( y \) | \( x^2 \) | \( y^2 \) | \( x y \) |
|-----------|-----------|---------------|---------------|----------------|
| 0.00 | 0.160 | 0.000 | 0.0256 | 0.000 |
| 10.89 | 0.472 | 118.6081 | 0.222784 | 5.13768 |
| 21.78 | 0.714 | 474.7684 | 0.509796 | 15.54372 |
| 32.67 | 1.032 | 1067.3289 | 1.065024 | 33.74944 |
| 43.56 | 1.258 | 1897.0836 | 1.582564 | 54.78248 |
\[
\sum x = 0 + 10.89 + 21.78 + 32.67 + 43.56 = 108.90
\]
\[
\sum y = 0.160 + 0.472 + 0.714 + 1.032 + 1.258 = 3.636
\]
\[
\sum x^2 = 0 + 118.6081 + 474.7684 + 1067.3289 + 1897.0836 = 3557.789
\]
\[
\sum xy = 0 + 5.13768 + 15.54372 + 33.74944 + 54.78248 = 109.2133
\]
---
## **Step 4: Plug into the Formulas**
\[
m = \frac{5 \times 109.2133 - 108.90 \times 3.636}{5 \times 3557.789 - (108.90)^2}
\]
Calculate numerator:
\[
5 \times 109.2133 = 546.0665
\]
\[
108.90 \times 3.636 = 395.7384
\]
\[
\text{Num} = 546.0665 - 395.7384 = 150.3281
\]
Calculate denominator:
\[
5 \times 3557.789 = 17788.945
\]
\[
(108.90)^2 = 11862.81
\]
\[
\text{Den} = 17788.945 - 11862.81 = 5926.135
\]
So,
\[
m = \frac{150.3281}{5926.135} \approx 0.0254 \ \mu g^{-1}
\]
\[
b = \frac{3.636 - (0.0254 \times 108.90)}{5}
\]
\[
0.0254 \times 108.90 = 2.764
\]
\[
3.636 - 2.764 = 0.872
\]
\[
b = \frac{0.872}{5} = 0.174
\]
---
## **Step 5: Find Mass for Absorbance 0.620**
The equation is \( y = mx + b \).
Given \( y = 0.620 \):
\[
0.620 = 0.0254x + 0.174
\]
\[
0.620 - 0.174 = 0.0254x
\]
\[
0.446 = 0.0254x
\]
\[
x = \frac{0.446}{0.0254} \approx 17.56\ \mu g
\]
---
## **Final Answers**
\[
\boxed{m = 0.0254\ \mu g^{-1}}
\]
\[
\boxed{b = 0.174}
\]
\[
\text{mass} = \boxed{17.56\ \mu g}
\]
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